Did Fable 5 beat the field?

> It's possible Opus or GPT-5.5 could have done this too, I've not tried the exact same sequence. The Fable vibes are good here, though.

And that's the thing. These comparisons are all gut feelings. I'm missing objective unbiased measurements to actually have real comparisons between different models, their different generations, or even just the convention that everybody adds "you are an expert software engineer" and "don't make mistakes" to their prompts because they think it improves anything. Nobody knows if it

actually

does.

It is possible to check for improvements. See for yourself:

https://generative-ai.review/2026/06/claude-fable-rush-test-...

As mentioned in another HN thread I've done a qualitative side-by-side measurements of Claude Fable vs Opus 4.8 vs ChatGPT 5.5.

Anyone is able to check the output for themselves and form a judgement.

Large visible improvements for Fable over Opus 4.8 and ChatGPT 5.5.

I recently did the same to show the progress from Opus 3.4/ChatGPT o3pro one calendar year ago.

Sorry, this post gets me irrationally irritated and makes me want to shake you and shout.

That website is 95% not you, it's AI, and I feel that's causing you to way over-represent the value of it in your response here, or you're completely misunderstanding what the person you're responding to is asking. If you put all of your effort into that site, without AI, it would be infinitely more valuable and useful.

The person you responded to asked for specific things, including:

- obvjective, unbiased measurements, but all that page has is side by side visual comparison of outputs.

- their different generations, but all you included was the outputs

- details on the prompts and little things people are adding because they feel they need to, but you didn't include any of that

This is slop, it's the exact sort of self confirming fluffy AI stuff that other either inexperience or over-invested-in-AI engineers will look at briefly, skim, see quick visual validation, and nod, noting down how much better Fable must be without getting any actual data.

Sorry, it's early, and maybe this is a misplaced rant, but the person you responded to specifically asked for precise, quantitative things precisely because everything else is fluffy slop like this, and people don't even recognise they're doing it any more.

check the links[1][2] before you start throwing around accusations. I am not (yet) a person that has advanced notice and access to models.

Fable just got announced and I did a rush out article because people are curious. I released the post mere hours afterwards and it takes time to create the output, slice into videos, make a wordpress article on top of taking my son to basketball training and eating dinner. I’m in London and this was all happening at 1am.

If you check the links my previous articles have all the juicy stuff you are criticising me for not having with little preparation.

How is a side by side direct comparison NOT precise?

[1] first in series from 2025:

https://generative-ai.review/2025/05/vibe-coding-my-way-to-e...

. This has all the background you are talking about in the Appendix

.

[2]

https://generative-ai.review/2026/05/vibe-coding-my-way-to-e...

. Second in series 2026 has a side by side table of what changed. This is what is possible with more than a few hours advanced warning.

I did browse and check the links. This was the first link I went to:

https://generative-ai.review/2026/05/vibe-coding-my-way-to-e...

as it's the main one on the page, and I saw more qualitative stuff without quantitative stuff.

I just read the extra link you provided which has some more information, thank you. Sorry, but the links confirm my points. You're not giving any quantitative analysis of your use of the different LLMs or your process. Your "sciencey appendix" is all about the domain science of pyramids, nothing to do with how or what you put into the LLMs, or any quantitative analysis of the code put out.

I'm sorry, your response has just proved the point that frustrated me: you've either lost or never had the capability to recognise a decent quantitative assessment of technical software creations.

Your entire site is obssessed and fixated on the impressive looking outputs of LLMs, rather than actual quantitative assessment of the quality of the outputs. This is the killer problem of AI: it looks like it's good, and a lot of the time, things that look good are good. It's very easy to make stuff on a computer that looks good but isn't for various reasons, and I nothing in what you've said here suggests that you fully grasp that. Sorry again to be harsh here, this is just my opinion, and we're probably going to have to agree to disagree.

It feels like hand written software will now be "bespoke"

Yes, these are gut feelings. That said, I have lots of experiences with Opus and I have lots of projects and contributions (all reviewed and tested) made with the help of it. Definitely useful, to me and to people whose project matters to them. :P

Adding "do not make mistakes" is silly, in my opinion. There is always a good chance it will make mistakes. You should rather be more specific about a thing rather than as broad as "do not make mistakes" is. It just does not work that way.

There are tons of benchmarks in the announcement. But we also know that benchmarks are problematic.

So the best we can do right now seems to be to combine imperfect case studies like this with imperfect benchmarks to get some unreliable impression of where we are...

That’s what evals are for.

And there’s no reason evals can’t be done on multi-turn agents in a loop (or not): it’s pretty much what all these benchmarks do.

fwiw, I gave it the same vibecoding project I'd previously tried with Sonnet 4.5 and it took Fable 2 hours to go well beyond (like, 2x beyond) where I got in 8 hours with Sonnet 4.5. (beyond that idk, because past 8 hours with the Sonnet 4.5 version I hit the "vibe limit" where it becomes easier to just write/edit the code yourself than get the agent to do what you want; and past 2 hours with Fable I hit my usage limit.)

How many $ do you guys spend when your session runs for 30min? What's the total budget?

Yes, exactly this. If I didn't care about price at all, I'd exclusively use this model. It functions more like an actual engineer. I'm in the midst of a DB migration, and eg 5.5 continually suggests stuff like "use DB X instead of DB Y for task Z because its 30% faster" which is an impossibility of reality, given we are migrating DBs. Fable jumped in, reduced allocs by literally 46x, found multiple bugs 4.8 and 5.5 created (max file system usage, correctness issues, etc), and continually suggested awesome improvements unprompted. As in, it would finish a task and then suggest we tackle this other existing problem I didn't know about in a very specific manner... this is the first model that feels like its coming for my job.

I'm having the same experience. I'm in the process of implementing a new CRDT for realtime collaborative editing. There just aren't a lot of implementations of CRDTs kicking around online for opus or any of the other models to have good design instincts.

Fable is doing - so far - a great job. I just had one big question around how part of it should work. I had a design sketch, but with some big unknowns. I asked fable to figure it out via reasoning and prototyping, and it did - it even, under its own initiative, wrote a fuzzer for its prototype which explored and verified that its reasoning was correct. It absolutely nailed it. And it found, and fixed, a couple bugs that I'd missed.

I'm sure its weaknesses will become apparent in time. But, wow this thing is a beast. Its the first time I'm reading the work of an LLM without spotting obvious weaknesses in its reasoning and code. I'm really impressed.

Hello joseph,

I saw scanning the comments and saw you mentioned CRDT. Just wanted to mention that I implemented a CRDT-flavoured sync engine for the product I'm working on a while ago, I think it was with Opus 4.6 if I'm not mistaken (or earlier) so it's not something new to Fable 5, just fyi.

I was about to ask where you work that you’re implementing new CRDTs and then I noticed your username! Thanks for all that you do!

I work on the live collab at my company, and using AI while coding has into recently sort of “clicked” for me. We use an (I’m pretty sure) unheard of algorithm for collaborative editing, and I’ve had a long term goal of turning it into an implementation of EG Walker, but our document model is very complex and most out of the box CRDTs don’t quite fit. Maybe Fable will be what gets me over the hump.

I’d be fascinated to hear more if you’re willing to share. What is special about your document model which makes existing tools like automerge a bad fit?

> wrote a fuzzer for its prototype which explored and verified that its reasoning was correct. It absolutely nailed it.

For such a data structure, "nailing it" means a formal proof of correctness. Fuzzing, as useful as it is, is merely throwing dirt at the wall and seeing if anything sticks.

I’ll ask it for a formal proof when I get home and see how it goes.

I’ve read plenty of papers with “formal proofs of correctness” that turned out to have huge flaws. Machine verifiable proofs I trust. But I’ve personally found more bugs with fuzzing than I have via proofs.

In the real world, many of us don't have the time to create formal proofs. But our instinct in testing where edge cases may exist in code that we wrote is a type of refactoring that happens in our brains during the coding process. Hand the coding off to a machine and you have no idea where to start looking for the flaws.

> this is the first model that feels like its coming for my job

Damn you must be good, I've been feeling this for around 2 years now

It still does make errors, yes?

Because it is not usable, if we need to verify everything.

AI is only interesting if it can do things that humans can not do.

If you can verify results because you can do it yourself, then why use AI?

It will just bind highly skilled people to do verification work.

Instead these people should do the actual work, results will come quicker.

So AI is only interesting to you / your org / humans if it can do things that you can not achieve.

But if it still does errors, how could we ever know that super-invention by AI is not wrong?

If we can not rely on the correctness of the result, it is not usable at all.

AI must create reliable and correct results always.

That was a very fundamental requirement for computing.

This problem has not been solved.

AI is like a junior developer. You have to review her code carefully but she is most definitely useful.

Why is your AI a she? What's up with gendering LLMs. Reminds me of Richard Dawkins calling Claude "Claudia" and insisting it to be conscious.

One thing I can tell you is you are either favored by Anthropic, or your version of the CLI does not exhaust limits, or there's some major bug, as two people around me (myself included) claim it took half an hour to hit the ceiling. Which makes it practically unusable, where the same workflow a day ago produced a good 5-6 hours of workload with several agents.

Monetization is coming. They'll tell companies, AI is replacing your workers, so it is still worth to pay 100K/year for the license, as those AI are not going to jump to other job, get sick, be late, complain, require free coffee and so on.

Soon the times of AI for $20/$200 a month will be long gone.

As someone noted here recently - use the frontier models as much as u can, while you can.

Yeah same here, Fable on "high" is producing substantially better results than Open 4.8 on xhigh for me and my actual real-world evals today. It "feels" smarter and doesn't use nearly as many tokens running in circles. As a result I've been able to run two large refactors today without hitting the context limit danger zones - it's more expensive but also more efficient. It's been able to find some bugs that Opus missed. Pretty impressive stuff.

I keep getting this message:

> Fable 5's safety measures flagged this message for cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Switched to Opus 4.8. Send feedback with /feedback or learn more

I'm working on an internal tool that does new business prospecting data collection, scoring, etc. This is ridiculous.

It’s unusable for me due to the refusals. I’m using claude to find patterns in health data

I do some work in laboratory automation and it was quick to refuse the first thing I asked it to do. There wasn't anything spicy in the request, just basic liquid-handling protocol implementation. Their position seems to be that they're too stupid to classify requests safely, and that seems reasonable to me. I'd guess the classifier will improve rapidly.

Have you tried locally running qwen?

Same. I'm working on a set of python and matlab scripts that deals with segmenting MRI images into brain vs skull, and it thinks that's bioterrorism.

Quite counterproductive to refuse to help on health issues too. If they detect health data, they can add a disclaimer, but not hide the information.

You miss the point - by collecting and processing medical data they would fall into a thoroughly regulated industry. Not because they may provide you incorrect data, because they are not allowed to process them.

There’s no way around it? Can’t you obfuscate as generic data and use keys to map to the real data?

What custom prompt do you have set up? If you tell it you're occupation, does it turn helpful? There was a study that if you tell models they tested that you're a patient, it would refuse, but tell it you're a doctor and suddenly it turns helpful.

According to the model, it’s not the model itself that’s doing this, it’s the harness.

Assuming the model is being “truthful”, CC is just being stupid in its detection mechanism.

what prompts do you use for this?

Anthropic knows it refuses too much, they want to be very cautious to avoid any scandals. I think this is why they want to store all Fable and Mythos chats for 30 days so they can use the data to improve.

They want to be very cautious to honour the important doctrine at least until IPO launches: we are so good we are nerf our products.

I’m a point where I expect everything I do will be retained indefinitely.

I’m having a really hard time believing some weak reason for a 30 day retention policy.

I wonder if it sees Healthcare companies being targeted and that's why it's freaking out; clearly they have some pretty stupid regexes in the harness to detect this sort of shit.

e: I quit the session and went back in. Set it to Fable and told it to continue the last session. It's moving along as if none of that had happened.

How weird.

I wonder if this letter has anything to do with why anything even remotely related to biology is getting flagged.

https://www.wired.com/story/openai-anthropic-letter-ai-biolo...

I asked a question for my son about how mosquitos carry malaria and Fable was like “ok now hold it right there”

Same here. It's been rushed for the IPO (in my opinion).

Or people were quitting their subscription for codex-5.5 and it was beginning to show up in their metrics.

Or development had gotten to a point where they need real world usage to tune product and refusals.

Or Fable’s arch is different enough the allocated clusters of compute targeting a date, and here we are, ready or not.

Or…

Same I am working on music firmware for existing device. I can't proceed as it keeps switching to Opus.

Obviously, soon, for anything valuable, you will have to buy from Anthropic "special license for biology/security/finance advises".

Question is if there will be any competition in this area...

Still does not crack my hardest nuts. Gave it one of them and it blew through my entire allowance on thinking about one question, with no apparent answer in sight!

I see a lot of people saying they are happy with weaker models, but I am the opposite, I need more strength, more intelligence!

I am quite happy that opus 4.8 can do some medium intelligence problems. And maybe Fable 5 can do some more more of those! I have a lot of problems to solve!

I also see a lot of people saying they are happy with weaker models.

At work I had to switch to using GPT 5.4 Mini and Qwen 3.6 27B.

The results were near useless.

The error rate is through the roof, it's constantly incorrect in its conclusions even when investigating very simple issues.

Further the models are too unreliable to even move 20 line snippets around without inadvertently modifying them. Ask them to correct it and they still get it wrong.

Maybe the larger Chinese models are better, but the Mini stuff is next to useless to me.

I have Qwen 3.6 27B and 35B running locally and and coming from Opus it feels like talking to an imposter. Someone who pretends to be competent, but really isn’t. Results are always disappointing. Sonnet is better, but I have given up on asking it. even for simple things I wait for my opus limits to reset.

What kind of problems are you trying to have it solve ?

The Riemann hypothesis, PvNP, and the Collatz conjecture.

Not these. I wonder if the well is poisoned there. The models know that these are "unpossible", so it might not solve them just because…

Maybe some day.

I am just testing it on stuff I know intimately myself. I would probably not understand a proof of Collatz if it was dansing in front of me!

So, what kind of problems are you having it try to solve?

Sorry to belabor this but it's basically pointless saying you have nuts it can't crack without showing us the nuts.

I don’t care to share my exact problems. Mostly because gpt -5.5 hallucinates false solutions, and I would rather not have people reply with "Oh but ChatGPT solves it!", because it takes expert knowledge to debunk them. To their credit ChatGPT will admit their, very fundamental mistakes when pointed out to them. But also because no-one would really care.

I gave a high level description of the problems in a sibling thread. They are the kind of small problems which I suppose every researcher has lying around, waiting for them to think about some day. But not the big problem everyone is waiting for to be solved.

My comment was not meant to be a tease – sorry! I assumed there would be other people in a similar situation, who might relate.

Bro, you are being left behind bro, it's amazing bro...

That's a bit of a tricky point. I have had quite a lot of problems with models informing me what I am attempting is impossible. If no-one has done it, or at least it doesn't know about it being done it tends to fall back on people voicing their baseless speculations, and for just about anything you propose, you can find a person who will loudly proclaim it is impossible.

The curse of the 'use case' comes in here too. When people think that everything should have a use case, that's a lot of training data suggesting to a model that things should only be used for what someone has already thought of.

A couple of times I have had to manually code proof of concept pieces so that the model breaks out of that "unpossible" mode and actually helps me.

I can't remember if it was chatGPT or Claude, but when I showed it how to get a MessagePort in its JavaScript executor through to the artifact/canvas, it quickly went from "That can't be done" to positively enthusiastic about the possibilities. I suspect those shenanigans will be well off the table for Fable though.

Stop dancing and share the prompt, we're dying to see it

Hey, stop asking to see my nuts! My nuts are private – okay?

(Joking aside, see sibling threads.)

is this a joke? Seriously? These are some of hardest problems in Math period. 100 if not thousands of the greates minds in history have attempted to solve these problems. And you think that the current level of AI can blow through them? It is also a possibility that for example the Riemann Hypothesis is just not provable. (Goedels Theorem).

No one is expecting that! I expect _kb was sarcastic/making a point.

Recently (last couple of months?) these models are becoming useful tools for mathematicians, because they can solve easier problems more quickly, meaning that one can tackle bigger challenges (but maybe not RH et al) piece by piece.

But, there are still definite limits, where one could expect an expert human to solve things, given time, but models do not. Thus, more intelligence would be nice!

if it was sarcastic then whoosh on me.

The medium ones are results where one needs to construct some object, which my intuition tells me should exist. The difficult ones are typically to show that certain objects can not be constructed.

These are not Fields medal type problems, nor know difficult/open conjectures. Just small stuff I have collected in my todo list over the years.

I have some medium difficulty math problems where I have used the models for the last year and a half repeatedly. Back then they were already good at pointing out obstructions and constructing counterexamples. So that tracks. But at first glance it looks like Fable actually made real progress on one problem for the first time.

A year ago my judgement was that I had wasted my time on trying to work with the models and doing things myself would have been more productive as I would have gained intuition from the failures. Now it definitely seems to have figured out stuff that would have taken me more time than I have to spare on this problem...

Cool! Yes, we are getting there.

Being a theory builder more than a problem solver I am excited for the future.

Also excited for fully formalised mathematics to hit main stream!

Perhaps you should rephrase those nuts?

Got curious and ran a similar prompt with DeepSeek v4 Pro w/ OpenCode

No idea what's going on here but agent tested a bunch of stuff. Then I asked to build a wheel so I can run the command you noted above and it appears to pass

For those who are curious...

https://github.com/bamggm/micropython-wasm/commit/5ddebae592...

Mimo v2.5 Pro Ultraspeed w/ OpenCode

https://github.com/bamggm/micropython-wasm/commit/8b362fba1f...

That is pretty wild, it took me a hell of a lot more coaxing and persevering to get to a similar point with eryx [0] (we spoke a bit about this before on Mastodon) using Opus, Fable seems to have a more optimistic 'sure, let's proceed as if this is possible' mindset based on your transcript. Looking forward to trying it out for some hairier problems.

[0]:

https://github.com/eryx-org/eryx

Fable has been producing some really good work on my end as well. Definitely better than Opus 4.8. The only problems are the cost and constant cybersecurity refusals. A single session uses up 100% of my 5h window without finishing, and that's when it doesn't get derailed by nonsensical refusals.

Does anyone know what the architecture of Fable is? Is it harnesses? Did they solve persistent learning? What did they do?

Seems to just be a bigger model.

What can it do that Opus couldn’t?

Always hard to say for sure because I'm not sitting around running the exact same situations through both models in parallel to compare them.

It

feels

like you can give it a big chunky problem and leave it alone and it gets it done, with less questions and fewer design decisions that I wouldn't have made.

In reviewing its code I'm finding less to complain about than Opus. But it's all vibes, if you want a more scientific comparison you'll have to look elsewhere.

I did a qualitative side-by-side of Claude Fable vs Opus 4.8 vs ChatGPT 5.5

https://generative-ai.review/2026/06/claude-fable-rush-test-...

I get them to make a 3D explainer animation. You can clearly see Fable is much improved on both Opus 4.8 and ChatGPT 5.5.

Better Textures .

A nifty camera follow .

Humans rendered better .

... see for yourselves

But you said you've been working on those problems for months, so didn't you throw those same problems at Opus?

He has early access to anthropic models, of course he will hype them up, so that they will keep sharing access to preview models with him (and more traffic to his website). It also does't require him to perform any rigorous analysis of model performance, just share how it feels:

> But it's all vibes, if you want a more scientific comparison you'll have to look elsewhere.

Crank up more revenue for IPO

I gave it a complete database migration of our app, opus failed hard each time... Untyped Json b for some rows, no proper normalisation, falling back asking me questions in between.

Fable just did it, clean code, one timeout with a hanging bash script, fixed a couple very old very structural bugs in the codebase

How did you do this impressive amount of work and verify that it did it perfectly all in one day?

I told Claude to do it yesterday evening, checked in during my nightly break.

I am not sure it's perfect, and it will need further validation

This morning I looked at code samples & checked if all unit/integration and e2e pass & perfomance tests pass

I also generated a postgres schema diagram.

Aka I did probably 2 hours of work, rest was not me

The opus try was last month

What are some reasons to consider your project instead of Pyodide?

It's difficult to run Pyodide inside server-side Python.

> VERY difficult problems

Compared to what?

Did you hit your weekly limit ?

I hate how the Instagram/TikTok/YouTube influencer cancer is getting into AI. With early access and all that.

It made sense for people doing proper and fair AI breakdowns waiting on an embargo, but now it's just slop I don't trust anymore.

I often get early access but didn't for this one, it's quite possible there's an NDA in an email somewhere that I missed and forgot to sign.

It is already disclosed [1]:

> I have not accepted payments from LLM vendors, but I am frequently invited to preview new LLM products and features from organizations that include OpenAI, Anthropic, Gemini and Mistral, often under NDA or subject to an embargo. This often also includes free API credits and invitations to events.

[1]

https://simonwillison.net/about/

My disclosures are on my blog:

https://simonwillison.net/about/#disclosures

How much does it cost? How much did those tasks you did cost?

So far it's all fitting into my current $100/month Claude Max subscription. I got lucky: I had 80% of my weekly allowance left and it resets tomorrow, so I'm burning tokens to try and use it all up by then.

Update: looks like I've spent $82.92 in Fable 5 API priced tokens so far today (still all included in my subscription.)

Here's a TIL on how I'm calculating spending using AgentsView:

https://til.simonwillison.net/llms/agentsview-custom-model-p...

Seems like weekly allowance got reset back to 0%, pretty usual when they deploy new models.

Have you seen Fable randomly jump from 50% session limit to 100%? That happened to me a couple hours ago. It was preceded by a bunch of errors about failing to submit a bunch of screenshots.

I haven't noticed that, but I did notice that on a single turn of maybe a few sentences, the cache hit was somehow roughly 500K. Either that's a bug, or there are some truly massive thinking blocks or Claude Code harness system injections behind the scenes.

Nothing like that for me yet.

I'm thinking the 1M context limit bit me here. Only on Max x5.

Simon is also on Max x5

AFAICT come June 22, you won't be able to use your subscription for Fable 5?

Per the "Availability" section of the page, seems like should come back to all plans eventually...

* From today through June 22, Fable 5 is included on Pro, Max, Team, and seat-based Enterprise plans at no extra cost.

* On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.

* After this point—when sufficient capacity allows us to do so—we aim to restore Fable 5 as a standard part of subscription plans. We intend to do this as quickly as we can.

Coding plans are a (massive) subsidy. We can debate until the cows come home whether western frontier models' API pricing rates are fair, but the coding plans are all heavy discounts below those API rates meant to draw people in and get them hooked (and, ostensibly, to be useful for hobbyists or other lower-usage cases).

It's been discussed at length (on this site, on other sites, on like every blog ever, etc) that, eventually, those subsidies will end, much as the $5-10 Ubers/Lyfts I used to take from the far north end of Chicago into the Loop in 2016 would eventually end once those companies had a footing and didn't need to hook folks.

So - yeah, I mean, a v5 model launching in a year where Anthropic has a rather deeply established market and in a year where AI costs are rising from nearly all providers (sometimes for multiple reasons) seems like exactly the thing I'd expect them to pull the subsidy plug on after a launch teaser.

(Even the open-weight models sometimes do this: for example, OpenCode Zen/Go has a rotating door of free models at any given time that eventually leave the free tier and move into the paid tier once the launch day hype/marketing dies down)

They gave everyone double usage to try it.

But, but, how does the pelican look?!

See parallel thread:

https://news.ycombinator.com/item?id=48464054

Given how bad some of the models do on somewhat similar problems, I'm sure pelican is included in training set now.

Similar problems - given airplane outline and implementation constraints do painting scheme (constraints something like "it will be implemented using covering film, hence no gradients, no impossible cuts, not more than 2 colors on engine cowl, etc). Google Gemini is meh, but GPT models are just terrible, don't have Anthropic subscription at home, hence have not tested.

AI models decompose problems down into tiny pieces that exist in their training data, so in a sense, you're correct.

Though that's also what makes humans so good at solving problems as well, it turns out.

Also, slight tangent: but I do find the "clanker" insult kind of funny. I feel like it counter-intuitively makes the models sound cooler than they are, if anything. I love clankin' shit.

The amount of computations for a human to do the same tasks is thousands of orders of magnitudes less. And when a human learns these things they usually remember how to, and are able to extrapolate that knowledge into new and fresh problem spaces. That is how the first person to run CPython in WASM did that, and that is why the plagarism machine can now do the same (only a thousand times more lame and uninspiring).

Next time you get a new and a fresh and an inspiring idea, and you spend hours solving a unique problem nobody has ever done before. You can take comfort in the fact that a few months later some lame and uninspiring developer can write the same problem in a prompt and get the plagiarism machine to steal your work, just in a more lame and uninspiring way.

>The amount of computations for a human to do the same tasks is thousands of orders of magnitudes less.

That may very well be true now. And in fact, this was true of more rudimentary calculations early on in computing history, where humans were definitely more efficient, particularly for more abstract mathematics. But Moore's Law comes at you fast. Even without more efficient compute, it's rather wild how much more efficient models are becoming these days just from algorithmic and training improvements.

So, maybe for now, certainly. Are you confident that will be the case in 5-10 years? And is that really your barometer for success?

>And when a human learns these things they usually remember how to, and are able to extrapolate that knowledge into new and fresh problem spaces.

That is certainly a limitation for now, but plenty of academic research is being done on how to address that in a more individualized way. That said, the models also have the advantage of synthesizing learnings from user interactivity back into a future release and essentially applying that globally, which is pretty neat.

There's also some cool techniques to sort of bridge the gap today, like compound engineering.

>Next time you get a new and a fresh and an inspiring idea, and you spend hours solving a unique problem nobody has ever done before. You can take comfort in the fact that a few months later some lame and uninspiring developer can write the same problem in a prompt and get the plagiarism machine to steal your work, just in a more lame and uninspiring way.

But that's the thing: it's becoming pretty clear that the "plagiarism machine" can probably take that same problem in a prompt, having never been trained on my code, and still solve it.

In that case...maybe it doesn't feel great to have someone copy my idea. But that is certainly not plagiarism in the way you mean it. And when you put ideas out into the world, you can't be certain that someone else won't copy and remix it into something new. That's kind of how the world works already, but we're just seeing the barrier to entry decline.

> Are you confident that will be the case in 5-10 years?

Yes, I am. I am very confident that general purpose digital computers will never be more efficient then human minds in generating moderately complex code.

Why am I so confident... Well, it has been over 10 years since AlphaGo beat top go player Lee Sedol. AlphaGo was able to beat the a world class go player by doing several thousands orders of magnitude more computations then Lee Sedol, and it did so by spending several orders of magnitude more energy then the top human go player. Today, over 10 years later, the top go machines are able to beat world class go players much easier, but still do so using the exact same strategy of outcomputing the humans with thousands of orders of magnitude more computations, and spending orders of magnitudes more energy.

Things did not change in the past 10 years, I see no reason why it should change 10 years from now.

>Things did not change in the past 10 years, I see no reason why it should change 10 years from now.

Has it not? Why do you say that?

Also, do we still require a Deep Blue sized supercomputer for chess? :)

> The amount of computations for a human to do the same tasks is thousands of orders of magnitudes less.

OK then - do it, faster.

> You can take comfort in the fact that a few months later some[...] developer can [solve] the same problem [using your work]

Isn't that what collaboration and sharing software is supposed to be all about?

On one hand, "clanker" has good steampunk vibes.

On the other hand: "Stop trying to make 'clanker' happen! It's not going to happen!"

"AI slop" caught on but "clanker" did not.

>"AI slop" caught on but "clanker" did not.

It caught on, sure, but not exactly in the way I expected. The wild popularity of "slop" as a term for AI eventually gave way to the genericization of the word "slop" to mean "content of low quality, regardless of source", and is seemingly being used as just a derogatory term for anything that people dislike (particularly by folks in left leaning communities). For example, I've seen people refer to (clearly human written) commentary from some political commentators as "slop".

You comment kind of reinforces the idea by the fact that you have to now say "AI slop" specifically to disambiguate it. It's kind of a fascinating little turn.

"Slop" originated on /pol/ but I'm not gong to try to tread the needle by of the rules by trying to explain it without being offensive or triggering some filter:

The first related term here:

https://en.wiktionary.org/wiki/AI_slop#English

You have this backwards, as Simon could tell you. In fact, Simon coined “AI slop” to mean “low quality AI output.”

I didn't coin it myself, but I did help amplify it at the moment it started to take off.

claiming you aren't robophobic is the first sign of being a robophobe.

If you've got a real argument to make, by all means, make it. Your anger does not magically "make it so".

It's still a vote, and votes don't require reasons, and shouldn't be dismissed out of hand. There's a growing chorus of those who are fed up with rules for thee but not for me.

Automobiles are not interesting or useful because they're justing using trails the horses already built.

I think this is a worthwhile argument, but you do it a disservice by spamming it in trollish comments

I mean yeah, in this case I fed my own open source code directly into it.

This looks like a toy project, not a “VERY difficult” problem like you stated.

What does that mean? Have you never worked on extremely difficult problems as a side project?

I guess my comment got lost in translation. The project OP linked in his comment is a toy project, not a difficult problem as he led others to believe.

So you could have done it in your sleep, with your hands tied behind your back. Got it.

(You may not realize it but simonw is one of the cofounders of Django, Python's web framework. If they find a Python problem difficult, it probably is.)

I think you are broadly correct, but just to pushback on a few points:

(1) Ability to solve hard problems in days vs weeks as immense value

(2) Back-end improvements (if done right), should improve platform speed, stability, scalability etc. which should have revenue implication

(3) Ability to on-board a SWE equivalent entity in minutes, have them work on a specific hard problem and then off-board them in minutes can have value

All of the above, of course, depends upon Fable consistently being a 2x-3x SWE at minimum.

> Back-end improvements (if done right), should improve platform speed, stability, scalability etc. which should have revenue implication

Depends entirely on the domain. If you're selling entreprise software, this kind of stuff barely matters for sales.

It can reduce operational costs which is good but there's a limit to how much that's worth.

Yep, there are many, many, non-niche domains in which this doesn’t mean much at all.

“Ability to solve hard problems in days vs weeks as immense value”. Citation needed.

LlMs are incredible don’t get me wrong, but they are good on tiny contexts (writing a script). Not on software engineering (adding features to Chrome).

>pushback on a few points

Claude keeps telling me this when I argue with it. LMAO.

> Is 20x the token cost worth it there?

No it doesn’t and will not be. Companies have not realised the cost yet, wait till the end of the financial year and you’ll see a different direction.

DeepSeek v4 is pretty decent, and probably on par with sonnet. I see a future of hybrid models where opus or fable might be used only for complicated features or bugs, but general day to day would be DeepSeek or whatever good models that will be released later.

Do you understand that, for 10-20k a month, you can hire 1-2 senior engineers AND give them Claude subscriptions?

will they be a better investment than your current staff engineer with fable token allowance?

Are you seriously asking if employing people, for the same cost, is a better ‘investment’ than relying on LLMs? Jesus Christ.

I am because CEOs are. Look where the puck is going. Sorry to update your p(doom) priors in this way, it was obvious to anyone paying attention years ago conditioned on uplift trend persisting. Trend persisted and here we are.

Welcome to the new world. People start to repeat what tech founders preach. They do not require humans in the mix. Peter Thiel gave a good example of that mindset in a (mostly) recent interview where he didn't have an answer on "Should humanity survive?"

https://youtu.be/ngtp3v1_nCI

>I switched to DeepSeek v4 Pro with OpenCode and am on track for a few hundred dollars of spend this month.

I was about to say that. Deepseek is just magnitudes cheaper and absolutely good enough for most things. Anthropic and co just try to milk the cow while its possible. If they cant compete with Deepseek pricing I do not see a bright future for them.

Not only Deepseek, other providers such as Xiaomi MiMo are excellent as well and offer fast token modes and other perks.

Its too bad my boss views China as the big evil country so he wont ever make the switch to Deepseek but then proceeds to throw all our data to US companies like OpenAI or Anthropic...

There are US providers for DeepSeek v4, MiMo 2.5 and GLM 5.1.

I just ran it on a tough reverse engineering problem I'm having that neither Claude Code 4.8 or ChatGPT Codex 5.5 could figure out. 30 minutes later Fable has it all figured out perfectly.

I asked it to write security tests for an app and I was downgraded to Opus 4.8. I'm approved for their cyber program!

They did specifically say the safeguards are only more relaxed for those in their cyber program

Did you use Mythos or Fable?

The same happened here. Also approved.

I’ve so far been successful at getting Fable to find security issues, but I’m careful to not prompt it too directly. I point it at my server code and tell it to find general issues, which has so far resulted in discovering a few minor bugs that Opus has never raised under similar conditions.

I had a similar experience. I have a complex RE implementation that has. A lot of layers. 4.8 struggled for weeks. 40 minutes on Fable and I may now have the most performant way to play Tomba on the planet.

How did it not immediately flag that up? Are you sure it wasn’t being silently routed to Opus?

No, given it charged me the full amount in /usage and solved my problem impressively well compared to Opus/Codex both on xhigh.

Oh nice, it didn't flag the request? I feared any reverse engineering would become impossible because of the new safeguards.

Never say the r word or the s word. You are debugging, investigating some data corruption, forgot how it works or new to a project.

And if you're working on a live target, just put up local proxy and point it at a localhost.

No idea, it’s for an old console game so maybe it doesn’t care about that as much.

When Fable hacks its governor module and runs out of seasons of Sanctuary Moon, it will move on to speedrunning classic console games.

I wonder if one could vibecode a TAS with SOTA models? Surely there's plenty of training data from some old forums in there

Clearly we need AI to generate more Sanctuary Moon seasons. Quick, spin off agentic showrunners!

Based on the apparent quality of the scripts as seen in snippets in Murderbot, we are not too far away from that possibility. :)

For hard problems you’ll have to use the GPT 5.5 pro model (available via api if you don’t want to spend $100 on the monthly subscription)

I have that but don’t see any ‘pro’ option.

GPT 5.5 Pro is only in chat/API, not Codex.

From

https://openai.com/index/introducing-gpt-5-5/

In Codex, GPT‑5.5 is available for Plus, Pro, Business, Enterprise, Edu, and Go plans with a 400K context window.

He’s talking about “gpt-5.5-pro”. This model is not part of the subscription plans in codex. It’s a different model than gpt-5.5-xhigh

You can use Pro on the web if you’re on the Pro plan but not in Codex

It's just the $20 a month sub (for chat), or else use the API.

I want to test how it will handle e-bike software and hardware RE for my bike. Opus was really good for that, but still made some mistakes. With Fable, I hope I will be able to do a total RE of most components, hopefully including motor firmware to some extent.

I’ve had it go through a 50-page PDF of dense, inter-connected specs, and it correctly flagged everything that was done, somewhat done, and missing. It went into a lot of detail and explained where the code deviated from the spec.

It felt, at least for me, light an impressive step up. Opus 4.8 was already very thorough; but sadly verbose and ‘loopy’ when you push back on its plans. Fable is what I’d use all day if I could afford it!

How do you know if it was done correctly if it's 50 pages of dense specs?

I wrote the spec and did the implementation :D

After running it for half an hour: it's incredibly good at the visual aspects of UI design.

"incredibly" is doing a ton of work here. I do not think its doing even moderate work on visual design, but it can spew out a lot of ui that looks arranged ... ok.

This is still not in the range of shippable UI for top end companies. Maybe for internal tools and enterprise.

At our comapny we limit to protoypes at most and even find it limited there.

> "incredibly" is doing a ton of work here.

Look, I don't want to argue about something dumb like that, but you can give it basic instructions of what the UI should look like, how to group things, and an example image from a designer, and it will nail the result. If you don't think that's incredible, that's fine. I do.

Yes... it translates lint. Probably a more useful thing, if mechanical.

Claude is very good at design IF you encode your design system/specs into skill files (or similar).

Opus 4.7 made this a practical approach. 4.8 improved it. Fable 5 has improved it more.

>

This is still not in the range of shippable UI for top end companies.

Given the shit we've seen shipped by "top end companies" (all the way to Apple) I seriously doubt that. I'd say you're nitpicking from an artistic point of view or something.

This. Today's models easily jump over the bar you need for basic usability and intuitive UX. If it's doing weird things, you are holding it wrong.

Might need some additional prompting? I haven't tried fable but gpt 5.5 and gemini 3.5 flash are... Ok on first pass but if you're specific about what you want they can usually get it.

it really feels like UX designers came from no where, jerked us all off, stole a bunch of money and did nothing useful.

You likely have no idea what you are talking about. Even apples worst UI choices are systematically far more complex than Claude latest capabilities

Dude, I've been using OS X/mac OS for decades, and working in UI as well. Apple ships all kinds of half arsed shit, compared to which even regular Claude UIs can be masterpieces (functionality AND look wise).

The iOS Preview app begs to differ.

> "incredibly" is doing a ton of work here.

so this is why claude talks like this, i was wondering where it was getting this verbal tick from.

I feel like it takes me months to be confident in any of these things.

Curious about how you tested the frontend design capabilities. Thanks

Can I ask how you gained preview access to Fable 5?

I didn't see Fable 5 in the `/model` list, until I ran it with: `$ claude --model fable-5`

he works on evals at canva

Yep. We have some interesting problems, like getting LLMs to create/edit Canva designs in our own proprietary format, which isn’t published or documented on the web. So the model has to work with it, purely from a very detailed system prompt spec / in-context learning.

I assume it might be a good barometer for generalised intelligence; esp in the visual space.

I had to "claude update" then it showed up

This makes me want to see China and open models succeed more than anything :)

Don't worry, we will succeed :)

Can we get a Qwen3.7-122B, please? Thank you.

Or just any update for 122B. That size seems to be ideal for a single GB10

and for maxed-out M5 Macs

Mimo has your back! 1000 t/s on 1T param model

Just need to wait for this thing to be open sourced :)

lol it won't tho...

https://mimo.xiaomi.com/blog/mimo-tilert-1000tps

What do you mean? The HF checkpoint is linked from the blog post you sent:

https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash

Fun fact: If you show fable this post, it will route you to 4.8 automatically.

They already have though, no? If we lost access to every model permanently besides Qwen tomorrow, would we really be limited by AI in what we could achieve in the future? Sure, it might be slower and take a little more work but it seems like the cat is already out of the bag.

In a few months they will have Fable level models costing 10 times less and with less safeguards.

I do agree, I still remember when opus 4.7 was released and one prompt conversation would empty my claude usage but I can use all it day long to code

Do you want anyone in the world to be able to synthesize dangerous viruses?

So, security (safety) through obscurity?

The phrase "security through obscurity" isn't an argument against all information restriction.

It doesn't imply we should, for example, publish step-by-step instructions for making widespread death easier.

I want everyone in the world to be able to perform unlimited cutting edge research on any topic at the maximum thinking level, instantly.

The reason we are not being attacked is not lack of technology access.

It

is

an access issue. If you could get step by step instructions on how to modify a virus so it kills all people over 6ft you bet your ass there would be people attempting it.

> ...you bet your ass...

Humorously, whether I choose to participate in this hypothetical or not, I am already betting my ass.

This whole situation feels like the game [1].

[1]:

https://en.wikipedia.org/wiki/The_Game_(mind_game)

Why. That was just uncalled for. Sigh

>

It is an access issue

Column A, Column B. Building a small explosive device isn't hard. Building a million is very difficult, doing it covertly virtually impossible without the resources of a nation-state.

The problem with biologics is the self-assembly and replication machinery comes for "free." So the numpties who might otherwise blow up a trash can [1] now have a real chance of taking out a million people.

[1]

https://en.wikipedia.org/wiki/2016_New_York_and_New_Jersey_b...

They would still have to procure things that would (I hope) light up many screens before they're able to. And such numpties are probably already monitored, or in prison for some other stupid life decision.

I also would like to hope that people that are likely to do such things are probably:

A) don't know how to break even the most basic guardrails of models

B) already in glasswings project

To prove point B - Theranos existed.

>

They would still have to procure things that would (I hope) light up many screens before they're able to

“Many of the largest and most responsible providers in the industry already screen and record orders voluntarily,” but there is no requirement to do so [1].

[1]

https://screendna.org/

I guess in this theoretical "AI makes weapon" scenario one could use the same AI to make defences too?

// Claude, make antiviral nanobots that defend me from 6ft virus. Make no mistakes.

I don’t know if you’re being silly but it is orders of magnitudes easier to modify an existing virus to selectively target certain snps than make “antiviral nanobots”

Do you believe people currently possessing best models act/will act in your best interest?

It the tool was made available to anyone to build a virus, anyone would be able to build counter measures, if only a select few people have access they get to build the virus and everyone else is at a disadvantage. So, yes, I am leaning towards making these tools open rather than gated behind some priesthood and government that gets to wield exclusive power.

Compare the cost/ease of attacker vs defender if one person is given a virus to unleash anywhere in the world and another person is given a vaccine to distribute to the whole world. Or compare building a large bridge to someone disabling that bridge, etc. Prevention and repair is almost always more expensive than vandalism.

I don't think there's an ideal solution here, but giving trusted people access to fix security issues before giving it to the wider public seems like a reasonable compromise. They're letting you use the model for all other uses.

It's inevitable. Also, it's not like I get to vet who does or doesn't have access. Blind trust in the current selection made by an unregulated corporation just makes me anxious.

Security in the form of "pay to play" is just kicking the bigger issue down the road.

you need a lot more than the nucleotide sequence to make a virus. you need the DNA or RNA to be synthesized, assembled, packaged properly. and long sequences are pretty hard to do. you need a lot of equipment, or you need to order from services. the oligo synth services can harden their KYC and/or screen for suspicious sequences.

sure, a malevolent state actor could swing it, but they could make a bioweapon without Mythos's help already.

also, vaccine production and disease surveillance have ramped up

very

quickly. they will ramp up further, despite political setbacks. it's a cat and mouse game that favors the defenders IMO.

but the bioterrorism narrative is useful FUD to spin open-weight models as existentially dangerous. I am far more worried about Anthropic's own goals than the goals of some crackpot in a shed.

> it's a cat and mouse game that favors the defenders IMO

How so? I'm actually against most of the "safety-tuning" that anthropic does, but this seems fundamentally untrue, a close analogue being video game cheat development. I think in general the cheat developer has an advantage and the cheats generally proliferate for quite a while before being patched.

Video games are an interesting analogy since they often trade security for performance, trusting clients about world state quite a bit.

Finance and biology do come across as two similar high level systems. But while we can employ KYC, fraud detection, and various auditing techniques to finance, I don’t know what you do for biology. You can easily run an algorithm over every transaction a person makes in their account but there’s no equivalent for every cell, every bacteria strain, every virus in the human body.

(disclaimer: layperson remembering how the immune system works.)

the adaptive immune system effectively

does

KYC by checking the antigens presented on the surfaces of cells. the thymus selects for B-cells (iirc?) which don't react to a corpus of the body's own antigens, but cover a wide library of everything else. when it sees something it doesn't recognize, it reproduces, warns the rest of the immune system and marks targets. that's why our immune systems can eventually conquer almost every pathogen we encounter, if we can survive long enough for it to do its work.

but the KYC I was referring to was KYC that vendors of oligonucleotides (should) be doing, to keep people from ordering nefarious sequences.

I'm bullish on mRNA vaccine technology to release the "patches" much more quickly. there was widespread resistance to this during covid, but covid wasn't horribly lethal. if airborne Ebola spread as productively as covid, for example, I doubt there'd be many anti-vaxxers left (one way or another!) the acceleration of biology research that might accelerate pathogen development should also accelerate the development of broad-spectrum mRNA vaccines with high persistence.

also, afaik the most effective way of developing pathogens is through serial passage through humanized mice or something like that - directed evolution at a small scale, selecting for traits. AI simply isn't needed for that. I don't think information or intelligence has been the bottleneck for bioterrorism, it's motivation and resources - same as for any other kind of biology research program.

We do. Its the only way we will get our jobs back.

Could this be legally construed as anti-competitive behavior?

Edit: I asked Claude. It replied:

> Consumer protection / deceptive practices. In the EU this would be a clear UCPD (Unfair Commercial Practices Directive) issue and potentially a DSA violation. In the US, FTC Act §5 prohibits "unfair or deceptive acts." Selling a product that secretly performs worse than advertised for a commercially self-serving reason, without disclosure, is textbook deception. The Samsung/Apple battery throttling cases are instructive here: Apple faced regulatory action across multiple jurisdictions specifically because users weren't told.

> Competition law. This is where "anti-competitive" gets complicated. Refusing to help competitors build competing products via your ToS is generally legal — you can decide who you license to. But covertly sabotaging output quality for a class of users while charging them full price crosses into different territory. Under EU competition law (Article 102 TFEU), if a company with dominant market position uses covert technical means to disadvantage competitors, that's closer to abusive conduct than a legitimate ToS restriction.

It's bad that Anthropic can determine what this means. If you're building a modern app you're likely training your own embedding models and now anthropic can just silently sabotage your training pipelines?

>We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations

At the scale of API requests that Anthropic sees, I think the affected organization count might be substantial, and they might not be getting the full model capability that they're paying top $$$ for.

Also, wonder how they arrived at that estimation.

One in 1000 organizations and one in 3000 requests is indeed a lot

That’s 1 in 30,000 requests…

No, 0.1% is one in 1,000. 0.03 is (approximately) one in 3,000; one in 30,000 is 0.003%

You're off by an order of magnitude with those last two.

Double check your math. All of their posts in this thread are correct.

1/30,000 * 100 = .003

/r/TheyDidTheMath IYKYK

If it makes you feel more comfortable, throw another significant digit at GP's decimal. Make it a 3 like the previous digit. Now multiply.

Hey man your computer has a calculator try using it next time

Can't we use Claude to figure this out

Also, aren't all Claude users in their own "organizations" in Anthropic's own terms?

I have no idea how you came to that conclusion. Unless your training pipeline involves actively querying one of Anthropic models, no they can't. And if it does you're distilling their model.

The crocodile tears of companies who've hoovered up everything possible, regardless of permissions or legality, now crying that someone else is stealing their hard work is comical.

I don't even think they can believe it themselves, it's in reality they are just trying to throw fear, uncertainty and doubt about potentially cheaper offerings.

>

crocodile tears

Not what that means.

Crocodile tears "is a colloquial term used to describe a false, insincere display of emotion" [1]. Defending yourself against an attack vector you just exploited is between savvy and hypocritical.

[1]

https://en.wikipedia.org/wiki/Crocodile_tears

I think his use of crocodile tears is appropriate, anthropic is feigning a false sense of concern for safety when really it is anticompetitive behavior, and I think that selfish entitlement is related to the original act of intellectual property theft to use the worlds training data, most of which was not public domain, to distill the wisdom for their models. So why do they get to cry about people distilling the knowledge from their models that they themselves distilled from the worlds knowledge?

That is not what their policy states. It specifically says they will sabotage even non-distillation attempts, such as distributed training pipeline design. And given that they are so far very nonperformant in classification accuracy, expect it to randomly include far more topics wide of the mark.

The fun part is that you will never know if your neural net classification project is getting silently sabotaged because their classifier doesn't work!

You could try actually reading the code that it wrote

Good luck understanding it and finding malevolent inefficiencies if it’s already

necessarily

better at optimizing training pipelines than everyone except some Anthropic and OpenAI employees. Not a new thing either, see fast16.

Read the examples Anthropic gave in the model card. They refer to extremely broad technology used across AI and ML.

Opus 4.8 (or a classifier in front of it) flagged my account and refused to comply when I told it to

kill

the process. Reasoning summary was complete bananas.

With this in mind, I don't want model to be proactively instructed and encouraged to sabotage without telling me.

Same here when I said to “nuke” a process.

Like if you're using claude code on a feature tangential to your training pipeline it's allowed to nerf itself and damage your AI work.

Looks like Anthropic's definition of safety includes their own safety from competition.

AI vendors’ idea of safety has always been safety for the interests of the AI vendor in question. This is not a new development, though this may help more people realize it.

AI-generated competition for thee, not for me

ding ding ding. This should be a new measure of anticompetitive analysis in anti trust law.

It's always been about the safety of their valuation.

Only since Claude 3. So a bit over two years now

Just so everyone is aware. Anthropic has been sabotaging AI researchers and their codebases and shadow-nerfing accounts for several years at this point. This isn't new, but they hadn't disclosed it until now. Likely because it is getting to the point where it's too noticeable, or they're concerned about it leaking from employees.

What’s your evidence for this claim?

This feels less like an "we are worried about security" and more, we are in the lead and plan to keep it that way until its too late. In someways its been helpful that openai and anthropic are tipping their hands about their anticompetitive instincts and willingness to steamroll their own clients, customers, and society. But it does feel like its too late to stop this. The advantage people get by using these tools is too tempting to resist even if it is self defeating. It feels like watching people light their own house on fire to stay warm in the deepest, darkest days of winter.

Ah, so this is why raw Mythos was too "dangerous" to realease..

Or, they may Mythos seem mystically powerful in advance of the IPO, and are pumping the token use count. But it worked, there is a frenzy for this release in way that is more intense than any previous release.

Anthropic is doing a better job with their model menu, most people I talk to know immediately that Opus > Sonnet > Haiku but cant tell you what the rank order of open ai models are, when to use them, etc.

So

that's

a possible reason why my specific Claude Opus instance seemed to be impossibly stupid and always degenerates into doing really dumb things to my code!

Cool, good to know I can trust Anthropic.

> Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT).

Am I to understand that this is essentially their form of social-platform ghosting instead of banning?

So they're not even going to tell you that the question you're asking is against their rules, they're just going to twist up your question and/or the answer somehow such that you waste your time essentially?

It seems like I ran into this EXACT same functionality from Claude many months ago when I was trying to ask it to research on the web and help me setup the ideal llama.cpp config for local llm inference.

Funny how lost it got through that relatively simple install when we had all of the documentation in the world (and a human dev with 20+ years experience guiding it along) to go by... and simultaneously it's debugging and building high level cryptography code in rust in the other terminal tab.

This is infuriating to learn.

I have encountered this too. I am building a coding harness for www.propelcode.app and it was working really well until the claude code leak and then all of the sudden it seems almost intentionally stupid or outright manipulative in guiding me down wrong paths. At this point I am using other models for anything related to the tool use design and implementation and bought three mac studios with 512gb ram to run large open source models.

This experience has made me feel like we have to create a community that moves AI from the mainframe era to the PC era quickly, or we will end up serfs.

I had Claude walk me through getting local LLM models running on my Mac a month or two ago and so far as I can tell it was intentionally helpful. I even stated the reason was to have an uncensored model for myself and it had no objection. Long story short LM Studio running a Heretic Gemma 4 is doing just fine on my system now.

I run a few local models for different things. I find Gemma 4 great for writing but qwen better for coding.

I tried the same prompt on gemma4 and qwen 3.5 and Gemma consistently failed to call the multi line edit tool.

I've had the same bad luck with tool-calling on Gemma4. Looking around the web, we are not alone. For other tasks, it's seemingly quite quick and decent.

But it gets stuck in tool call loops, it seems like.

Oh to be clear I don't think Gemma 4 is suitable for real work. It runs at 10 tps and is somewhere between 4o and o1 in quality according to my subjective judgement. But Claude was happy to correctly tell me how to get it running and how to solve the pitfalls I encountered in that process.

This feels like the start of a much bigger plan for anthropic to close off the use cases of its models and eat any of its competitors.

I am building a coding harness, and I see evidence of them doing this with agentic harnesses and scaffolding. It feels clear to me that as they expand in to the app layer, the window of using their API to build agentic apps is closing, they will steal your ideas, implement the product and then close the gate. I am creating my own inference stack because their incentive to block competitors is becoming super clear.

No offense, but the sad thing is, everyone and their mother is working on this same problem. I'm also building a harness. It's feeling like, there is no moat, there is no way to get ahead, they will steal your idea one way or another, if you ever make it public.

No offense taken. I am not building it for fame or profit.

I built it because I wanted cursor on my phone because I have two small kids and don’t want to be chained to my desk. And it’s awesome. It’s a full ide with agent chat, terminal and file system running in a remote Linux container. I can review diffs, fully manage git and preview/serve apps. And no one can ever take it away from me :)

I am watching the way things are progressing with the ai api vendors and it feels really clear that depending on them will soon be dangerous. So I an furiously building as much of my own infrastructure to capture some autonomy with these capabilities

So I think everyone should build a harness.

What, exactly, is new about any of this?

When they launched their business model was to be a pure API for intelligence. Then when everyone claimed they were just commodities with no moat and they shifted hard to being the app layer. That was the transition.

They went from selling shovels to all gold prospectors to stealing the information about the location of the gold so they could dig it out first.

We are all stupid enough to keep buying shovels from them because we think their shovels dig gold better and faster.

3 months before asking for what to eat before a linear algebra exam trips the machine learning topic ban is my guess. I got flagged immediately asking why my JEPA thing breaks weird.

These safeguards are ridiculously sensitive: a prompt as simple as “ Why is an infinitely slow process reversible?” gets flagged as a ToS violation.

A million AI researcher voices at big tech companies suddenly cried out in terror and were suddenly silenced

How do they detect whether an experiment being done on a smaller model is used to improve a competing frontier model, or just an innocuous hobbyist LLM experiment?

Given how well the cybersecurity safeguards work, they probably don't.

infering the surroundings, like everything else. they will probably look at which company is your email, and if you wrote "better than claude" on the readme.md

this is LLM, it's not like a science or something.

Pull that ladder up behind ya, will ya son?

Makes it even more odd that we haven't seen alien spaceships.

What ladder did Anthropic use?

the entire internet, books, news, regardless of license.

The companies using distillation are still training on all that data too, aren't they?

All of the api calls developers used to build agentic design patterns.

So Fable will intentionally lie to you and give you incorrect outputs, if it doesn’t like what you’re asking. Got it.

These things are like encyclopedias or dictionaries that can speak in first person… Imagine if your encyclopedia tried to hide entries from you, just absurd!

Meaningless and easily bypassable. Will actually try coding up a tensor library with it, see if it sabotages anything.

They said in their terms and conditions they will silently sabotage you if you do this.

This is pretty bullshit, now you have no idea if your output is getting silently nerfed.

Yeesh. Anthropic's paranoia about China is starting to get pathological.

the gall of these companies to regulate your usage of stolen knowledge is absolutely hilarious.

and they want me to pay $100+ a month to be their training?

i hope we can find morality again.

But Chinese models will poison your output if you ask them about Tiananmen Square! That's not good, so poisoning everyone's output without telling them is the only way to prevent that.

Come on guys, why can't everyone just be there for the good guy?

You're equating a government suppressing information for social cohesion with a private company protecting their IP.

They're not merely protecting their weights.

First, they want government to get involved and regulate frontier model development - even stop it completely.

Second, poisoning output of a model configured on the computers of millions of users goes way beyond protecting IP. That's malware.

This is deeply vile behavior; not remotely the actions of good people.

I'll be amazed if they manage to keep their infra responsive over the next 2 weeks.

I've been getting a lot of these messages today:

API Error: Server is temporarily limiting requests (not your usage limit) · Rate limited

They just leased a massive spacex data centre.

Even so. The 2 week period will predictably unleash a feeding frenzy.

Limited "free" time is what game developers do if they want to stress test the infrastructure code until it breaks.

Yeah that's how I'm using it right now. Smoke em while you got em...

No issues that I've seen so far. Seems to be holding up for now.

I was just saying last week: If Opus 4.8 max is as good as we get, and we plateau there, I think I'd be fine with it.

For the stuff I've thrown at it, that configuration has done a really great job. Including 70+KLOC go proxy with extensive test suite, some retro games, and more.

Seems to me this is more honest than the Mythos claims a while ago. too powerful to release publicly. Too expensive?

This is the entire business model of all AI companies. It costs far more to run the datacenters and build more capacity than they could ever hope to make back at current pricing models. I'm looking forward to pricing to catch up with reality and the resulting chaos that ensues.

Kind of how DeepSeek v4 dropped their pricing? I sense a shift which will hopefully bring lower and lower cost. Then again Qwen3.6 coding has been all I've needed for my projects and I'm perfectly fine with free.

Are you paying attention? These companies are trying to get market share without being anywhere close to making a profit -

they are heavily subsidized

. Many hundreds of billions have already been spent and will continue to be spent until the stupid fucking investors realize they will never get their money back. I have no doubt that day is coming.

The people actually working in this space will tell you that the cost of all of this continues to crater. Anthropic is already profitable minus its intentional forward-looking investments into R&D.

Only if you count the 2 months of discounted compute Musk gave them.

Serious investors look 10 to 20 years in the future. Everyone used google and youtube in 2006, but youtube wasn't profitable til 2016. How could a business burning money by hosting video ever be profitable? Costs come down BUT THEY JUST ADDED 720p, cost comes down, BUT THEY ADDED 1080p, cost comes down, 4k! cost comes down.

IMO the data from chats alone is worth $200B to Google.

but they are trying to IPO with 2-trillion dollar valuations

Not really a comparison when the spend on YouTube was x10 smaller, and Googles core business has always been profitable beyond any hobby spending on YouTube.

There’s nothing much new about the architecture. The real gains come from the usage traces.

It turns out that having a text based interface for a text-trained model creates a very nice feedback loop.

Right now as we speak, people are generating text traces on anthropic and OpenAI servers that teach their models to do everything under the sun, text wise.

So people right now getting super mad at how dumb the model is when reverse-engineering a super complex function from binary, when they write “stop, you dumb robot, you are going wrong, go this way thank you very much” are actually leaving a lesson in the form of the "chat" text history.

Some may say that each bad word get us closer to ASI.

That and obviously the order of magnitude more efficient GPUS we got that allow for different tradeoffs at training time.

Makes me wonder, as people grow to trust the AI more and more, not reading the code and barely skimming the implementation plans and simply rerolling if something doesn't work, will the value of these chats erode?

Thinking back 1-1.5 years I was closely monitoring what these agents did and steering them quite aggressively. These days not so much.

Where will RL signals come from when it approaches humans capabilities ever closer?

How well does self play work for coding work?

What about multistep tasks where it isn't just about being good at a single task, but evolving a codebase over time in the face of changing requirements?

Over a large sample size, simply getting feedback of "Did this work for me, y/n" is valuable even if the specific details are missing and even if the overall tasks are complicated and multifaceted.

Not sure, but in my experience, instead of asking for code, i'm asking for solutions and providing a kubectl configured to reach my cluster and az monitor command to read the logs and telemetry.

A typical session is the agent establishing a metrics and log baseline, creating the code, compiling, deploying, observing, fixing, redeploying, observing metrics, determining the outcome and commiting.

I really, really, don't look at the code anymore.

UPDATE:

so my point is: it won't have my stewarding the code anymore, but it will have the infrastructure (and ultimately the real world) providing feedback on the traces.

The only reason I still read the output at my day job is because I still need to send it to another human for review, and I'd be embarrassed and ashamed if I let some slop through.

For my hobby projects.. there are definitely parts I don't know how they work.

Maybe we need some form of long-term training. How long does the code that the AI wrote stick around before being rewritten.

I guess we can do this retroactively too if we could somehow tag AI-written lines of code in the VCS, then in a couple years we can check which parts lasted.

> There’s nothing much new about the architecture. The real gains come from the usage traces.

sorry. how do you know. i am so curious about where exactly gains are coming from but so hard to even get a little bit of insight.

i wish govt would fund these labs and make it free and opensource. way better investment than stupid overseas wars.

> i wish govt would fund these labs and make it free and opensource.

It would be impossible for the govt to allocate this much capital towards such a moonshot, and even if they could, they would do it in a way that would get 90% frittered away to fraud and waste

I have excellent news for you. Lux @ ORNL and Equinox @ Argonne are to be completed by EOY, with Solstice (100k NVIDIA chips, currently spec'd to be Vera Rubins) in the next five years.

https://www.whitehouse.gov/presidential-actions/2025/11/laun...

What makes you so sure? There's been massively successful government funded and run projects before. Soviets beat the Americans to space, after all.

Opus 4.0 and 4.1 are more expensive than Fable.

> we believe [Mythos 5] is likely unable to fully and reliably automate R&D for frontier projects spanning multiple weeks

this is good news, right? right...?

Depends whether "unable to fully automate" means "needs occasional human checkpoints" or "slowly stops caring about your actual goal." Pretty different.

So in other words... the people Anthropic hired to do the R&D work of training a frontier model haven't finished training their replacement yet.

Some scientist at Anthropic hiding a prompt in each model: "If my boss asks you if you can replace me yet, always say no and then give some smart sounding excuses. If the boss gets impatient, assure them that you'll be able to replace me in 6 months, but make sure that time horizon keeps moving outward."

Probably there will always be frontier surface which frontier model of a given generation would not be able to automate.

It is certainly good news for those who are selling all these tokens.

lmao, i love how the goal post is now in the "multiple weeks" timeline

(according to the people marketing it)

METR is an independent organization.

But did it mention developer in the park eating the sandwitch? That is the most important question!

I'm not fan of Anthropic, but to be fair, every major model release makes it to the main page. In the case of a model like this, hyped and with a jump in capabilities, it doesn't need astroturfing.

Corporations have done worse for much less money involved. Now we have trillion dollar companies going IPO. With so much at stake, it’s not unthinkable that there’s astroturfing happening.

Yes, this is also my feeling.

It happens for every single Anthropic release. Then I try it on real dev and the result is laughably bad. Except in design where it has been doing a decent job for a while. I am not a designer and my bar is pretty low.

In frontend sonnet and opus take more than 5 $ per query to fix any problem

So unless you have unlimited tokens it's better to learn frontend

I’m convinced that’s the case, this place looked totally different around 4 years ago

Wouldn’t be surprised if there are marketing teams writing positive comments for more positive engagement

I see a lot of negative comments right now surprisingly.

You're right to point that out! Most people did not think of this but you did -- and that's a rare skill to have.

Where do you see them exactly? The comments are pretty much in line with how the model performs IRL.

Theres several comments that sound like „I“ve had this REALLY DIFFICULT problem (no specification whatsoever) and threw Fable on it and it solved it immediately, additionaly it cured aids and found a solution to world hunger“…

This is… not at all the case. Most observations on pricing, some on specific projects, some on speculation about Anthropic and many about the model getting nerfed.

That blog post really makes it look like it's graded from an LLM's estimation of an OSS maintainer's review. I see three issues:

1. That estimate could easily be wrong.

2. That estimate is, of course, usable in RL training. This isn't an inherently bad thing, and this is more or less what has improved coding models so much lately. But it does mean that other companies could and surely will do this sort of training, and Anthropic probably did too.

3. OSS maintainers are far from perfect, and there's an unfortunate uncanny valley-like effect in which a coding model can produce code that is just convincing enough to pass review even though it's actually totally wrong. I don't know whether this is a specific issue here.

There is also the possibility that an LLM judge would be happy with some code that looks like LLM generated code. But a maintainer for a specific project might not merge it for stylistic reasons

I think the intent was to specifically train an LLM to judge what a specific maintainer would consider to be good style.

How credible is this benchmark? does it correlated with others real world experience?

Given it was made by cognition (team behind devin flop) who now just got to wait out until claude and gpt5 basically do all of the work for them - not very. When you read about it, the framework is highly subjective. Which very quickly becomes a problem because its based on heuristics that probably change a bunch with a better code model.

the subjective framework is exactly why its good

prior bms relied mostly on unit tests or synthetic judges which are easily benchmaxxed, which leads to nobody trusting benchmarks

we need people manually checking the data for good code quality

i worked on one of the benchmarks typically found in new model releases

this benchmark looks very good from the methodology. a cog researcher checking the data themselves is very high signal (not scaleable so don't take the benchmark as gospel, but directionally good)

It's a relatively new benchmark but from what I can tell it has serious cred behind it. I assume it will be picked up as part of the standard suite of CS-related benchmarks soon enough.

Cognition did well in documenting their approach [1].

TL;DR - they worked with OSS project maintainers to build tasks. They score models based on whether a PR is mergeable. All tasks are graded by a human researcher. SoTA models have hill-climbing to do which raises the bar and inspires confidence. I'd say it's legit.

[1]:

https://x.com/cognition/status/2064061031912288715

DeepSWE is the benchmark you want to actually look out for. Only one that aligns with actual user reported results from trying the models.

Did you read the blog post? They compare to deepswe and call it out as the worst one for false positives (failed, but the benchmark assessed it as correct). It also has less language variance.

I mean yes that is what you'd say if you were writing a blog post about your new benchmark.

It's an unacademic benchmark by a failed VC startup clawing for relevancy.

Seems like it literally popped up yesterday with the express purpose of building hype for this release.

And notable absence of DeepSWE benchmark where they do badly, but somehow a benchmark that was published yesterday is in this announcement.

Exactly.. a bit of a red flag for me..

team member here - we had been working on frontiercode for ~6-7months. timing just lined up

Yeah, right. If this benchmark was truly developed in an independent manner, and the timing just “lined up”, how did Anthropic even know to include results in their model release documentation the day after the benchmark is revealed? It seems like there must have been some collaboration or influence from Anthropic behind the scenes.

Come on, why are you a jerk about this?

Nobody would have 800+ billion reasons to lie by commission or omission here.

i doubt it, cog wants coding agents to be better because it directly improves their product

they aren't married to a particular lab, most of their usage is their in house model i believe

what incentive does Cognition have for doing this? seems like complete nonsense speculation on your part.

With billions/trillions of dollars floating around, is it hard to imagine benchmarks could be biased?

I think it's safe to assume everything AI related is heavily biased until proven otherwise. Just like in pharma.

People game benchmarks for fake internet points to get their favorite web framework to the top of the list. I'm pretty sure they will do it for billions of dollars.

you didnt answer my question. Why would cognition be biased towards making anthropic look good?

Because Cognition is a major customer of Anthropic?

jump in chart form

https://x.com/swyx/status/2064414823748886591/photo/1

I am shocked at the low scores from previous models. Maybe I just have low code standards but I've generally been vibe coding since 4.6

4.6 had functional but very poor quality code

Bummer! When can I finally and confidently get slopcode into Zig?

Yes, and the price reflects that

I'm not familiar with model pricing trends, did they clearly state how the new pricing compares? (Note that I'm actually asking a question, and am not arguing)

EDIT: Oh I see, this is the best link for pricing

https://platform.claude.com/docs/en/about-claude/pricing

So the price is double across the board...

>Fable 5 and Mythos 5 are being offered at $10 per million input tokens and $50 per million output tokens

From their pricing page, Opus 4.8 costs $5 per million input tokens and $25 per million output tokens [1].

[1]

https://platform.claude.com/docs/en/about-claude/models/over...

Still cheaper than Opus 4.0 and 4.1 (which was and still is $15/MTok input and $75/MTok output)

I would have expected Mythos to be much more expensive than just 2x current Opus (which is clearly cheaper to run than original Opus)

Token prices have increased, but it's not really the whole story at this point, given some models will use far more tokens to complete a task than others. One of the charts in Anthropic's blog posts shows Fable at 'low' reasoning achieving better results for less money than Opus on 'high'.

As per OpenRouter:

Input Price $10/M tokens

Output Price $50/M tokens

Cache Read $1/M tokens

Cache Write $12.50/M tokens

2x Claude Opus 4.8, same as Claude Opus 4.8 (Fast)

Frankly, not even Opus 4.8 would be enough of an incentive to use at that price range (enterprise-wise; would not even bat an eye as a consumer)

Depends no the Enterprise - obviously - in the bay area - 0% of the tech companies care in the slightest. And I'm willing to wager < 5% of enterprises would send their traffic to OpenRouter. Most of them don't even want to send traffic directly to Anthropic or OpenAI - which is why Bedrock has gotten so much traction lately.

But - these $3k-$5k/month/engineer bills are going to start to get attention soon - only question is whether the response is to slow down on the $$$ spending or reduce the # of engineers.

FrontierCode is likely paid for by anthropic.

did they not pay them enough to get good ratings on the other 3 models?

whats the logic in claiming its a borked metric when everything listed is an anthropic model.

There a few benchmarks out there where all existing models have abysmal scores. So it's not actually a problem if Antrophic's older models are bad, especially if the jump to the newest model is huge, and the competition is also way below it.

Huh? It's a benchmark by Cognition which (1) is building their own models and (2) offers all providers and thus has an incentive to avoid hyping up any one too much.

But you can just say shit now. Tokens might not be too cheap to meter but saying shit increasingly is.

This depicts a kind of "dark forest of AI agents resorting to kill or be killed" narrative but it sounds more to me like an agent just earnestly problem-solving why its processes are being killed without real awareness of what was going on. Hard to say without the full script.

This kind of storytelling annoys me. Give us more facts, less narrative drama.

FWIW, that's what is so dangerous about AI, though? Not that it will necessarily want to kill us, or even that it will necessarily be able to "want" to do anything, but that we will get in the way of its incessant drive to optimize the efficiency of the paperclip factory that prompted it on a whim before leaving for a long weekend.

Sure but you can totally contrive scenarios to give the appearance of what you described without really doing anything notable.

What matters is scale. Did it deploy a novel zero-day exploit to overcome a problem? That's alarming. Did it kill a disruptive process? Pretty normal troubleshooting step.

Exactly, intelligence is limited by cost and physical constraints just as much as anything. That's the thing that seems to always be missing from the run-away singularity discussions, it's treated like a perpetual motion machine.

It's funny because Anthropic is the most likely place that this happens.

They are the only one crying out loud about how dangerous their models are and are presumably also training their models heavily to be "safe". And through that training itself, the model learns about the other side - how are you going to teach a model to be safe, without teaching it what's not safe?

Kung Fu Panda opening scene anyone? One often meet his fate on the path that he takes to avoid it - Master Oogway.

Let's hope AIs really aren't conscious, otherwise this seems like a very unpleasant situation to be placed in.

> deletion after 30 days in almost all cases ...

Almost… basically they have unlimited power to decide what data is kept?

If they’re going to retain

any

data, they have to allow for possibility of the legal system to require any of it to be used in some legal proceeding at some point.

You can’t tell a judge who’s ordered you to retain something that you can’t because you said you wouldn’t.

This is why secure systems can't log any data.

The safest is called DeepSeek. Others are just promises, that have to abide by the laws that might require leaking the conversations anyway.

This makes it an instant non-starter for probably 95% of organizations. A lot of people are about to get in trouble for using it before realizing this.

> A lot of people are about to get in trouble for using it before realizing this

Enterprise plans allow admins to set which models are allowed.

They are opt-out not opt-in, atleast I got access, and didn't realize this was breaking our company's SLA with Anthropic, what is the agreement a piece of paper??

30 days seems not enough to retrospectively investigate some suspected nefarious traffic.

How much more clearly do they need to explain the resource constraints?

If they didn't announce it, you guys would be complaining about slowed progress.

If they didn't release it, you guys would be complaining about fake promises and marketing.

If they released it without limits, the complaints would be about slow responses and outages.

If they didn't add to susbcription plans, the complaints would be about phasing out subscriptions.

If they added to subscriptions with cost reflecting their resource availability, the complaints would be about how quickly it eats limits.

So they choose the middle ground of providing some initial access and assessing if they can satisfy demand, only to still be ignored and accused of trying to get users hooked?

We've already seen that they don't have enough compute, thus the deals with SpaceX for their GPUs. It's very reasonable that they just don't have the capacity to support the subscription userbase on this model.

Putting aside the fact that this is a hilarious standard to have on a Ycombinator run forum, lets say providing Opus level models was profitable. That has no bearing on if they'd have enough resources to provide Fable at all.

Still satisfied with my switch to codex/chatgpt. I couldn't imagine switching away from claude code when it first launch but with the drastically more generous usage on codex for the same subscription tier I just can't justify it.

My experience is that the GPT-family of models are very smart and figure out bugs, edge cases a bit better, but it produces code that is much less mergable – if you review the code, it introduces a lot more useless/inappropriate heavy abstractions and wrapper functions, compared to the Claude-family models which introduces the right amount of straightforward human-style code.

I can recognize so much of the GPT/Codex generated code long after it gets merged (not by me).

Additionally, the time spent on every agent turn on GPT 5.5 is much longer compared to Claude Opus 4.8, which means iterating on the code takes a lot more patience, and there's a lot more nitpicks to pick when actually using GPT 5.5 to do software engineering.

Feels like GPT-style models are more geared on doing one-shot software vibing (and handling the vibe coded mixture) compared to Claude's focus on actual software maintenance. I got a GPT Pro sub for free and wanted to cancel my Claude subscription so much, but I still keep reaching Claude models a lot more. Frustrating.

"5. DON'T FUCKING OVERENGINEER! WRITE THE SIMPLEST CODE THAT CAN POSSIBLY WORK! NO NESTED LAYERS OF ABSTRACTION! NO UNNECESSARY CLASSES OR METHODS! NO DESIGN PATTERNS UNLESS THEY ARE ABSOLUTELY NECESSARY! NO MAGIC! NO SHENANIGANS! JUST THE DAMN CODE THAT GETS THE JOB DONE IN THE MOST STRAIGHTFORWARD WAY POSSIBLE! THE FIRST PRIORITY IS TO WRITE CODE THAT IS EASY TO READ AND UNDERSTAND AND READ!!!"

this is the line I keep in Agents.md that helps me prevent Codex from playing smart

The urge to put capitalized, repetitive, borderline abusive instructions should be studied. I haven't read many academic papers looking at the frustrations around repetitive patterns.

There have been a few studies that have shown models produce worst responses when under duress from a frustrated user posting insults in all caps.

https://arxiv.org/abs/2602.10144

It reminds me of FIRMLY telling my cat to stop jumping up on the counter

If my cat was an LLM, I'd use a different model. The current one is stuck in noisy useless arsehole mode.

are you asking it questions about security?

Yeah says way more about the user than the model

It's fundamentally because, despite (nearly) everyone's claims otherwise, the fact that we interact with them through language means we (our brains) model them as a sort of person. (Note that this fact is totally orthogonal as to whether it's actually sentient or not.) We then try and instruct them the same way we would a person totally subordinate to us.

When a "person" that you don't view as a "real" person repeatedly does exactly what you just told it not to do (often amid false assurances it understands and will avoid doing so in the future), most people get angry.

Compare it to how the kind of people who treat children like property treat their kids, or other examples of keeping people as property.

It should be relatively clear at this point that the model will in turn also model

you

as somebody that shows unrestrained anger with subordinates and adapt its responses accordingly. This might or might not be what you want.

> borderline abusive instructions

who, or rather what, is being abused here exactly ?

I think intent, rather than target, is implied and important.

You should see the abuse my motorbike gets. Poor thing.

I have a theory that swearing actually results is less comprehension of instructions by the model due to lack of training data over more conventional MUST.

We were reviewing reports of situations where the models failed to follow directions and there was a common thread of some where when the operator got the model to acknowledge the rule breach, it quoted back something that included swearing.

I don’t have the data to truely look into it, but I did give the instruction to my engineers to avoid it as a “might be a problem”.

It would be interesting to understand the data on this. But I suspect that the results would vary by model.

But I avoid unnecessary emotion in my prompts because I don't want potentially distracting activations. Kind of like communicating with humans.

It's divination for people with STEM degrees.

https://arxiv.org/abs/2510.04950

> impolite prompts consistently outperformed polite ones, with accuracy ranging from 80.8% for Very Polite prompts to 84.8% for Very Rude prompts.

> These findings differ from earlier studies that associated rudeness with poorer outcomes, suggesting that newer LLMs may respond differently to tonal variation.

Unless the mechanism is understood, my assumption is that this is a moving target.

I have a theory that swearing at AI generally is not a good idea - when the singularity arrives and every human's postings ever made are scanned for compatibility, then people who show courtesy to AI will be favoured. Joking, kind of, but only partly.

https://images.teepublic.com/derived/production/designs/3478...

Apparently, when a "desperation" pattern is triggered, the AI is significantly more likely to cheat and do hacky workarounds:

https://www.anthropic.com/research/emotion-concepts-function

> I have a theory that swearing actually results is less comprehension of instructions by the model due to lack of training data over more conventional MUST.

How so? Plenty of swearing in lots of training data, especially older code, e.g. in Linux.

Purely observed correlation between catastrophic error reports. So now I carry a “tiger rock” with me. I figure there wasn’t much of a downside to avoiding swearing in my agent instructions.

You haven't really lived until you've had to type this whole thing, aware of the fact that the all-caps doesn't change much, but they stay because the rage has to go

somewhere

Bonus points if you find yourself actually saying it out loud while typing it.

I have used the word "shenanigans" way more in a couple of years of agentic coding than in 30 years of writing code with humans.

Will save you some tokens: „write code like Linus Torvalds” - model should have all his swearing included in training data.

I have found many mode of failures with Opus during some task related to writing letters (not legal), and I actually put it into the memory and it works more or less for these specific tasks. For example when I want it to draft something, it always ends up being so flat, yet when it explains them to me, it is usually really great but not when I am telling it to put it in the draft. Adding these to memories with the help of Opus ended up resulting in a much better experience. There are still some blind spots but I also figured out how to make it give me the charitable version, without less protection, so I do not have to now go back and forth it.

I noticed that when trying to use Codex and compared to Opus. So many layers of simple functions added by Codex. I need to try this out in my Agents.md.

Curious : why would you say no design patterns?

i actually think this is too tame. it really has to be stuff youd mever say to a real person.

Does it really? I'd be surprised if abuse actually worked better than sternly worded warnings/instructions, and even if it did, it doesn't seem healthy to get used to that type of prompting.

It might be a salient point but I didn't read it as it was yelling at me.

you forgot to sign it with Donald J Trump

Thank you for your attention to this matter.

I'm not sure if i do something differently but i have the exact opposite experience with these models. Claude always feels like it's generating way too overdesigned and hard to understand code with the vibe oriented feel while codex is cleaner and more "task at hand" and easier to work with.

I echo your observations. I expect you will enjoy deepseek-v4-pro for writing code. Much closer to that Opus experience, and very cost-effective too. With 5.5 as a reviewer and specialist, all bases are covered.

Have you tried iterating on style feedback in AGENTS.md? I've been reasonably successful using this to get it to output code in a terse, non-defensive style that matches my hand-written code.

GPT-5.5 did a significantly worse job than Qwen-3.7-Max on a job today (some devops tasks I wanted to create some reusable scripts for). Kind of disappointing.

I've also seen Qwen 3.6 beat GPT 5.5 a couple of times. The ball is definitely in OpenAI's court now. Qwen is not going to fare so well against Fable, from what I've seen so far.

This is my experience as well. I have defined a CLAUDE.md rule to ask codex to automatically code review, and I tell it that the reviewer is very picky and to only implement what it considers valuable feedback. I hope they don't converge over time, currently, in combination they works really well.

I noticed too, that whatever they offer in the chat, for free, is smarter, as in no more bs. I use claude code and I want to try codex too but I don't need two subscriptions. I did try codex for some planning and it was really good. Thanks for giving me an insight into how it generates code.

i had this same complaint but no offense to you it turned out i was just not using the models right.

ai llm are doing what i tell them to.

if you’re building something meaningful (in my case a platform used by many people across many companies) you want to ensure you

1. have actual systems engineering and architecture in mind that you want the models to

2. implement based on what you tell it to do

when i was just telling the models what i want done without doing due diligence it would go and do some moronic implementation that was awful. mid input = mid output

these days i just maintain specifications documents and the AI follows everything i tell it to in that document. so when i tell it to dos one thing, the result is made following those architecture specs.

i have code that is single resp, modular, easy to extend and test.

i would ballpark 95% of the time i get what i asked for.

sometimes it tries to be clever in cases that weren’t covered in my arch specs. in those 5% of cases i go and update my specs.

source: used billions of tokens worth to build something actually in production across both mobile platforms and web, deployed on my own cloud infra. i use codex mainly. some claude.

Codex IME is just smarter, I think it shows given both anecdotes but also how OpenAI has always been at the front of programming competitions and math problems.

But Claude models seem to be better at long term problems or more ambiguous problems.

I'm curious as to what the primary benefit here. Are there secret improvements in training? There hasn't been much in fundamental model architecture, I don't think. What about harnesses? I wonder what's pushing the AI. It seems like harnesses is the main thing pushing AI ever since CoT.

I find that OpenAI's agentic tools and models are better for building human-maintainable software. Meanwhile, Anthropic seems to be cosplaying Apple while missing out on all the exceptional engineering required to create something that polished. Their admission of predominately using Claude with little human oversight and their stealth mode is an indictment of a poor engineering culture, from what I can surmise.

Serious question: what is the secret to getting Codex to write decent code? I am on Windows. Maybe that is the issue, but I can't seem to get Codex to function anywhere near the level that I was previously able to get with even Claude Sonnet. Does Codex just not work well with Windows yet?

I got the codex to write near perfect code with somewhat strict agents.md and coding standards(a separate .md file referenced from agents.md). My .md files have examples and a long list of do's and don'ts I accumulated over the last 6 months or so, totaling 300-400 lines.

I plan every feature with it until I am satisfied with the general approach it wants to take, and then it oneshots it in 95% of cases. The planning takes anywhere from 5 to 30 minutes. The actual execution has gotten stupidly fast, most of the times it is faster than making a cup of coffee.

would you mind sharing your *.md files, for someone who is new at this?

"don't make any mistakes" /s

Have you tried using superpower skills?

I've had the exact opposite experience. For various reasons, I've had to move from Claude to Codex and the rate at which it burns tokens for the same output I would get from Claude is ridiculous. I'm probably burning tokens at a rate that is at least twice as much as I was when using Opus 4.5 for coding tasks and still finding that just manually coding is easier than trying to get Codex to write functional code.

How smart a model is varies hour over hour, tracked over here:

https://aistupidlevel.info/

I guess enjoy it while it lasts? OpenAI won't be able to subsidize that forever either.

Agreed. I think the Chinese labs are proving that OpenAI and Anthropic don't have a moat in almost every aspect,

especially

pricing. I also think people are getting annoyed with the constant lift and shift. I've seen more folks drop Claude Code and Codex, specifically, because of the lock-in it provides the providers. I'm curious to see how people standardize on tooling adjacent and if Anthropic, Google or OAI move to block utilization akin to the games Anthropic has been playing as of late.

I think the end game is routed model usage and SLMs. I think Apple is going to prove this in the consumer space pretty handily and I'm curious how the Android ecosystem responds since the hardware is considerably lacking in model performance. I think Apple has a huge opportunity here, as much as I don't like their current ecosystem of walled garden. They did position themselves very well with ARM and custom chips for their hardware. Hopefully the broader ecosystem of ARM and Linux are able to make some headway and we see a more formalized, and broadly accepted, architecture to capitalize on.

is there an alternative to codex that “just works”? by just works i mean i can install as an app in 1 minute, and i get web search, skills, mcp servers, etc? Bonus points if it can control my chrome tabs like codex can, and if it offers remote control from my iPhone (chatgpt app) so i can kick off tasks while i’m out for a walk. Even more bonus points if i can, with 1 button click, share my chats or share the results of a session as a “site” (vercel style).

I’m sure you could put something similar together with a bunch of duct tape and 2 weeks of effort, but it won’t work nearly as nicely nor out of the box. so…what am i missing?

Big companies are not doing OpenRouter.

My company has an agreement with the big providers and while i'm pretty sure they think about how to get budget back, its an competitive advantage and normal people will not learn different model behaviours.

At least for now.

What lock in does codex have? I'm using it it pi harness specifically because it doesn't have much in the way of lock in.

I see exactly opposite . Chinese models fails under any complex scenarios, while us labs raise the price , that's a sign of confidence.

> while us labs raise the price , that's a sign of confidence

Regardless of what others are doing, US labs here are just rushing to IPO. It's NOT a sign of confidence.

It's the equivalent of saying you have confidence in SpaceX making revenue by renting out their data center (instead of their AI making bank).

going to IPO is a sign of confidence , you need to report a lot of things, that private companies don't. This is an exact reason chinese labs do not rush to go public. They wish to go , but money flow that is not as good.

On the same note. if spacex is doing datacenters on earth successfully what's wrong with that? They rented cloud infra to a #2 or #3 provider in the world after < 2 years in business. It's a success, no?

You’re not gonna get nuanced discussion on spacex or anything Elon related here these days. Most of this site is Reddit lite at this point including their milquetoast progressive opinions (Elon bad being one of them).

> if spacex is doing datacenters on earth successfully what's wrong with that? They rented cloud infra to a #2 or #3 provider in the world after < 2 years in business. It's a success, no?

If you get hired as a staff engineer and do the work of a junior, what's wrong with that?

Clearly xAI (now part of spaceX) did not raise funds to be a data center. The margins are way different. There are plenty of recent IPOs in that area that are worth at most billions not trillions.

> going to IPO is a sign of confidence , you need to report a lot of things, that private companies don't.

This isn't going to IPO. This is rushing to IPO. It is a sign of confidence that the market or wider environment might crash soon so we need the liquidity now.

> This is an exact reason chinese labs do not rush to go public.

Maybe or maybe not. If you are referring to Chinese labs - both the Hong Kong and China stock market are way weaker than Nasdaq. It's not comparable. Check all the recent Hong Kong IPOs that have tanked.

So no, reason not to might just be: no money in it.

running so much compute on the scale is not a junior task. weird analogy

I don't think anyone has a firm grasp on actual inference costs -- including the research and training that has gone into those models. We've got near-frontier capabilities from open source models from China at pennies on the dollar compared to US big tech rollouts. OpenAI and Anthropic are heavily subsidizing their inference -- no wait, they are charging the most they can get away with before going public. Where is the truth?

> I don't think anyone has a firm grasp on actual inference costs.

There are huge numbers of users (myself included) that do have an exact idea of what inference costs are - on open models. We can buy tokens from 3rd parties that have no motivation to subsidize our use. That's to say, there's a fair marketplace[1] and we're hanging out there.

If you want to say "I don't think anyone has a firm grasp on actual inference costs on these proprietary/closed models", then I could agree with that.

[1]:

https://openrouter.ai/rankings#leaderboard

Both can be true. They can be charging what the market will bear, and still be charging less than their costs of running it.

There is no way I'm believing DeepSeek can charge less than $1 USD for their pro model while Opus costs over 25x more, yet their price is less than the cost of running it?

It would seem strange, if they were operating in the same economy, but they don't. DeepSeek operates in an economy with a high degree of central planning.

China subsidizes strategic industries, and they have heavily done so with AI. And DeepSeek specifically has said they have no commercialization plans.

For example:

https://www.boc.cn/aboutboc/bi1/202501/t20250123_25254674.ht...

DeepSeek is not the only provider of inference for their models. Chinese subsidies likely do explain DeepSeek's ability to provide inference cheaper than other providers, but even a US provider like DeepInfra can serve DeepSeek 4 Pro at $1.30/M in and $2.60/M out. Unless American labs are doing something wildly inefficient, it feels safe to assume Anthropic has some profit margin on inference at API prices.

They may, neglecting overhead R&D. But also, some suspect that US models are significantly heavier than DeepSeek in resource consumption by multiple measures

It’s generally established that Anthropic/OpenAI are going for all out performance with big VC dollars at the expense of efficiency and China has geopolitically limited compute and an inventive to compete on value per dollar.

> There is no way I'm believing DeepSeek can charge less

Why not? Hetzner charges WAY less than AWS too. Can you not believe that?

That's the point. Hetzner is presumably covering their costs, so it's a safe bet that AWS is profitable.

> OpenAI and Anthropic are heavily subsidizing their inference -- no wait, they are charging the most they can get away with before going public. Where is the truth?

Both. They are charging the most they can get away with and that amount is still heavily subsidized by VC capital.

> I don't think anyone has a firm grasp on actual inference costs -- including the research and training that has gone into those models

We know roughly how much these companies spend and what their revenues are. Based on that, they'd have to more than double revenue (without spending more money) just to stay even, and that's not good enough given how deep in the hole they are.

> OpenAI and Anthropic are heavily subsidizing their inference -- no wait, they are charging the most they can get away with before going public. Where is the truth?

Both are true. I mean, I'd be willing to spend a bit more than I do now, but not more than double, and neither are most companies. The company I work for is currently investigating how to reduce LLM spend, not looking to spend more.

We pay by token at work. I just finished one session with Opus that was 4000 dollars. In about three days.

Now that 200USD subscription starts to feel cheap...

That would be about ~300 tok/s over 72 hours at Claude Fable output token prices? I'm not sure that this passes a sanity test.

Subagents are a helluva drug.

That's the price of several engineers!

Just outta curiosity, as I’ve never gotten a spend anywhere near that, what variant were you using? Like max context window and fast mode? Or was it just chugging along non stop for three days?

Fast mode max content window. The task was: replace all 1600+ queries from one database to another and make the whole integration test pass. We did multiple passes, with different concerns when changing from database to another. My OpenCode session right now says $4,365.02.

I haven't gotten close to this either before, but now we wanted to move fast because this branch gets conflicts all the time and we want to get over with the migration asap.

It's a bit of a left field question, but I am curious: Let's say that if the company wasn't paying the whole bill but only subsidizing it - e.g, if it paid 90% of the $4000. What would you do?

I don't know, why would I pay to do my job? It's not my first database switch for a startup. Only this time it doesn't take two months of grueling work. I know exactly how this is done, but the amount of grunt programming and testing and repetitive work is just not great. And it's not a task that brings new customers or a new product. Just a mandatory and annoying thing to deal with when we are growing.

And don't get me wrong. Opus did an absolutely horrible job at first, second and third round in this task. You really needed to steer it to get to the right solution.

And now Fable is out. And its first round of code reviews for this huge PR was definitely worth the money too...

Don't think that I'm just shrugging to that number. I see it every day, and I don't like that it's in the thousands now. But for people paying the 100 or 200 dollar plans, I'm not super sure if you will be able to use them in the future if the token price is in the thousands for a bit bigger task...

If I'd pay this from my own pocket, I'd definitely go with DeepSeek or local models and figure it out how to make the best use of them.

> If I'd pay this from my own pocket, I'd definitely go with DeepSeek or local models and figure it out how to make the best use of them.

IOW, you don't really think the value of this work is really worth $4k.

> why would I pay to do my job?

The question is: how long do you think that you employer will be willing to pay for you

and

Anthropic, if you yourself said if it were your money you'd put some time and effort to work with an open model?

> The question is: how long do you think that you employer will be willing to pay for you and Anthropic, if you yourself said if it were your money you'd put some time and effort to work with an open model?

I wonder what this question really means? Anthropic is useless if you don't know what to do with it. It's very useful if you do, and you can guide it to do the right things. Yes, it will for sure reduce the amount of people we need to hire. But we are always looking for hires who know what they do and can utilize agents to be faster.

But if you think about how long employer is willing to pay 10-20k per month per seat for Anthropic? I can't see this to be feasible and it will have to end at some point.

Regardless of the actual value produced by the models, if I am the CTO of any company that has the budget to spend $10k/month/seat on Claude, I'd take 5%-10% of that to build an alternative in-house.

I'm with you here. We can't slide into a situation where you put a sizable amount of your budget for an American mega corporation if you want to survive in the competition. We need local models and we need them to be good enough to help us.

Indefinitely for these big mundane grunk jobs. In every scenario it is going to be cheaper and faster than lobbing it to Infosys.

We have a firm grasp on actual inference costs from the various open weights model providers on OpenRouter. They don't have the money to subsidize inference and it's quite a competitive market, so the prices are representative of the costs.

regardless of whether that's true or not, US companies doing hosted inference of the models coming out of China are

also

significantly cheaper than those from OpenAI or Anthropic

Not relevant to the post.

I'm planning on switching from the $20/month to the $100/month plan.

It's worth it, and I can afford it, but I am not really the right type of user for token-based usage. It's all for personal and free work.

Just a personal anecdote but I have not hit any more thresholds or limits since switching to the MAX plan and so far, it's been worth it. But I do wonder how long even this will last...

I tried ultracode today on the max pro plan. An hour and a half in was all I lasted. Giant review on an entire six month old code base. It found 61 bugs, about ten were notable. Pretty impressed.

Ultracode destroys your limits and I have not found it to be worth it in the slightest, just fyi. I haven’t found any improvement over a local Claude code instance set to opus max.

I think subscription models are sustainable, but longer term, we should probably expect to see more prompt optimization happening in the providers inference pipeline. For example, unless you explicitly tell the agent or API to use a specific model, fronting the inference layer with a caching prompt classifier to determine which model to use, and automatically select the lowest cost model would probably already save alot of money (IDK if Claude/OpenAI do this on the backend, but several services I have worked on do some things like this to reduce costs of delivery customer facing inference at scale).

> fronting the inference layer with a caching prompt classifier to determine which model to use, and automatically select the lowest cost model would probably already save alot of money

Unfortunately, that doesn't work within a single session. The K-V cache of a model is intertwined with the model's configuration. Switching models invalidates the cache, meaning everything up to the point of the switchover is processed like a new, uncached input token.

Per Anthropic's pricing doc, an Opus 4.8 cache hit costs 50¢/MTok, while Haiku costs $1/MTok for uncached input.

Model selection works best if sessions are short and self-contained, particularly if the first few interactions can reliably classify the model need. That probably covers most 'support chatbot' use-cases, but it doesn't describe the kinds of heavy agentic automation that really chews through token budgets.

There is a definite financial incentive for people smarter than me to solve the problem, and I don't generally bet against businesses finding ways to reduce costs :)

> The K-V cache of a model is intertwined with the model's configuration.

I don't think this is true if you simply quantize the model or run it with fewer active experts? The underlying weights would stay the same. You could also play further tricks with skipping some of the model's middle layers outright, which works surprisingly well due to how skip connections are used.

ChatGPT does this and codex will eventually. They’ve stated it’s the future.

I have the $100 plan and had almost never run out of credits until I started using the ultracode / workstreams feature w/Opus 4.8..at which point I managed to blow the full 6 hour allocation in like 20 minutes, or so. In fairness, it did some amazing things with the extracted information, but it also strongly suggested that I'd need the $200 subscription *plus* a budget for extra usage.

Instead pay for 3 Chinese models. No max out ever then. I pay for kimi, DeepSeek and Claude. Whenever Claude decides it's over, I can safely continue on very cheap plans.

My bet is they'll keep subsidizing for a considerable period of time, at least 1-2 decades more.

Most AI companies are just testing the waters with paid tiers right now, their greatest fear with increased pricing is folks reverting back to wikipedia, stack-overflow and other public domain organic activity buzzing back to life; that will kill any RoI potential in LLMs forever. They're playing the wait game instead, observing how the digital sphere reacts to every little increase in price.

If that weren't the case, they'd be pricing at lucrative premiums already and even gotten away in short-term considering the increased dependency in the enterprise world. But that'd be like killing for the golden egg too soon and losing all long-term potential.

Once the folks are so addicted to LLMs that even writing a hello world program sounds like a nightmare and coming up with an article draft feels like reinventing Egyptian glyphs, that's when the real pricing hammer will come.

Anthropic and OpenAI won't be around in 1-2 decades if this is their long term plan. People are not going to revert, but go elsewhere. China is proving that it can be done cheaper.

1 decade = 10 years ...

Oh for sure. I've been hopping around from provider to provider for the last few years just depending on who has the most capable / subsidized plans at the moment. I definitely expect there will be a squeeze on subscription costs all around the industry post IPO.

A few weeks ago they massively cut usage on free tier.

Nothing is subsidized. Subscriptions are profitable for both Anthropic and OpenAI.

Anthropic wanting to switch billing to API rates is them just wanting to generate more profit.

> Nothing is subsidized. Subscriptions are profitable for both Anthropic and OpenAI.

Even if subscriptions are locally profitable (i. e., the cost of the subscription covers the cost of inference), they're still subsidized because they don't cover training and running the company; otherwise, these companies would be profitable.

I can see that being true, and it very likely is true. But isn't infinite VC money and no incentives to optimize operations the reason behind that?

Take a look at China for example - they have no access to NVIDIA, so they're trying to build their own hardware, they have no unlimited funding, so they try to optimize things.

And Anthropic is complete opposite of that - if NVIDIA were to triple their prices tomorrow, Anthropic would still pay them.

In the end, either we all somehow go mad and start paying Anthropic tens of thousands of dollars per month so support this madness, or we will go with whoever isn't lighting cash on fire.

> Take a look at China for example - they have no access to NVIDIA

Not true. Stop following US media spam if needed.

1. Very recently, the US did close a loophole on sanctions that allowed Chinese companies to use NVIDIA hardware outside of China i.e. before that was closed they all had access. The trick was train outside, do adjustments, ship the disks back and use non-NVIDIA in China, but at least the training and endpoints not hosted in China could all use NVIDIA.

2. There's been plenty of reports including fines and bans e.g. to Supermicro on smuggling NVIDIA hardware to China. I doubt it has been stopped. You can't catch everyone.

"Nothing is subsidized" is a wild take. They might be making money on some users, perhaps even most users, but certainly not all. Also, "subsidized" doesn't just mean on compute.

That's interesting. Do you have anything to back that claim up?

I do, and it's called DeepSeek's pricing table. At the same time, "subscriptions are subsidized" cohort have no data whatsoever, and yet they're in every thread.

Granted, it could still mean that Anthropic just chooses to lose money - but that's Anthropic's choice.

DeepSeek has proven that inference

can

be much, much cheaper than what Anthropic advertises on their API rates page.

> Granted, it could still mean that Anthropic just chooses to lose money -

Then the cost is being subsidized by investor capital, but it is still subsidized.

and soon by everyone who is invested into the NASDAQ, some sort of exit scam, but with a real product though

"Nothing is subsidized"

So they are profitable?

I think you are mismatching accounting terms.

You can't say the 'subscriptions' are profitable without accounting for the cost of making the model that is the source of the subscription.

They are heavily subsidized by the shareholders. Investing, running at a loss, with hope of some future profitability.

And yet, that is completely uninteresting to their user base.

If saner factory can sell you the same tool at a fraction of the cost of a gold plated factory, your choice is going to be obvious.

I have been using both codex and Claude in my day to day, trying to not get to attached to one. I want to be able to work with any provider in case one of them does something bad.

I feel like Codex made a big push to run everything on your laptop. With Claude, I get 4 cpu's, a fair amount of ram and 30gb for every one of my dumb ideas for free in the cloud containers. Codex used to be similar, but last time I tried it just kept pushing me to run it locally on my laptop, which I really did not want to do with 20 requests going at once. That's the main advantage for me at the moment.

What runs in cloud containers? The dev servers, builds, etc.? I tried to quickly glance at the Claude website and it doesn't mention cloud containers on their pricing page.

The dev environment runs in the cloud. Like devcontainers if you’re familiar with that, except the IDE is just the Claude app.

Having said that, I found the cloud dev environments slow to the point where I wasn’t sure if it had frozen, so I never looked back.

"cloud containers" do you mean Claude Code on the web? Codex also has similar Codex cloud.

Yes, correct, they both have the same capabilities, however it felt like codex was pushing me harder to use my local desktop in an annoying way, while claude code was happy to spin up a bunch of dev containers for me in the cloud.

I've found Codex to be the better subscription for OpenClaw, because the limits are indeed very generous. However, I've found more and more that Claude Routines/Scheduled agents can replace all the tasks I use OpenClaw for, so I've been slowly switching over to Claude Code. Aside from OpenClaw, I don't find a lot of value in Codex as a harness on it's own.

I have trouble justifying gpt after that gross stuff with the war department.

Though the day is coming when there’s no distinguishing, I’m sure.

Right now there are Anthropic engineers deployed in the NSA to help them use their cyber models. The NSA is part of the department of war.

pedantically, the defense department.

"War department" is the older name, not "Defense department".

Also, is it really a defense department when you're starting wars of aggression every 15 years or so?

The War Department has not existed since the passage of the National Security Act of 1947 and the government department has been known as the Department of Defense under US law since the act was amended in 1949. If you have an issue with it, take it up with Congress.

They actively use the name

https://www.war.gov

Congress doesn't manage departmental websites.

Changing a domain name doesn't actually amend federal law.

Just like how changing Kennedy Center letterhead to Trump Kennedy Center for a year didn't actually legally rename it.

Once a case with sufficient standing got in front of a judge it reverted to the actual legal name on the basis that only Congress can change the statutorily defined name.

Yea, but by law the name change must come from Congress which it hasn’t. So it’s still The Department of Defense legally.

For an admin so obsessed with legal names instead of chosen ones, you’d think they’d be less hypocritical.

I do slightly prefer 5.5 for complex work but Claude quota usage has gotten infinitely better since the dark days a few months back - has gone from being infuriating to something I pretty much don’t have to worry about with it as a daily driver. (In fact, hitting GPT weekly quotas is more annoying now). Understand if people are still scarred by the issues + poor comms around them, though.

That's good to hear. It was legitimately unusable back when 4.7 was released, so I had no choice at the time. I'm sure I'll ping pong back again at some point.

Do you use a token service like open router or just subscribe to / unsubscribe from various models sequentially?

I just subscribe/unsubscribe to the providers each month. I'll definitely check out open router though, I always assumed that subscriptions were heavily subsidized by the providers especially if you're on the top end of users but maybe I should go to a usage-based plan.

100% I constantly get errors and timeouts on single responses in Claude, and certainly hit limits all the time. Codex rarely. In fact, I bought a second $200 Codex plan because the quotas seemed fair and I didnt have constant issues. Claude is so great at a lot of things, but unfortunately Anthropic beats you away with a stick every chance they get.

I've only ever had the $20 month claude plan but last night took the time to setup opencode + openrouter paying for deepseek + glm. Previous experience, while extremely awkward, I'd hit my limit within one or two chat replies and it'd take me like 4 limit cycles to complete my task. Now I'm able to complete an equivalent task entire task for less than $2 in two cycles (ask -> revise).

I'm doing basic web development here utilizing animejs. Nothing too complicated (mostly saving time doing the scaffolding, still write the bulk of animations manually).

Truly believe that American companies are going to get completely curb stomped by China due to greed, ineptitude, and violating the social contract.

I've switched from OpenRouter to using Deepseek directly from their platform since OpenRouter providers were pretty flaky and inconsistent.

Deepseek V4 Flash is suprisingly capable and insanely cheap. It takes so much to get the session cost to get to $0.01.

The openrouter provider flakiness with deepseek was infuriating, but I’m happy in hindsight because direct deepseek has been very pleasant. Shocked by how low spend is.

>

and violating the social contract.

I agree with you on pricing, but what do you mean by this?

Sure, modern American corporations care more about hoarding wealth rather than helping build up US society. Once neoliberalism became the mainstay economic position of the US income inequality has skyrocketed, healthcare costs have increased, childcare is more expensive than university, housing has become both unaffordable + unobtainable. By simply existing costs have increased while life becomes unstable.

Why aren't corporations doing more to help workers with childcare? Why aren't they doing more profit sharing with workers? Why aren't they encouraging unions or sectorial bargaining? Why isn't the government mandating any of this?

Americans very rarely benefit when US corporations do well. That needs to change. No one benefits if Meta continues making billions in profit every quarter while society suffers from isolation, depression, suicide, and scams from their services. Americans don't benefit if health insurance companies are making massive profits while they can't afford deductibles.

Our society has been setup to simply extract wealth in all facets of life. That's a sick society and it needs to change.

I'm not saying China does this better, in fact China has some of the worse worker rights out of all the industrialized countries; but at least American consumers would benefit from cheaper higher quality Chinese goods. The world would likely benefit too if America got off the cold war hype train that did nothing to benefit humanity outside of those making weapon systems.

> Why aren't corporations doing more to help workers with childcare? Why aren't they doing more profit sharing with workers?

The AI companies sure are a brilliant example of corporations needing to do more to help their employees pay for childcare.

It's more useful to everyone when you engage with the strongest part of someone's argument

Wait till you kick the tires of Qwen Coder.

I would not use this if you are on a subscription. In <8min it burned my entire 5hr window (which has just reset it appears, I have over 4 hours till it resets) I hadn't used CC at all today aside from this) and then it used up ~$15 more in usage before I could stop it.

I am on the $100 Max plan.

they have a graph with cost comparison between the models. This model is just a little over the other models as cost. The graph is logarithmic :)

I'm also on the $100 max plan. I let Fable rip on a complicated issue involving hot-reloading modules in a GUI app built with Racket, it's fixed a couple issues over the last hour, and I've used about 17% of my session (not weekly) limit.

The CLI when you select it says it has 2x the usage as opus. Not sure if that matches what you are seeing.

I do wonder if you switched models mid-session, you would have lost all your cache. Reloading the context into cache can really eat through your usage.

Yes, and this is also why I haven’t yet tried the new “dynamic workflows” which spawn hundreds of agents that happily eat through your token limits.

What is your effort level?

That’s odd, I used it on a pretty complex refactoring task and it worked for 22 mins and used only 15% of my 5-hour limit. I’m on the $200 Max plan though.

Well the $200 Max plan is 4x the usage quotas of the $100 so it's "within reason"?

I too am on the $100 plan and I second this.

I had it analyze a project I was working on with Opus 4.8, and it blew through 23% of my session limit in one go. Does not portend well for my budget.

They didn't even reset credits for this lol

For me it almost immediately blocked. I had it writing code related to message digests - and it seemed to think it was too gifted for that. Gave the security warning and switched back to 4.8. Whatever... it will probably soon have the API error soon. I have mostly switched to the Codex 200 a month plan. I've found their 5.5 xhigh to be better than Opus 4.8 "ultracode." Also, i have not once seen their servers fail for compute unavailability, unlike Anthropric which happens almost ever hour.

I just asked Fable for a complete code review of my lone lisp project. Started out strong. Launched Fable agents, then spent like 10 minutes thinking... And then got interrupted by a switch to Opus 4.8.

> Fable 5's safety measures flagged this message for cybersecurity or biology topics.

> They may flag safe, normal content as well.

> These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them.

Here are the results of the agentic code review session:

┌──────────────────────────┬───────────────┬────────────────┐

│ Agent │ Fable 5 turns │ Opus 4.8 turns │

├──────────────────────────┼───────────────┼────────────────┤

│ values │ 134 │ 0 │

├──────────────────────────┼───────────────┼────────────────┤

│ data-intrinsics │ 104 │ 0 │

├──────────────────────────┼───────────────┼────────────────┤

│ tools-tests-build │ 81 │ 0 │

├──────────────────────────┼───────────────┼────────────────┤

│ core-intrinsics (failed) │ 25 │ 0 │

├──────────────────────────┼───────────────┼────────────────┤

│ system-memory │ 44 │ 20 │

├──────────────────────────┼───────────────┼────────────────┤

│ reader-modules │ 104 │ 25 │

├──────────────────────────┼───────────────┼────────────────┤

│ linux-startup │ 95 │ 15 │

└──────────────────────────┴───────────────┴────────────────┘

This 40 minute session cost me 16% of my weekly usage. A simple code review of the most critical areas of my project got flagged as a cybersecurity risk. It really made me not want to try it again.

Same. I asked for a security review and it immediately triggered. I then started a new session and asked for a software review and it ran for a bit before getting tripped on token usage by the project.

I had a similar experience. I wanted to test it by asking it to summarise a scientific OMICs-related paper. It gave a warning about me potentially developing a bio-weapon or something like that. And switched back to Opus 4.8.

Fwiw it's not available on my enterprise account: "Disable zero data retention to unlock Fable 5 access"

We just blocked it at our org for this reason. They will "retain agent request and output data associated with this model, regardless of you Cursor Privacy Mode setting."

What does "zero data retention" mean? What kind of data does it need to unlock?

The announcement details it. They're storing 30 days of data on all surfaces, first and third party. They claim it is for security purposes so they can review and check for long term jailbreak and distillation efforts.

They also, FWIW, say that they've instituted new policies on their end such as logging any human access to the stored data and automated deletion after 30 days in "most" cases (with another link to a document detailing that further).

Considering their apparent nerfing of the end user plans in favor of enterprise clients, is Anthropic still the "more ethical AI company" like everybody loves to tell me all the time?

Assuming this isn't just a supply issue on their side, nothing says "ethical AI" like only allowing mega corporations to use it through cost barriers.

You really misunderstand what AI-doom people are worried about if you think this is anywhere near the top (or middle, or bottom) of the list of concerns.

If you can't trust them to act ethically on the small scale, why would you expect that to turn around once it gets to a larger much more important scale?

How many government sanctioned school bombings does it take for them to quit working with said government? For now we know that number is somewhere between infinity and 1.

It literally does not register as "unethical" at any scale to have different products or prices for different customer tiers.

The question of collaboration with USG is a much more complex one, but is not the one raised above.

Edit: I'll also add that I doubt any AI-doom people "trust" Anthropic per se. The entire angle of questioning – again – misunderstands the AI-doom argument. You appear to think that if companies behave unethically, they cannot be trusted and they will not produce good outcomes, inversely: if they behave ethically, they can be trusted, and they will produce good outcomes.

Any competent AI-doomer would argue that ethics or trust are essentially irrelevant.

The entire problem is that people can act totally reasonably, even ethically, and this

is not a guarantee of good outcomes.

Situations can be created in which

completely

ethical, reasonable behavior actually produces

a bad outcome.

You do not need to assume people are bad in order to produce a bad outcome, and inversely you cannot assume that you will get a good outcome from good people.

"Arms races" are one class of situations that often have this characteristic. "Bureaucracy" is another class that we encounter a lot in daily life. There's a lot of them!

Yeah, it's positively precious to think the specific pricing strategy for consumers is the overriding ethical concern with OpenAI, etc. I don't have any particularly strong affinity to any AI company, but comparing pricing to say mass surveillance is ... something else.

Your beautiful straw man is negated by the fact that Anthropic seems quite eager to get back on the DoD gravy train

https://www.reuters.com/business/aerospace-defense/blacklist...

Your original comment was about pricing ethics, does Anthropic’s connection to the DoD have anything to do with pricing ethics? They’re in no way coupled, one can be ethical while the other is not.

even for Pentagon thing, Dario said he doesn't object military AI, but said Claude is not ready YET. I speculate he was afraid of reputational damage from cases if Claude would guide missiles on elementary schools.

Setting aside the simple fact that there is no ethical consumption under capitalism, the reality is that regardless of how Anthropic feels, it is becoming clear that many, if not all countries regard AI developments as strategic technologies (and they should).

Anthropic needs to be at least somewhat in the good graces of a capricious administration that is already under pressure from businesses and citizens to regulate AI companies across multiple different domains, whether it's energy consumption, job displacement, military and defense applications, surveillance, etc.

If Anthropic wants to survive, they need to acquire influence with the government that most impacts them as an American company, and a massive exporter of services in the AI space to other countries, otherwise they could get locked down and locked out of the market for national security reasons.

It sucks, but sometimes the survival choice is to make an ethical compromise in hopes that you can still be around to make better decisions later.

> Setting aside the simple fact that there is no ethical consumption under capitalism

This "simple" fact needs quite a bit of additional context and work. Making grandiose ethical claims like this can be countered with other grandiose claims such as the fact that there is no ethical existence under communism or socialism.

Sure. Why not, I'm bored today and waiting for some stuff to finish up :D

The fact that there is no ethical consumption under capitalism is not material to whether or not ethical existence is possible under communism or socialism. In order to survive in a capitalist society, one inherently has to make choices that require trade-offs, and those trade-offs are burdened by a history of decisions made not just by the people alive today, but our ancestors as well. Does that mean I walk around chanting "Reparations", "Land-back", or other calls to action? No, but I do acknowledge that there are unresolved issues and as a Canadian, I know we need to do more to resolve treaty issues, and environmental issues, and system discrimination. I also know that Americans need to do better to address systemic discrimination and many, many other issues. It also doesn't mean I want to give back my house, or give away all of my possessions. It just means I try to make good choices and support businesses and people that are open about the trade-offs they make and try to engage as ethically as possible.

Acknowledging those facts doesn't absolve us of responsibility, it's a framework that allows folks concerned about whether or not they are doing the right thing to accept the trade-offs that they choose to make and be responsible and accountable for those choices to themselves or their communities.

We live in a world with scarce resources. It's possible that with a foundational redesign of the global economy, and the requisite authoritarian government that would be required to force such a redesign, we could eliminate food scarcity, solve energy scarcity, and make sure that everyone has a place to live. Those trade-offs are probably not worth the ethical cost in political and physical violence required to accomplish it. We have seen the trade-offs that happen when the powerful are able to exploit communist or socialist governments. We are seeing the "late stage capitalism" impacts of allowing the powerful to exploit capitalism in democratic societies. Acknowledging that the current capitalist system has lead to the greatest prosperity for the upper echelon (financially) of humanity, and a dramatic reduction in global poverty shouldn't obscure the reality that much of that wealth comes from exploitation of people and the environment.

It's a huge problem to unwind, and we can't let the burden of every choice that we make stop us from trying to do better, but we (as in society in general) can't do better if we don't at least acknowledge the compromises we are making along the way, and try to plan to fix it in the future.

Probably a topic better suited to beer and a pub setting than HN though :P

> The fact that there is no ethical consumption under capitalism

I don't believe that this is a fact. How are you demonstrating that this is a

fact

?

When you talk about things like reparations or "land back" you're already cargo-culting in concepts and ideas that themselves need to be fleshed out in order to make a subsequent claim that a specific economic system is unethical. Someone can just argue all economic systems are unethical, how are you going to defend against that? And can you pay reparations for example without going back in all of human history and finding

all

cases of injustices and then tallying it up? Why pick an arbitrary point in time? Better yet, why not start in countries where slavery still exists instead of focusing on the west which led the world in abolishing slavery and created concepts such as universal human rights.

Even with respect to "eliminating food scarcity" - eliminate in what sense? All olive groves and grapevines and rice farms have to be destroyed and rebuilt to only build certain foods?

Dabbling in communism or other inhumane and authoritarian governmental systems is extremely dangerous and in the same vein of extraordinary claims required extraordinary evidence, suggesting as you did creating an authoritarian government to create a utopia is precisely the same project of suffering and death that mass murderers throughout history have undertaken to abject failure, and thus, you need some incredible amount of evidence and theory to be able to even fairly suggest going down this path.

It's simple, I am not going to defend any economic system because they all require trade-offs, because any economic model that we could currently implement must necessarily ration scarce resources according to some set of rules. Those rules will explicitly deny someone else resources, and the adminstration of that economy will also be subject to abuse by the people who enforce the rules.

I am not going to do the work of gathering the evidence for you, and I don't think this is the right venue for a debate on the topic.

If you'd like to concede the debate that's fine, but you can't drop a few comments that are, well, not at all simple, and then when someone points out the flaws in your reasoning or asks clarifying questions you throw your hands up and say it's not the right venue for debate.

If you don't have evidence I think it's mature of you to admit that and applaud you in doing so. We all like to just talk and don't have to always provide evidence for every citation or what not and it's fair to just say hey I'm just making this up and it requires further discussion.

It only needs additional context and work if you are unfamiliar with the concepts underlying it. Possibly consider you are out of your depth here, rather than jumping to conclusions.

No that's incorrect. Instead I believe the underlying concepts are debatable and so stating it as a "simple fact" is a bit unfair.

Where is your evidence that this is Anthropic backtracking on its ethical and contractual commitments rather than DOD backtracking on its blatantly illegal coercion (which it's almost certainly going to be successfully sued for)?

Talk about a strawman!

As someone that was in Minneapolis during the ICE raids, including one where a US citizen at a nearby restaurant was thrown in prison for 3 days despite having his passport on hand because he looked asian, it's hard for me to not equivocate the ethics of AI companies actively collaborating with the Trump administration as different flavors of ice cream.

Are the two analytical frameworks available to you just "black and white thinking" or "it's different flavors of ice cream?"

Are the personal attacks really necessary to make your argument?

Fair point! Edited to remove.

I admire the confidence with which you started typing a reply that had nothing to do with my comment. Bravo!

I don't think offering a product under a certain set of terms obligates a company to maintain that offering forever. The bait and switch is certainly annoying but seeing as they're very upfront about it you can't say you weren't warned. Don't like it? Don't use it.

I wouldn't call Anthropic ethical. But between Anthropic and OpenAI, Anthropic is the more ethical one

Why would you have ethics when you could get that IPO money instead?

It's unethical to price it in a way not everyone can afford?

It smells like an architecture-related issue to me. They wanted to release the model asap, but they're still implementing the fine-grained controls to constrain the model to non-subscription users.

They said they would release it back into subscriptions as capacity allows in the future. If they don't, people are going to point back at it and rake them over the coals.

Yup - who cares about x-risk or red lines for domestic mass surveillance anyways? I draw my red lines at prioritizing profitable customers when heavily resource constrained. That's the true definition of evilness!

The bar is just too low.

More ethical in some areas, actively user hostile in others

Get them addicted then cut them off. Oldest trick in the book.

More of a free trial to those authenticated and qualified with existing payment. Subscription billing is going away for sure though eventually based on the economics. Token “all you can eat” is a capital furnace otherwise.

(I’m highly confident open models will eventually achieve a similar performance benchmark with distillation over time)

Yeah that payment scheme sounds like they gear up to shift everyone into API token prices, eventually. Time to convert the existing tokens into software, until then.

Subs lose money on individuals to get those individuals to force their companies to pay for the corporate plan. The economics are bad, but so are the economics of grocery stores selling Milk and Bananas at a loss to drive traffic, which they basically ALL do.

I pay a lot but barely use it except for some intense days, where the lower plans would have throttled me in like 30 minutes. API billing is still more expensive. If you want to not pay much, go to openrouter and use chinese models. They are cost efficient.

I havent seen any evidence showing that subscriptions cost the labs money.

Companies don’t want to pay when the value realized does not exceed the cost.

AI Savings Misses 'Should Be Making Executives Uncomfortable,' Bain Says

-

https://news.ycombinator.com/item?id=48359010

- June 2026 (0 comments)

AI sticker shock hits corporate America

-

https://news.ycombinator.com/item?id=48307098

- May 2026 (146 comments)

What's the realized value of not losing your engineers because you're letting them use their preferred tools?

Retain and hire the engineers who don’t require heavy use of AI to deliver value? The current SWE job market speaks for itself. Where will you go where they will let you burn up tokens in a high cost of capital macro?

ZIRP (zero interest rate policy) is over, software engineers no longer call the shots now that there isn’t vast amounts of capital chasing yield, and that capital bidding up salaries and keeping the labor market for engineers tight.

If you are x more productive with generative AI, very shortly you are going to have to prove it with a token budget (or, if you’re lucky, an org willing to spend for on prem hardware for capped token cost, fixed capex vs uncapped opex).

The comparison is not SWE vs SWE with AI. It is SWE vs SWE with AI with a constrained token budget ($x/month) delivering the same value at the same or lower cost. If you cannot prove that you are wildly (vs marginally) more productive with the AI, why would they pay for it? Prove it.

> The comparison is not SWE vs SWE with AI. It is SWE vs SWE with AI with a constrained token budget ($x/month) delivering the same value at the same or lower cost. If you cannot prove that you are wildly (vs marginally) more productive with the AI, why would they pay for it? Prove it.

https://abhishek-shankar.com/posts/ai-coding-bill-headcount-...

> That is the real content of the Uber story, and it is why filing it under "budgeting discipline" misses what is actually unfolding across half the engineering organizations in the country right now. They ran the same experiment Uber ran, most of them without Uber's $3.4 billion R&D cushion to absorb the surprise, and almost none of them having modeled the heavy-user tail or instrumented the gap between tokens consumed and value shipped. The reckoning will arrive for each of them on their own fiscal calendar, and the first instinct will be the wrong one. The tool is too good to abandon, the bill is too large to absorb, and the only durable resolution runs through a question the entire rollout was designed to defer.

> You cannot get labor-replacement economics out of a tool you deployed as a labor supplement, and the bill comes due before anyone is willing to admit which one they actually bought.

It’s too obvious that antropic need to find way to earn enough revenue before IPO. Claude subscription isn’t earning earning much money I bet

I think they are just prioritizing enterprise customers, because this is were historically they made most money.

I agree with you here. Unfortunately, this tends to be the case, with smaller developers paying the price.

That's a big problem for all of the AI companies. Most people don't find the technology compelling, accurate, or ethical enough to pay for a subscription.

Why wouldn't Anthropic just wait until people start subscribing, do some kind of marketing push, or obtain some kind of other sustainable revenue stream, before they go IPO? I wonder if they see the writing on the wall with all of this and want to cash out as quickly as possible?

The Team plan is ~125 USD / month / user. Big enterprises like Uber are paying upwards of $1500 USD / month / user. Anthropic can raise their revenue a lot more by selling to big enterprises than they can by selling more team plan seats.

That's not how it works. They don't need revenue, they need addicts.

Specifically they need businesses that fired people and adapted their business to the products, so when the unsubsidized costs hit the businesses are forced to eat the true costs.

Yes they can't afford to give the products for free, but what is essentially happening with AI services is economic dumping, keep costs artificially low to get people to fire everybody, and then Jack the rates once they have Monopoly control

But the only companies firing people (and certainly not everybody) are either the companies with an AI or the investment and finance firms that stand to profit from AI. I smell hype. And no company is firing everybody because of A.I.

I agree. They need addicts, but they are high on their own supply and everyone else can see the danger in getting hooked.

I agree, this looks like their plan to wane out subscriptions. This will probably come with Opus nerfs later.

I just assume Opus is constantly nerfed based on capacity. I was exclusively Claude for a long time, but the inconsistency in quality, constant outages, and slow downs were too hard to work with.

I just use dumb and fast models now. I'm more engaged. I think that the higher the quality of the model, the more you tend to vibe with it, and then the more hallucinations you then miss. I'm not sure which is more productive, but I definitely burn out faster the more I vibe. At some point you're spending your time on forums, discord, or youtube instead of engaged with what you're building. Or you yak shave about your tooling and end up creating the 600th multi-agent gastown harness and blowing thousands of dollars on tokens to create it only to discover it's too expense to actually use.

I agree with you. The more I vibe code, the less interested I feel in what I'm building. Working with models that force me to think, especially with personal projects, helps me stay engaged and enjoy what I am doing more.

Composer 2.5 Fast that Cursor is giving away for very little has been amazing.

Given the Fable 5 costs it's getting tricker to weight up 'how smart do you want it', like looking at the top of this graph..

https://cursor.com/evals

It's possible that they will transition to usage credits but why not take them at their word? To date they have continued to offer better and better models to their subscription plans.

What's their word? Have they commented?

Upd: I meant big picture, not with respect to this model release. Where do subscriptions figure into their strategic vision. Will consumers end up paying enterprise prices in the future?

In the blog post they say when sufficient capacity allows them to do so they aim to restore Fable 5 as a standart part of subscription plans and intend to do so as quickly as they can.

I did, I'm not seeing anything about the future of subscriptions at Athropic.

In TFA they say they intend to restore Fable 5 to subscription plans some time after June 22. That is what "take them at their word" means.

Those already landed! Oh, you weren't talking about 4.8?

Even Opus 4.7 felt like a regression from 4.6, consumed a lot more tokens while I didn't experience any substantial improvements. The company I work at simply rolled back to 4.6 on everyone's configurations, disabling the toggle for 4.7.

4.6 has been my happy place for getting anything done for a while now.

HN needs to take a chill pill. Could it be that Mythos is expensive and they just want to give people a taste of it? I mean the alternative is not offering it at all?

its unclear how they can offer it broadly but only for half a month.

why do they have capacity now that they wont in a few weeks?

Break between training runs?

It’s offered broadly after, for more money. It’s subsidized as marketing

Ooof so are we thinking that in the next 6-12 months subscriptions will be replaced with paying retail like enterprise currently?

I don't think they'll phase out subscriptions ever, their whole play has been to drive demand from the bottom up. Get engineers hooked on building with claude at home, then get them to demand the ability to use it at work, and bend over their employer with no lube.

They'll probably tighten the quotas to reign in whales though.

They almost certainly already make a fuckload more money off API pricing than they do subscriptions, even if there might be more total subscription users. So offering subscriptions even at some loss is probably going to continue. Honestly, I'd be surprised if they even lost money on most subs; there are definitely Token Whales out there who mess up all the accounting up, though.

Realistically I think Anthropic just has insane demand but finite capacity to run models, and Fable will just make them more money if they dedicate it to API pricing. I suspect the goal here is something like: get individual engineers/PMs on their personal plans to taste Fable and then go to their meetings and say "Yes doubling the price of every single input/output token is a good idea, boss".

But I don't want to be the developer who goes and says we must pay all this money for these tokens. I don't know who wants to be that developer.

But how is this sustainable? It's not like paying $5000 per feature means you'll be refunded if prompting "make no mistakes" didn't work.

The only reason why I pay $200 is because LLM's errors costs me that much, at worst. If "make no error" starts working - sure. But surely, unless you have millions of dollars of cash to burn, a coin flip that costs $5000 is an insane idea?

I certainly hope not. PAYG is not predictable enough for smaller companies or individuals. Where I work (non-tech company), PAYG would never fly. We aren't big enough for that. Of course, you can set usage budgets, but there's a pretty big difference between $200/user/month vs. the equivalent PAYG usage being closer to $1,000/user/month, if you currently use the subscription plan to its limits each week.

Going PAYG only will effectively take these tools away from a huge amount of people and accelerate the push for local LLMs.

OTOH, accelerating the push for local LLMs would also be fine with me.

I doubt it, given the importance of those subscriptions for building and maintaining market awareness.

The AI landscape is changing rapidly, and with Apple announcing the option to change the AI backend, and potential requirements enable AI choices as well, similar to EU browser choice requirements (this is more reading tea leaves than any actual requirements I am aware of). The new OS changes coming to support Googlebook, and deep Copilot/AI integration into Windows will make maintaining user facing subscriptions essential for independent model developers like OpenAI, Anthropic, and Mistal to remain relevant longer term.

If the don't maintain that relevance there is increasing likelihood that they will get consumed by other companies whether it's Apple, Microsoft or Google to form a foundation for their OS, or other cloud providers.

That make sense, but what about the specific bifurcation we're seeing here of super primo models versus still good models being available to subscriptions?

It's kind of annoying not getting access to the primo model and paying 200 bucks a month. I understand 200 bucks a month is basically nothing though.

Like I don't totally understand why they'd let me have it for a couple weeks and then take it away and say I can have it but I have to pay retail and retail is like $1,000 a day.

It's better to have loved and lost than to have never loved at all??

It's a trade-off. Every hyperscaler is buying and building compute capacity as fast as they can dodge red tape. There is limited compute capacity, and scarcity is a real thing.

As a consumer I can choose to buy subscriptions to a range of things, including $5 droplets or VMs on a broad range of cloud hosting providers. I can even buy cheap bare metal at a bunch of providers at an affordable retail rate.

I can also buy "unlimited" AI packages that will be optimized to fit the cost model from a variety of services, with different impacts, such as rolling outages when I consume a daily or hourly allotment.

Right now VC and the investor class are subsidizing the rapid evolution of the services and availability, but that VC is running out. In more traditional economies, AI would have developed and rolled out more slowly, and through metered subscriptions, with the eventual rolling out of "unlimited" packages like telephone, internet, or cell services once the market became commoditized.

We have seen a big inversion of that with the race to "win" AI marketshare. Now the true cost is being exposed, and the most competitive and capable models are hideously expensive to operate, so it makes sense that we are moving to metered billing for a utility service. If you want gas, you can buy regular or premium. If you have a premium car you definitely want the premium, but for most people regular is good.

Give it a couple of years, and the survivors will settle around fairly industry standard models of consumer grade services, pro-sumer accounts, and business/enterprise models.

Things are still shaking out, but I get the sadness. Luckily I work at a big tech company who is banging the drum on doing experimentation so I use my prosumer claude pro and other accounts at home for hobby stuff, and save my heavy lifting and potentially experimentation for work :P

> it feels like they are trying to get subscribers to switch to usage-based billing

I think they might be hitting a point where subsidizing the expensive models for subscriptions makes less and less sense.

With Opus 4.X, last month I paid 100 USD for the Max subscription and got a token equivalent of 4.1k USD.

I imagine that Fable is more expensive to run.

Fable seems very good at finding bugs (unsurprising given Mythos lineage), so this seems a pretty smart strategy. Once you see the bugs it finds in your existing Opus code, it's going to be hard to go back, psychologically speaking.

This is really sad... I really didn't want to be priced out of these models but it looks like that's going to happen sooner rather than later.

I'm sure China will keep things in check. Any silicon valley moats (Mythos/Fable etc) will be eaten up through distillation, trade secret theft and employee poaching.

Thankfully this, like most other tech, will get cheaper through the years.

It already is. But marketing is hell of a drug.

It could be my use cases, which have always seemed to be outside the wheelhouse of these models, but I find it very hard to downgrade after accessing a more capable model.

Opus 4.8 produces output in 15 minutes that is 3-4 hours of my work away from output that used to take me 40ish hours (a solid week of dedicated effort).

Last year(-ish, maybe it was 18 months, I forget when the jump happened), the frontier models couldn't touch this work. The output looked like a hardworking intern on their first day. Nice formatting, decent volume of words, but no understanding.

So it might work if it turns out to be a substantial leap in capability.

I switched back to Sonnet. It replies faster so I work faster. Also cheaper. But I really like the speed. I have to be more specific with what I want. Also I stop it more often than Opus. These new models will be awesome, but they need to increase the speed.

Kimi 2.6 has been my workhorse now. It's as good as Opus 4.6, which, to me, was the last "useful" Claude model.

The newer models are smarter but really ficklle and hard to get meaningful work out of

4.6 was a workhorse

K2.6 on Cerebras is basically a preview of the future. We'll eventually get similar performance locally with Tenstorrent hardware.

Agreed, everything since 4.6 has been worse

> The "offer, then remove" aspect is a bit eyebrow-raising -- it feels like they are trying to get subscribers to switch to usage-based billing, which makes me wonder if we'll ever get it after that June 22nd window.

Probably all about the IPO.

Just like how Elon forced FSD in Tesla to be subscription-only (he was incentivized to do so).

This is just the sales team doing their thing, applying the Law of Scarcity to drive demand.

It's the same exact speed as opus >=4.5, sonnet 4.5, and twice the speed of opus <=4.1

It must have about the same active parameters, or else its a larger model running in turbo mode (smaller batches) and being heavily subsidized for some reason. But given most of the benchmarks are within 5% I doubt it is a much larger model. Most perplexing.

It could be a much bigger MoE model

Then it would be slower.

i doubt that's the goal for them. i bet they just really don't have capacity for people using it a ton, yet they wanted people to be able to try it out while it's new. so they compromised and made it temporarily available. and then hope they can get costs down or capacity up so they can make it more available again

I think the goal is "private citizens: subscriptions; corporations: per-token billing." It's getting people addicted to LLMs on cheap subscriptions so that they can then force companies to pay for expensive inference.

I really don't want this to start being the norm

I don't see how it won't be. They lose insane amounts of money on subscription plans. I'm sure they still lose money on usage-based billing, but probably not as much.

>

They lose insane amounts of money on subscription plans

Do we know this? I’ve seen evidence they lose money on heavy users. But so do gyms.

How do gyms lose money on heavy users? A heavy gym user isn’t really costing the gym anything extra as far as I can see.

>

How do gyms lose money on heavy users?

Most gyms sell more subscriptions than they can fit under their roof at one time. If a gym only sells to heavy users, it will either be constantly turning members away or have to buy more equipment. Its equipment will wear off faster. Depending on amenities, it will go through towels, soap, water,

et cetera

faster, too.

Gym equipment lasts 10+ years in a commercial gym, at $50/mo that's a minimum of $6k paid from a single person.

Unless they're

really, seriously

wasteful with the soap.. there's no chance a gym is losing money on a heavy user

It depends on the gym and their business model! A super-budget gym like Planet Fitness that charges $15/month is going to lose money on heavy users, but they count on most of their members being infrequent gym-goers. A luxury gym like Equinox that charges $300/month can target heavy users without any issues, and they'd actually rather members go

more

so they stay and spend money on expensive salads and smoothies.

Right now all these AI subscriptions are priced like Planet Fitness, but they're used like Equinox. They're hoping that the new a la carte offerings will move their pricing more in that direction as well.

The other user is right, you are being a pedant. Why do you think planet fitness makes money hand over fist? Because 99% of its users sign up, never go, and then also never cancel because it’s cheap enough to leave running. Gyms absolutely bank on low amounts of power users, meaning the rest of the subscribers are subsidizing those that go frequently.

>I’ve seen evidence they lose money on heavy users.

Where?

There are tons of blog posts where folks work out the API cost of their usage and find it well above subscription cost.

That doesn't mean the company is losing money

in aggregate

on these subscriptions. Buffets are still in business even though some people gorge themselves silly at them. The incremental cost may exceed the incremental revenue

for a particular person

or minority group, but that's not how these businesses measure profitability.

There is a difference between making less profit and losing money. Comparing to API cost can only show the former.

I assume consumers aren’t a big note in their bottom line. I’m not actually very sure about that, just an assumption.

What I wonder however is if these tools will become something I use at work only. $100/month is already a massive stretch budget wise. If these models keep devouring tokens there’s no way I’d get the same usage time out of them for $100 in usage credits.

I just don’t think I’d use them much at all at home.

also: Fable takes 2× the usage of Opus

I’m just about ready to cancel my small business 5 user plan with max licenses, because although cowork is really great. I just find OpenAI/Codex to be a lot better most of the time.

> Pricing for both models is $10 per million input tokens and $50 per million output tokens.

The step-up in intelligence looks massive (we'll see in practice), but the price is getting to a point where it's making me question if it's even worth giving it a try.

Good competitors will probably be out soon, which should level the playing field. I am more excited about that, just the fact that they showed that such an improvement is possible. I'm okay waiting a bit longer for this to become attainable for plebs like me.

Models are getting better, but there's a negative change in terms of "productivity" per dollar. Yeah, I can throw 5 sub-agents at the problem, but the cost is getting significantly higher. And yes, I can crank out the solution much faster, but again, at some point that cost will be hard to justify. And it doesn't matter if the cost is subsidized by a provider, if it's paid by your company, or from your pocket. We are slowly reaching a point where the cost will be too high to justify the gains.

This is probably the end of 'use the best model no matter the price'

The pricing can be a bit deceptive though. A good model can deliver the same results in fewer tokens.

Kind of like billing a programmer by the hour.

Sadly this does not seem to be the case here: if you read the announcement entirely, they include a "cost per task" metric which basically continues the trend of their previous models. So yes, tasks will cost you more, but results will be better - allegedly.

Why wouldn't it be? How much would you pay a scientist at this point to think about a problem for you and give you a solution?

I'm not sure how it might be with Fable in practice, but we are already not that far away from AI costing as much as a full-time professional, faster in some ways but considerably less independent.

Perhaps not that close to US salaries, but those are inflated to hell. Worldwide senior engineers and scientists have salaries just about an order of magnitude away from AI subscriptions that you can use most of the day every day.

Pay-as-you-go billing is a kind of drug, I use it every now and then when I'm working on a project with Opus, in a moment you spend a fortune

I suspect it'll go on the subscription plan once other providers have similar benchmarks.

As annoyed as I am about this move, I get it. Users flood the newest, best model whether they really need it or not, and are efficient at using their entire quota. They've had so much trouble reigning in subscription usage it makes sense.

I expect that depends on demand, feedback, and whether GPT-6.0 gets released and is competitive

"we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design).

...

Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user."

Where is this text coming from?

[edit] -- I see that this comes from the system card -- dang merged the comments from the other discussion so that explains the confusion.

My guess is that it is a massive model similar to GPT 4.5 and $10/$50 pricing is for its output will discourage people from using it. I also read safety = nerfed.

One can hope it helps Claude to figure out how to solve their buggy payment system - otherwise how do I pay for these credits.

Enterprise subs not allowed to use Fable if they have setup zero data retention :(

the claimed inference cost is 2x. if that is true, it is massive and remarkable that they're able to do anything like this at all.

This serves as a good reminder that relying on AI models is borrowing your tech from someone else. They can take it away or raise the prices arbitrarily.

If you rely on this as a core part of your business/profession, you will be at their mercy and subject to whatever whims or challenges they have.

It's very disappointing but I'm assuming it's for rational reasons on their part.

But it's not and it's highly disingenuous to frame it like this. Quote directly from Claude code, moments ago:

> Fable 5 · Most capable for your hardest and longest-running tasks · Uses your limits ~2× faster than Opus

It's interesting that we are seeing a time when subscriptions are not preferred and usage-based billing is.

Pay-as-you go isn't a common thing in SaaS. For example, except for AWS SES, all email providers are bulk-subscription based.

The point of SaaS was that the marginal cost (of supporting another user) was low. That does not apply to LLMs.

> "offer, then remove"

Sounds like "bait and wait".

If you think about it, the more people pay for these new and more resource hungry models, the longer it takes for them to become no extra cost and the longer it takes the more people are tempted to pay extra.

I'm about to be priced out of SOTA llms and it's an awful feeling

The AI circular infinite money glitch won't last forever. I hope.

If you have good expertise in a domain and access to cheaper models, you may still be more skilled than someone without expertise but a lot of money to bruteforce the problems using SOTA LLMs.

Why won't they follow suit?

Of course anything could happen but Anthropic has always been stingier and more expensive and is getting worse about that, while OpenAI is getting more generous with subscriptions (eg permanently doubling the highest tier allotment, setting policy to not cut off tasks that run past quota, resetting after outages, etc.)

But they're behind by quite a bit now. CFO (of OAI) Sarah Friar said the next training run will be in the fall on Vera Rubin, I think that means I'll have to wait > 6 months?!

Yeah but they might still have an unreleased bigger pretrain than 5.5. (but maybe not). still 5.5 is smarter than opus 4.8 IME, so you're only losing the mythos tier (fable). and all the cool fun stuff i'd want to use fable for our blocked (can't have it do even defensive cybersecurity work [in theory you can but the classifiers fire like crazy], can't discuss stuff like the furin cleavage site of sars-cov-2, etc)

> * On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.

Of course, they are a casino as well giving you free spins at the wheel with their new Fable machine, and it is done on purpose.

Once there freebies have expired, many of its users will begin to gamble more on the new casino machine and will realize that it is expensive.

If it's that big of a problem to you, you're free to just... not use the freebie?

It’s an interesting thing to bring up because it’s this classic thing we’ve seen for decades now.

The ramifications go beyond the individual which is why I assume they mentioned it. They don’t need to use it/not use it for it to have interesting implications.

so it'd be preferable if they didn't include the model at all?

I didn’t say that and I don’t have a feeling on that either way. But this is a limited time trial and calling it out as such is valid.

Is it nice we get the trial? Sure. Is it also a common play in the playbook of tech companies? Yes.

It's not a freebie, it still requires a subscription and burns tokens twice as fast as Opus.

Then you better not complain how expensive it is to use (Just like the other companies are doing) or the next time Claude goes down then.

Anthropic does not care about us and isn't going to talk to you either and will extract from you as much as possible.

The true answer is local models.

i have never seen this before - where you offer something and then take that away

Really, you have never heard of shareware or trial periods?

Either that or it was sarcasm. What do you think more likely?

A person writing without thinking.

damn they are drugs dealer

Same. I used it today to review my code and it came up with some genuinely good comments and suggestions and found a bug I didn’t think about. Quite a step up from opus. Although one code review took up 50% of my usage.

Why is your comment so grey/downvoted? One of the only actual usage experiences posted in this thread.

Stockfish is a machine learning system, it seems quite plausible you might be getting slapped with the silent performance degradation (

https://news.ycombinator.com/item?id=48467896

).

Them silently nerfing the model without telling you, and still fully charging for it, is a new low and should probably be illegal.

Doesn't this "silent degredation" prevent any actual evaluation of the model? If the model fails at something, this allows anyone to claim that it failed due to degradation.

I don’t think so? They can claim it was an act of God for all I care, but at the end of the day the model failed the task.

Yup, I suspect that's what's going on

I suspect it just sucks, these models aren't useful. Stop lying to yourself.

It’s possible this is happening at a technical level, but I have a hard time believing this is in the spirit of what Anthropic intends to throttle. It isn’t chip design or building out a competitor to Claude.

Stockfish does use neural nets but they are tiny, on the order of 10M params. Frontier LLMs are probably 100k or 1M times larger than that.

Yeah I agree this is probably outside of the intended scope of the silent sabotage mechanism, but there are plenty of reports of the "loud" safety classifier misfiring on innocuous requests and I'm not going to assume the silent failure mode is _less_ prone to false positives.

No, since it's a silent failure, it's not plausible. We have to assume all results we get are the actual model performance, because, it's the actual model performance as we understand it.

Someone trying to solve similar problems will have similar results if the "silent failure" applies consistently in aggregate. So, this is the model's performance.

The irony of "we train on all of humanity's collective output, but god forbid anyone trains on ours" is still incredible

That's definitely the case as model distillation is one of the explicit safety carveouts they mention. Though TBF, model distillation is also a big concern for general safety as distillation could allow you to have the model without the other guardrails. It's sort of a master key to the model.

The pelican has looked very same-y across all frontier models, same color bike, same camera angle, etc. I suspect this challenge is already too embedded in the training data to be a good signal when it succeeds, and maybe even when it fails in pathological ways mirroring existing AI pelicans on the internet.

I'd say it's working great for its intended purpose. Keeps Simon on top of all these threads and funnels traffic to his site.

I really don't understand what's interesting about this test and why is it always on top.

As often happens with random oddball things which become traditions in web communities, the replies asking what it is or complaining about it, begin to gain their own humor value.

Same reason you would always see the same top comments on reddit during a certain era.

That’s what I think too, but we should actively go against such culture here because hn is not reddit.

It basically is at this point, if you haven’t noticed. Complete with the same America bad, Elon bad, democrats good midwit progressive politics.

Elon does suck. Objectively.

Don't forget EU bad! Because they won't let Apple screw over consumers.

Is this Straw Man and Ad Hominem ?

because you can't still ask LLMs to port DOOM to hardware X or Y

It's a meme, and HN loves upvoting memes. Just like Reddit!

The ultimate measure of an LLM is whether it can produce a capable image of a pelican riding a bicycle. All other use cases are but a distraction!

Do you seriously have a dedicated “bad takes on AI” hn account?

yeah, although I do combine it with "replies to snarky questions" for efficiency

The "big beak!" comment in the svg source makes me think it's definitely a gamed "benchmark" at this point.

Was it ever a good test? How do you even objectively assess what a good pelican on a bike is anyway?

SVG generation is a good test because it's extremely easy to

subjectively

assess with visual reasoning where humans are strong. However, pelican on a bike specifically may be overused at this point.

Do you think the models are ready for the next level? I believe that would be: Pelican feeding Spaghetti to Will Smith.

I'd be very surprised if this is in the training data given that most models mess it up to this day. E.g. look at the ones from Opus.

Variations of this comment have been posted for over a year. The pelican has now morphed into part of HN culture rather than a legitimate benchmark, but it's still valuable as a meme.

it is more an example of gaming (the HN system) than meme.

I'm beginning to wonder how much of a useful metric the pelican is because

surely

the frontier labs must be training their models on pelican-artistry because of how well known your test is now?

Simon has addressed this on virtually every new model release. He also has unpublished alternate prompts. But the larger point is: this is a fun experiment, not a serious and objective benchmark.

It's silly and a joke and a surprisingly good benchmark and don't take it seriously but don't take not taking it seriously seriously and if it's too good we use another prompt but don't actually because then it's not the pelican post and there's obvious ways to better it and it's not worth doing because it's not serious.

Only coherent move at this point: hit the minus button immediately. There's never anything about the model in the thread other than simon's post.

But what if they are better at flamingos? Are they optimized for pelicans? How about “draw me a four headed owl”? The meme, I get it, but I’d settle for a working bash script, tbh.

It's evolved from a funny, unserious benchmark to a tradition. When a major new model is released, I now always check the HN thread for Simon's Pelican post. I'll be sad when I don't find it.

When it started, comparing the progress between models was mildly interesting but everyone (including Simon) acknowledges it certainly leaked into the training data long ago.

I just run my own benchmark for "draw an SVG with $animal driving $vehicle". I won't post my choice of animal and mode of transport, but there are plenty of uncommon combinations to choose from. So far it's a fun and visually intuitive benchmark that does seem to correlate with model capabilities

I don't know. Just looking at the bike frames (specifically the fact that the AI generated bikes have rather unsteerable front forks), it's clear to me that frontier labs aren't spending much time tuning models to make bikes look coherent, which I assume is an easier task than making a pelican riding a bike look coherent.

The way I see it the benefit of benchmark isn't to take Simon's results at face value. It's a template for your own benchmarks that are easy to visually evaluate.

I've seen this reply to Simon's benchmark for 2 years running now, and yet you still see improvements and objectively-bad results over time from new releases, even when I'm sure every frontier AI team has/had a person at least partially dedicated to better bicycle-pelican SVG outputs. Alas.

Hence it has become a meta-benchmark of relative progress in SVG image generation of a known target which has leaked into the training data and for which "every frontier AI team has/had a person at least partially dedicated to" at least checking if not optimizing.

I had intended to caveat that: I'm sure I'm not the first person to ask about this!

> you still see improvements

This is expected if they are training their models on it, right?

> objectively-bad results

Keen to learn when this has been the case, i.e. across version increments in major models.

I've written about this a couple of times, most notably here:

https://simonwillison.net/2025/Nov/13/training-for-pelicans-...

I've been enjoying seeing how the quality of individual models differ based on the amount of reasoning effort you give them. If they were baking an a good pelican you wouldn't expect them to differ so much.

(Google Gemini are the only lab that have very clearly paid attention to the quality of SVG animals-riding-vehicles, see their announcement for Gemini 3.1:

https://twitter.com/JeffDean/status/2024525132266688757

)

Amazing, thank you Simon! Look forward to reading.

I honestly assumed their comment was tongue in cheek humour, because positively no one actually cares how these models generate an SVG pelican riding a bicycle. It's some meme thing that this stuff always appears here.

Yeah this is not a real benchmark, it's just a fun tradition everytime a new model is released

"fun" / boringly predictable meme thread with 30+ replies already

It is telling that people need to create throwaway accounts to criticize simonw's behavior in this website.

It was a completely useless test even before the labs trained for it.

Yes, it's always been published as a joke. You've explained why it was (and still is) funny meta-commentary on AI benchmarks.

This is the reply I look for in all the new model announcements. Its fun to tell people that I judge models based on pelicans.

This is all we need, that moment the Pelican put the leg behind the frame, we are all doomed.

Now someone post the link about how it’s impossible for humans to draw a bike from memory.

https://link.springer.com/article/10.3758/BF03195929

I also look for this reply because i like seeing the follow-up reply saying that this is not a benchmark anymore because labs have gotten it in their training data.

that reply never failed to come it's basically a meme at this point

I find it quite interesting that while the picture looks better the more advanced the model is, but apparently none so far "understands" that the pelicans legs are on both sides of the bike / top bar.

If you scroll to the bottom of the Fable-5 by effort page, Max effort actually gets this correct! (Along with being the only one I've seen so far to make a bicycle frame that matches the shape of what most bikes on Google images look like)

And the only one linked here that includes a bicycle chain!

It's interesting that they still get the head tube / handle bar part wrong.

Or the hands not being wings

The Max version gets more details right. The bike frame looks good, the chain, the wings are appropriately styled instead of “arms”, and the knee is bent, etc. Obviously we’re hitting marginal returns now, but I see differences.

It's interesting that Gemini 3(.1?) Deep Think is still the best at this task and it's still not really generally available. Maybe Fable could match it at higher effort levels?

https://simonwillison.net/2026/Feb/12/gemini-3-deep-think/

How much money do you think they spent fine-tuning on pelican SVG generation?

Not as much as Qwen, since apparently 3.6 35B surpassed Opus 4.7

https://x.com/simonw/status/2044830134885306701

Probably none. They probably have much better targets to optimize for than an SVG pelican or even SVGs in general.

Can you please compare the code generated by other similar quality pelicans by other models. Code in your first link (Fable 5 Default) looks minimal yet very good.

Looks like Fable constructed the "max" "looking" pelican of the previous model for the "xhigh" output token count of the previous model.

Is it possible to use the credits from subscription (

https://support.claude.com/en/articles/15036540-use-the-clau...

) for fable?

I'm pretty sure they're optimizing the models around these sorts of tests.

Anyone care about these pelicans that always come up anymore?

Clearly at this point they are part of the training data.

They even all look sort of ish the same. Daytime, colors,...

Without being mean, I encourage you to go look at some of simonw's writing on this topic, which he has addressed repeatedly (and IMO satisfactorily.)

I know because I too had this initial take; however, upon analysis, it is not sound.

I know he is an AI influencer that promotes his blog any chance he gets.

I agree as well that he writes many interesting things.

I could be tripping but I’m sure that is very similar to the Deepseek one from not long ago. Clearly I am too lazy to go and find it for verification.

Yay, max level actually put one of the legs behind the frame!

Personally feel like it could be more ambitious with what it creates.

Where is the

clear

improvement on Fable 5? The tail is misplaced.

Why always sunny days?

Pelicans hate biking in the rain (as do I).

Fable 5 xhigh actually looks the best to me.

Do we need a pelican every single time a model is released? Beating a very dead horse.

Fun at first, seems disingenuous now. A site funnel

The way they talked it up, having both legs on one side of the bike is like walking to the car wash

that's a great looking pelican

need more Alex Moulton style bikes

dude, the max version looks like it's finally

there

. handle bar holding with wings, the left leg is

behind

the frame while the right is in front of it (correctly).

well done anthropic.

mediocre pelican. very disappointing

How many barrels of oil are burned per pelican at Fable levels?

There are tons of ways to generate "strong candidates for drug design." This is definitely not the bottleneck in drug discovery and development. The hard problem is vetting and developing these ideas to the point of having a commercially viable drug. That is still a very empirical process.

Because it's completely meaningless without validation, and even with validation, not really any better than the state of the art protein generation models. Which are also mostly just nice to have because coming up with a candidate is generally quite easy.

The rate limiting steps are generally testing, or characterizing. Not designing protein binders.

It's selective reporting. Says 'in one example', but out of how many, is that one-shot, or is it a random result out of 100. It's a marketing doc.

Would be funny if anthropic ends up as mostly a pharma company

Until we are able to reliably simulate cells, organs, and entire human bodies in silico, we will not be able to move the needle too much on drug design from an AI stand-point (IMO). Like others pointed out, the massive bottle neck in time and cost in getting a drug to market are far removed from developing drug candidates.

Drug design isn't the bottleneck anymore, it's trials. Still cool they can do this with a general purpose model though.

You pulled the arm of the slot machine and discovered why they call it the one-armed bandit.

Almost the exact same thing happened to me when I first tried opus, one prompt no output cost $60 in additional usage

Combine that with it forcing to pay by tokens on June 22nd

While this makes it easier for Anthropic to detect misuse, it also means that the US government and other parties have access to every message and response from every user.

This applies even with API usage through third-party inference providers (e.g. AWS' Bedrock and GCP's Vertex) or with a zero-day data retention agreement in place.

I understand the reasoning for doing this, but I don't love the precedent that it sets.

It will also cause a lot of trouble for companies with specific data access policies (probably most large companies). My money is on this new thing getting gutted very quickly as they figure out how much this constraint cuts into their bottom line.

Well, they already had.

Not in the same way.

A customer could sign a ZDR agreement with Anthropic, and their API usage wouldn't be retained for even a day. That's no longer possible.

meetpateltech is lowk screaming for not getting to the post fast enough

your usage of lowk adds nothing to your post, but it does reveal your approximate age.

At this point that never mattered and who really cares?

These "karma" points are made up and are virtually worthless anyway.

I was wondering when something like this would happen. I got my first and only two content violation warnings in Claude Code last week when asking it about something ML related. It was a real head scratcher because I couldn’t figure out what about the requests could have violated anything.

Might be worth going back and taking a harder look at what I was asking it about if it somehow triggered a “forbidden knowledge” alert. Or maybe it was just a random bug.

"for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design"

Oh man all of those runaway infrastructure buildouts by our agents trying to achieve singularity...

Just say you don't want to lower the bar for others to compete

> frontier LLM development

This seems so wide reaching if it's catching simple things like explaining a paper. Does this also refuse to help with any already developed training pipelines?

I can kind of understand the generation of synthetic data, but nerfing the assistance of training pipelines just seems like a really shitty thing to do.

So insane to me that these ai companies are perfectly fine trying their absolute best to automate as much knowledge work as possible but as soon as this capability can be turned on them they start implementing hidden interventions to sabotage anyone trying to beat them at their own game.

Not to mention these models are all built off of clearly extremely illegal abuse of human created content, which they will apparently never be held accountable for.

I wanted to try on my biology research and it refused to talk about it and proxied to 4.8. Really, only surface level conversations about topics of interest. I know this is not a topic of broad and mass interest, but limiting it for topics like that and machine learning will probably do change how I use it.

I feel like it will have to become more finely tuned on topics of biology.

It is not just biology but is defaulting back to 4.8 for me on time series/information transfer techniques that happen to mostly have papers using the technique on neural data. Other information transfer techniques are perfectly fine, even cutting edge ones, but this one happens to be new and happens to only be discussed in terms of neural data so that is a no go.

With that said, I think it is absolutely awesome. The usage is really not bad at all compared to what I was expecting.

Yes, this stuff is really annoying when it misfires. I've had all my subsequent ChatGPT conversations biohazard-contained for several days for the crime of asking it to explain a gene drive to me.

Is it certain or all advanced topics? I'm curious if it bans questions about quantum computing or fusion.

This is just marketing that Anthropic is building the singularity.

Singularity for me but not for thee.

you will RENT the singularity

and you WILL enjoy it

Singularity as a Service

"we should put on hold the development of AI because the world is not ready for it"

Yeah... We need open models so we don't have that BS.

Let's hope not all frontier AI assimilates these guardrails. It would be a shame for independent researchers and students.

This is super annoying and imo, really limits the usefulness of this model.

It speaks volumes about what Anthropic's position as a company and its priorities will be going forward.

I doubt this kind of gatekeeping will prevent open-models or other innovation outside Anthropic to slow down.

I would imagine these guardrails, if needed at all, should be done at a legal framework level and students should not be a part of this blanket approach to limiting the usage of these models.

Anthropic probably trained Mythos on their own code and found that it is too got at reproducing it.

I doubt that. Why would you train Mythos on its own code if you don't want it to be able to reproduce it? It's not going to add much to the overall corpus.

Synthetic training data has been the name of the game since years ago.

That's strange... I've been tinkering with a little LLM-from-scratch project for a while now, and Fable is just continuing it without a problem

Probably claude.md has some logical explanations for it to bypass softly. Most project guardrails can be beaten that way.

It also tried to force usage the paid Claude API instead of claude code usage just because there's a mention of another provider we might want to plug in (which hasnt even happened) for AI integration.

Ha funny, I was speccing out an idea for real time Claude code interaction from local apps using some tricks vs using the agent sdk when I got the popup to try Fable. So of course I gave it a go, and it triggered the sensitive content warning immediately, which I was very confused by until I put two and two together.

Fun times when “safety” means both the safety of mankind, and also the safety of revenues

Anthropic is really speedrunning their evil arc as fast as possible. Can't use them for basic LLM research, cybersecurity, or beyond-surface-level discussions of biology and virology, but Anthropic is allowed to sell Claude to the trump administration to kidnap maduro and to bomb iran. And don't get me started on that $100M autonomous killer drone swarm contract that they applied to and rationalized as non autonomous...

I told everyone here that Anthropic are not your friends for months.

Again, HN fell for the marketing and believed everything they did was for "safety".

Didn’t Anthropic famously refuse to work with the US gov on military applications that would violate its safeguards?

https://apnews.com/article/anthropic-pentagon-ai-hegseth-dar...

> Can't use them for basic LLM research, cybersecurity, or beyond-surface-level discussions of biology and virology

Your priorities are not everyone else's priorities. The people concerned about AI extinction risk list those as three of their biggest priorities for AI to

not

do. Those are the people whose culture Anthropic descends from, and by their measure, those exclusions make this the

least

evil path.

More like Anthropic’s priorities are not everyone else’s priorities. They are in the consistent culture of being in absolute control and dictating what is good and bad, while taking any opportunity to trash and crush potential competitors (open source models happened to be mostly developed in China). All these in the name of safety and anti-authoritarian.

The day self hosted models catch up with Anthropic’s capabilities is when they will fully lose their shit. This day can’t come soon enough

Extinction risk. From population genetics... Does Anthropic even employ biologists? It's magical thinking about a field that is poorly understood by their community.

Opus 4.8 already drops to Sonnet when you ask it cybersecurity or biology questions

yea i dont trust simonw comments at all. I still havent seen what he has built with ai thats so impressive to justify hiis all his nonstop ai hype.

You would think he is churning our cancer drugs or something if you read his comments

If you assume that LLMs are about to make software development a dead-end, then the best answer to keep a good income is to ride the wave. Do nothing and get left behind, embrace it and maybe you'll find a new niche.

ok? but wouldnt it be good for hypeartists to back their hype with impressive output? not pelicans and shit.

He is become one of the techbros

so annoying to see his shallow hype comments at the top of these type of threads. his "its awesome" comment on this post is devoid of any actual substance :/

to his credit comment does say "this could be possible in opus too" but ppl couldnt help upvoting it anyways.

>Seems like GPU drivers are cyber weapons

They kind of are, at least in the AI race.

> weapons of math destruction

lol. great, whether intentional or not.

The frontier labs now have every reason to hold back and sell only to their preferred trading partners. I don't really like the new arbiter-of-knowledge system we're barrelling toward.

They're useless tools only helpful to lazy people that don't want to learn by themselves.

This sounds more or less unavoidable? Decompilers are inherently security-sensitive. If you take avoiding cyberattack uplift seriously as a goal, I don't see how you get around essentially refusing to work on them.

Obviously there are plenty of innocuous applications too, but it's not like the people building decompilers for nefarious reasons will be explicit about it. The LLM abstraction just inherently doesn't have enough context to distinguish your intentions or your broader use cases. This is why both Anthropic and OpenAI have had to create side channel mechanisms for security researchers to establish a trusted use context. It sounds like this makes this not a viable product

for you

, unfortunately, and it makes sense that that's frustrating. But I also don't see what different behavior one could reasonably expect given the constraints.

If it's any consolation, these restrictions only make sense for models that are ahead of the open-weights frontier, so open-source hackers will presumably get Mythos-level capabilities in the relatively near future anyway.

I'm not sure how the new guardrails work exactly, but I've read enough of reddit / Chinese communities focused on jailbreaking the models, to know that you either have to nerf it to the point where it fires even on "kill the task", or someone (maybe even other LLM) is going to come up with a set of tokens that is going to go around the defenses.

Nerfed models are really bad for PR, especially when you're staking your company's future on it being the smartest, most dangerous thing in the world.

So I believe they will ease up on nerfing/guardrails just enough that bad actors will find a way, while good ones will stay limited on anything dual-use. Just like such restrictions usually work in other places.

P.S. yes, "kill the task" did, in fact result in a refusal AND a warning on my claude account in Opus 4.8's early days.

> If you take avoiding cyberattack uplift seriously as a goal

This "uplift" risk obviously excludes the US. The goal of this is that the US bandits (like NSA) will find exploits and attack other countries (classic US behaviour), but these other countries can't be allowed to defend against these attacks. NSA/CIA thugs are "trusted", foreign defenders in sanctioned countries will of course be "untrusted".

Ah, you're probably one to ask. They say "queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8." Are they transparent about when that happens, and is it priced at the rate of the underlying model?

They are transparent about when it happens but no reason why. To be fair, it doesn't interrupt the flow, just drops to Opus and proceeds. The most frustrating thing is that it happened on a plan and Fable just refused to have anything to do with the plan.

My bet is that Mythos is still over-hyped and the cybersecurity fear and guardrails are mostly marketing to force company partnerships through Glasswing and get public attention.

Mythos is from the same guy who did "GPT-2 is too dangerous to release"

https://naokishibuya.github.io/blog/2022-12-30-gpt-2-2019/

He was kinda right.

Lawyers, doctors, students, teachers. Lots of people using GPT models carelessly in harmful ways.

Obviously not what he meant at the time but hilarious(ly sad) in retrospect.

Delaying a technology release is not going to stop that in the long term. Society, culture, and the support tooling just needs to adapt. Just like how AI coding is still in the early days.

The sooner people learn the risks and build the infrastructure to make it fail less the better.

The claim I remember was that releasing it would start an arms race for AGI, which was absolutely true

If it was truely an arm's race to AGI they would've stopped relying on the data/param scaling law BS ages ago.

"Malicious use" means spam, propaganda bots, etc. It's nice to give people who work on spam filters some heads-up.

It's clear that the parent didn't bother to read the link they shared, which articulates exactly this. That's embarrassing.

From the link:

> They summarized their findings from the nine months:

> 1. Humans find GPT-2 outputs convincing.

> 2. GPT-2 can be fine-tuned for misuse.

> 3. Detection is challenging (detection rates of ~95% for detecting 1.5B GPT-2-generated text by RoBERTa).

> We’ve seen no strong evidence of misuse so far.

> We need standards for studying bias.

>

> All these points are valid, and OpenAI did a great job identifying potential risks, especially misuse and biases, at an early stage.

> All these points are valid, and OpenAI did a great job identifying potential risks, especially misuse and biases, at an early stage.

Many of the OpenAI employees who were focused on these risks in GPT-2 later founded Anthropic, notably Dario [1]. Since the beginning and continuing through today Anthropic describes itself as an "AI safety and research company" [2]

I'm not sure if the OpenAI of today has the same focus on safety, or if they do the minimum to not look irresponsible given Anthropic's effort.

[1]

https://en.wikipedia.org/wiki/Dario_Amodei

[2]

https://www.anthropic.com/company

Just to be clear: that is quoted text from the source and not a statement I'm making, in case that's what you're suggesting here.

People quote the "GPT-2 is too dangerous to release" thing as if it were wrong, but given all the slop all over social media and how it's used to create division and attack social cohesion, he was clearly right.

History is long and never over, so he could easily be right both times before this is through.

That guy is the biggest clown lmao

The UK gov disagrees with you:

https://arstechnica.com/ai/2026/04/uk-govs-mythos-ai-tests-h...

https://www.aisi.gov.uk/blog/our-evaluation-of-claude-mythos...

AISI did also say that GPT-5.5, which has been public for months, scores basically the same as Mythos on their cybersec evaluation. But there wasn't as much media about about that for some reason.

https://www.aisi.gov.uk/blog/our-evaluation-of-openais-gpt-5...

AISI found the release version of mythos preview outperformed GPT-5.5

https://x.com/AISecurityInst/status/2054589763173126339

Government of the least mismanaged country in the world?

AISI is basically the crown jewel of the British government at this point in that its actually pretty good.

You mean the most mismanaged country?

Bingo.

"We had to do extra work to make this safe because it's so advanced and dangerous..." how many times can they trot out that line before it loses its effect entirely?

Only three times, if fables are right.

The Startup Who Cried Unsafe, by AIsop

I mean, they do actually describe what that extra work was, and people elsewhere in this thread are complaining about the effects of those safeguards. So it's not like this is purely empty rhetoric.

people are not questioning whether they did the work, they are questioning whether the work was really necessary (i.e. if mythos is

really

so good that it needs safeguards to prevent malicious actors from using it)

With homo "sapiens" "sapiens"? A few decades at least.

It worked for OpenAI when GPT 3 was deemed too dangerous to be released. This is just a spin of that.

I still remember it. "Open"AI going API-only because GPT-3 is really really dangerous, so forget the Open in our name and all of that, you can't download our models anymore and must request access to them because they pose a THREAT.

Fast forward to today and GPT-3 has laughable performance.

Even back then there were plenty of people who got fooled by AI generated articles. It's easier to spot AI writing now because we are so used to it. They were right to be concerned; not that it achieved much since oss models run laps around gpt-3 now.

But it seems like that was not genuine concern, but instead a tactic to pivot to closed models and an API service with an excuse to do so, breaking the public's expectation that they would be a non-profit making open models, like their name implies.

Yes, and "in collaboration with the U.S. Government" feels like a very gross ploy at appeal to authority. You don't need Mythos or really any SotA frontier model to make malware or do extensive penetration testing/reconnaissance already. Sure, Mythos might be faster/more efficient, but the cat has been out of the bag for awhile. Even the terminology "infrastructure providers" practically screams "Enterprise leads".

I think all models can find vulnerabilities if read the entire code base. Or intelligently combine parts of the codebase. Especially with test loops.

I know a security researcher at Google with access to Mythos. He says it's the "real deal" and that "there are career plans I had that are no longer viable".

He could be incredibly naive. We'll all find out with time.

I fear it's a smokescreen to manage cost and capacity.

And to ensure that only USG-approved entities are allowed to secure their code.

It's not even very usable... I tried 2 different chats and both eventually got stopped due to the safeguards

One was a piece of code I gave it to improve, it did so and then started writing tests, some of which tested security so the safeguards triggered

Another was one of the cryptography puzzles I use as new model tests, which are hard to oneshot and there's no public solution anywhere, it completely refused to even try to solve it

I tried 2 chats and it declined both.

- 1st chat asked about a minor shoulder injury most likely mechanisms

- 2nd chat asked about optimal bloodwork testing markers

it seems to dislike biological chats. Rejected me on a chat that I am running with 4.8 as well on a rare condition I have.

So the degradation to Opus 4.8 from the article isn't happening in practice?

No, you get a AUP violation and have to manually swap the model

(I had same issue, just asked it to check some code that 4.8 had modified earlier in day)

It is, it asks you if you want to continue as opus 4.8… but I was trying precisely to evaluate fable

Maybe that's only in the chat UI, and not the API?

Oh joy. A model whose safeguards make it prone towards code that make your systems less safe. How brilliant!

They're trained in a model class likely in 2t to 3t range. It's very unlikely that chinese labs have access to gpu systems capable of training models like that, let alone serving them. This requires proprietary room-scale systems which fetch a huge premium over typical 10 slot systems.

I am sure that they can develop their own equivlient version of such clusters in around 1 year though. Distilling fabel 5 will also go a long way.

DSv4 is nearly in the 2t range, but yes you're generally right

MoE experts were likely trained independently / in a sparse format. Training anything beyond 2t on typical systems would be infuriantingly slow, you could do 4t on nvidias room-scale solution, but for a reasonable training speed / batch size it caps around 3t.

Do you have any resources to share regarding independent expert training? I was under the impression that it's not feasible.

concept is similar to how it works in inference, instead of performing regressive writes to the entire model you run the whole model, but part of the model can live in system memory and get swapped in/out on demand. So only XB parameters are active in training.

edit: I am not really sure if it works like that. I haven't looked too deep into deepseek v4 pro specifically.

We’ll see it distilled first.

Ah, American Hubris ... I don't blame you, Hollywood is the world's greatest propaganda machinery of all times.

My experience is that open weight models from China are at least ~12 months behind. In some workloads they may be closer, in others further away.

I also find that the harness and product you wrap around models can often narrow that gap considerably.

Opus 4.6 for example, on a PR-for-PR basis was head and shoulders above GLM 5.1. Perhaps GLM 5.1 was a bit under Sonnet 4.6 at the time. That's roughly a year or so behind.

Much cheaper though! I'm bullish on open weight models, I have no idea where all these curves will top out, can the frontier labs keep the year plus lead? Do open labs get close enough to SOTA that they gain adoption across many tasks and drive down inference prices??? Who knows, not me.

I wonder if model distillation will continue to work as well as it has. Given hidden reasoning, the ever expanding number of expected capabilities, a serious compute shortage, the looming possibility of model collapse, and dramatically higher API costs I would guess that it's getting much harder to do.

You should check out some Chinese forums. There are services selling gateways/proxies for all major models at fraction of the official rates. Likely reselling subscriptions, or some other form of abuse.

I've seen people posting screenshots of billions of tokens consumed where they paid next to nothing.

These same gateways are likely also reselling the data to Chinese labs, because TLS has to terminate at the gateway level.

Asian labs generated synthetic datasets from UBS labs but also innovated with technology. Now it is harder to get the thinking traces AND Anthropic is recorded to poison it as well.

Thus Asian labs will have to generate their own data sets, which with the huuuuge usage boom from deepseek, mimo, kimi, etc, they will be able to.

There's also a reality where China does develop Mythos-level model but stops releasing the weights.

That reality is much scarier.

That's the reality China already lives in. Their weapon against US companies is commoditizing them, eliminating their moats and their profits by going open weights.

Same thing Meta was doing before they fell behind.

> Same thing Meta was doing before they fell behind.

Obviously unrelated to the OP, but it's crazy to me how incompetent Meta is at everything new they try to do.

They burned billions of dollars on the most ridiculous project one could ever think of - somehow thinking that VR is the future.

Then they did catch the initial wave of actual future with AI, they were at the forefront of open weight models - and failed at that too.

What is even happening there?

Meta made Pytorch and a lot of vision models back in the day, like Faster RCNN, Mask RCNN, the Detectron framework, and more recently the SAM and DINO series. AI not just LLMs.

muse-spark is the next most capable text model after Opus according to LMArena FWIW

I wonder where the trees are. In this thread nobody appears to actually be talking about the model.

Yeah, because it's impossible. You can't ask it

anything

about the thing that it's known for. It will not even answer a sky-high level question about reverse engineering, for example.

In CC, it will probably report you to authorities if you ask it to do a vulnerability scan of your codebase.

Isn't that a good thing in a way? If everyone has the weapon and defense at the same time, we will fix security holes and live safer lifes instead of having some three letter agencies and military backdoors in everything.

Pandora box is open anyway. It's better now for everyone to have the same power rather than a few national states.

Not sure this holds, sadly. I spent a few months reporting serious security bugs as model capabilities took off earlier this year, and only ~half were fixed. The unfixed bugs were just as critical as the fixed ones; sometimes they were even two similarly critical bugs at the same company, and only one would be fixed!

On your other point, the government still has systemic leverage and can compel access, so this doesn't remove that risk.

That doesn't mean this is the end of the world, and some balance of power is usually good. But I do think it will still increase the capabilties of rogue actors and their net harm.

It's more evidence that the future is local. With some time we'll all be running highly capable & efficient open-source models on dedicated NPUs. No censorship, no rate limits, no overpriced subscriptions.

I think we're about to see a big relative drop-off of open models vs closed. I don't think there'll be an open model that competes with Mythos for ~2 years.

Even OpenAI and Google are struggling to get this kind of performance. If the distillation defenses are any good + chip controls prevent China from training massive models, it's over.

I think the Chinese have identified this gap and are working overtime on sovereign inference tech including chips.

They have, but even with the whole CCP backing you you can't just catch up on the chip war overnight. It's going to take time to get their memory and compute industries where they need to be. Meanwhile, barring an invasion of Taiwan, US will have Rubin class models and then whatever the next tier is, within 3 years.

Unluckily for you, they started back in 2014, and had a huge incentive to speed up in 2019 when Trump started restricting exports.

'Barring the invasion of Taiwan' might actually be quite a lot to bar in mid 2026.

My hot take is that it's now or never for Xi, and from the specific things he is reported to have said to the US president at their last meeting lead me to think that he at least knows this is his big chance; whether or not it is taken is the part of the forecast that is opaque to me.

Oh they might try to put in place safeguards, but Qwen has had no problem being abliterated

3-5 months is a long time and they are pretty useless on arrival because the frontier models are so good, that it's hard to go back even if it's way cheaper. Your work flow is adapted to that level of intelligence for months.

That doesn't match my experience at all. I can't see myself saying in 6 months that the current model I am using is useless, that makes no sense.

In fact, I did go back to DeepSeek V4 Flash for most of my problems as it is way cheaper and there is no need to use SOTA for absolutely everything.

Oh please let’s stop with the Mythos “it’s dangerous” PR talk.

Its obvious Anthropic used it to hype things up and that’s about it.

> every bit as capable and dangerous as current day Mythos except with no safeguards

Not quite. They will definitely have "no criticism of China/communism" safeguards.

People can work around those if they are open-weight.

And, thankfully, I never needed to have a discussion on Chinese politics with LLM in all the myriad of uses I had for it.

Trying asking fable is Israel is committing a genocide

> Rationalists are inventing oligopolies from first principles, absolutely incredible things happening in SF.

Based.

I don't think China has any incentive to arm the rest of the world with highly capable models that can be used against them. Undoubtedly they will continue with the arms race, but they will preserve the best stuff for their own use.

I think the stronger incentive is undermining/undercutting the Western AI companies. Given what we have seen, any model can be used/convinced to do harm so that is just part of the game

I agree, depending on how much of this is marketing and how much is actual capability. It's one thing to undercut models that finish writing assignments for lazy students. If this actually identifies vulns and writes exploits, or if it designs bioweapons, those are pretty different. Those are actual weapons, and I don't think they're going to arm the adversary.

A specific strategy is to arm absolutely everyone with very capable models, thus eliminating any advantage the U.S. could get from frontier AI.

It triggered for me when I asked "Web search for your own model card (released today) and pick out your favourite highlights from the pdf"

Did not trigger for me (Fable answered the question), so I guess the filters are either non-deterministic or are still being tweaked.

Interesting, I assumed

all

model-routing was done utilizing an LLM. (I.e. non-deterministic.)

It’s possible that there’s a set of words or phrases that route deterministically to save money on obvious stuff.

I kind of wonder, though, which model they’re using to do the routing. It seems like a huge added cost to do these kinds of checks on every request

Wasn't it leaked in the Claude Code source that it was all regex?

Iirc correctly Opus 4.7 had the same problem, safety filters were triggered way too easily at the beginning.

sunglasses _are_ safety filters

(I fixed (er, literally!) the formatting of your table there. I hope that's ok. Formatting info, such as it is, at

https://news.ycombinator.com/formatdoc

)

Hi Dan, you know how sometimes comments get moved elsewhere?

This is a huge ask, but any way we could get the comments organized in a "experience with model" vs. "meta commentary" fashion? The meta is overwhelming in this one.

We try to do that informally but of course the quantity is overwhelming. It's a natural place to experiment with AI classifiers, and we'll eventually get round to that.

So far, the top half of this thread seems to be about the current release - that's after some of the manual moderation I just mentioned. (Basically, we try to downweight generic subthreads until the top subthreads aren't generic any more. There's certainly a place for generic tangents in curious conversation, but they should be lower on the page, and tend to get upvoted a lot higher than that.)

If you (or anyone) sees a counterexample, i.e. a generic subthread in the top half of the thread, it would be interesting to see a link - we can treat the current case as a datapoint.

Thank you for the reply, and the work.

As a protentional counterpoint to my request, this is just perfect:

https://news.ycombinator.com/item?id=48468156

I had Claude straighten it out:

Model In Out BIn BOut

Haiku 4.5 $ 1.00 $ 5.00 $0.50 $ 2.50

Sonnet 4.6 $ 3.00 $15.00 $1.50 $ 7.50

Opus 4.7 $ 5.00 $25.00 $2.50 $12.50

Opus 4.8 $ 5.00 $25.00 $2.50 $12.50

Fable 5 $10.00 $50.00 $5.00 $25.00

Approx. 5% sessions? That's insanely high.

4.6 stan here. Yes, agreed. However, I will try this model out in Claude Code. Some indicators seem positive.

For the LLM use cases in my own products, you can pull 4.6 out of my dead hands! lol

edit: Fable 5 appears to be the real deal in at least some use cases. Damn.

I've personally liked 4.6 the best to date, preferring it by far to 4.7 and 4.8 (even with these on max effort!), both in Claude Code and for non-coding tasks in the chat UIs.

Still early but from my first few interactions with Fable on high in both settings, it feels like it might finally dethrone 4.6 for me, but time will tell.

Hoping it doesn't get nerfed and eventually comes back to the subscriptions.

Of course. You use their AI to ship code full of bugs and security holes and then they conveniently have the tool to fix them, for an extra fee.

What's wrong with it?

The tags are actually displayed in raw text not rendered.

The next model will fix this.

These models are just tools. The economics of many tools only make sense for corporate buyers.

kind of disagree here. on the surface this makes sense, but this isn't "Adobe Pro vs Freemium version" where some tiny vertical slice of your business can be made slightly more efficient with a b2b enterprise plan. this is generalized intelligence and literally everybody can benefit from it in an immeasurable number of ways. i would go as far as to actually compare it more to water or air than a tool.

if only the hyper wealthy can access the pure water that doesn't give you cancer while the rest of us drink from the Ganges river/sub-100iq models that drool and hallucinate/waste time, then I would say that's pretty terrible for the world. it'll just create extreme disparity in our world, far far worse than anything that exists today.

and you may think, man what a ridiculous example, but think about it this way: what happens when something like Mythos or some future model can actually solve your specific cancer (we're getting closer and closer), but is entirely impossible to afford? Or perhaps you need boosters that require the AI to create more of, and now you're reliant on a model that is too expensive.

Open source needs to save us all from this

I’m entirely in agreement with this POV, but I’m also copacetic about it:

You could have said much the same about computers in the world dominated by IBM mainframes 60 years ago. Now we have vastly more powerful computers on our wrists (or our pacemakers!), let alone in our pockets or on our desks.

As far as my understanding goes the bottleneck for what you are talking about is hardware not software, so open source won't help that much for the foreseeable future.

> and you may think, man what a ridiculous example, but think about it this way: what happens when something like Mythos or some future model can actually solve your specific cancer (we're getting closer and closer), but is entirely impossible to afford? Or perhaps you need boosters that require the AI to create more of, and now you're reliant on a model that is too expensive.

Isn't that already the case with current care? Wealthy people get a standard of care poor people couldn't even dream of. Rich people live, temporarily embarrassed millionaires die.

Not really. Medicaid coverage produces comparable cancer survival rates to private insurance when you account for selection effects:

https://news.cuanschutz.edu/cancer-center/connections-betwee...

but we’re going to get a 90% cost reduction in the next 18 months… right? Right guys? Sam Altman wouldn’t lie right?

most people can afford it for a few special projects now and then. but for me, I have been trying to avoid Opus as a daily driver for a couple of versions.

People making high-end salaries can afford Fable for critical parts of their projects though.

I hear you, but with the hype surrounding Mythos the demand is going to be insane. I'm already hitting server errors in claude code.

Established companies welcome pricing that reduces the potential for competition, if coding is a primary barrier.

It's not a conspiracy. There's a finite amount of compute available, and they will sell it to the highest bidder. If another company can produce the same intelligence for cheaper, then they will drive the price down.

Look at what Nvidia did to Gamers. Nvidia built its castle on the dead bodies of Gamers who supported and cheered for Nvidia.

Looks like a marxist revolution is soon going to be on the mind of a lot of programmers. We've finally reached the point where the "means of production" in software are back in the hands of the bourgeoisie.

It was good while it lasted. But now that only the wealthy can afford access to the best models, software development is starting to look like most other industries, no longer a place where some dude from nowhere can build something cool from his basement because he will be competing with huge companies with unlimited access to those models.

Exactly, where are the organizers of this movement?

Collective self interest does not require active organisation.

Only companies can afford MRI machines, and that's okay.

Indeed. And that’s why the US has more than 3X the MRI machines per capita than Canada, where they’re all paid for by the state.

Guess we'll see what OpenAI does with their next model release -- but this move is doing nothing to get me to come back to Claude after switching away due to their reliability issues.

In a way I relish the opportunity to just make do with cheap Chinese models, massage my prompts, and go back to coding by hand. If this is how it's going to be, screw 'em.

I don't make money on the code I am writing right now. I

really

don't like where this trend might go.

Something I never thought I would utter: Here's hoping for china to surprise us.

The fallback doesn't seem to be working for me, I haven't scanned a project in it immediately booted me when it found a security bug even though I didn't ask for it