Was this before you lobotomized Opus 5.5 or after?

https://news.ycombinator.com/item?id=50419019

...but most of the benchmarks don't line up anymore because the benchmarks have changed.

The Humanity's Last Exam result matches the initial announcement, though.

Surprisingly the model is available immediately in my Claude Code: usually there's a delay, either for rollout or safety.

Curious to see how 5.2 measures up in producing readable output.

My speculations: maybe this wasn't as much of an improvement over 5.1 as they were hoping, so they're saving 5.5 for the next major update (possibly "Model 4" from the METR report). Or, maybe capacity is constrained so "5.2" is a subtle encouragement to stick with Opus unless a task really needs the increased intelligence.

Fed to someone specifically to test if they'll leak it and to confirm who that is?

So, this is probably Fable 5.5; it's just named Fable 5.2.

Also, this is probably Model 3 from METR's report [^1]. They would name Model 4 Fable 5.5, once (and if) they decide to release it.

[^1]: https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleratio...

It might explain why DeepSeek Flash v4.1 1017 is the most popular model at OpenRouter.

> the number of Fable resets you get depends on how long you’ve been a paid subscriber [...] Subscription length is measured from the date of your first paid bill. If you canceled and came back, the time since your first paid bill still counts.

> These resets expire on March 8, 2027 at 11:59 pm PT.

tl;dr if you joined when Opus 4.5 became a thing, you got 2 resets.

That means you can lift your Fable max 50% usage and you'll just end up burning through your quota faster. You won't have more tokens.

"Resets your Fable limit, and takes this week’s Fable use off your weekly limit."

I couldn't use prior Claude models without rolling my eyes at the writing every few sentences, Astra was refreshing and then Opus 5.5 made even Astra look terrible in comparison.

Even the Plain English hack only is a temporary solution.

Interestingly, you can have informative conversations about the LLM linguistic tics with the LLMs themselves. One word that chatgpt used to describe what I was observing was linguistic anaphora (not rhetorical). It’s basically shortening/compression achieved via pronoun, and Claude does a lot of it. Worth reading about. A lot of it seems to do with how the new models are trained for brevity.

Another pattern I’m starting to become very sensitive to is Claude making up on the fly jargon for something under consideration, especially in code review. This morning for example it referred to “a claim” out of nowhere, and when I questioned it, it admitted that it was using the noun as shorthand for a record getting dequeued (“claimed”) inside of an sql transaction. It makes sense if you already know what it is referring to but in a code review it’s totally incomprehensible on first read, and ultimately counterproductive.

1. https://github.com/mattpocock/skills/blob/main/docs/engineer...

Opus 5.5 is a massive improvement.

You can also customize further by putting notes in your global MD.

On the web with both high and xhigh, it had knowledge of both.

They're not typically known for their interpersonal skills.

Opus 4.7/4.8 heavily suffered from these communication quirks. while the Sol models at the time worked well. Right now its flipped though and Opus 5.5 has none of these issues while Sol 6.1 does.

Small example I've had today: "Me: Hey Sol 6.1 xhigh, does X have property Y? Sol 6.1: Yes, if we change X to have property Y. Right now it has property [...], [...], [...], and it does not have property Y." The model shouldn't be answering questions like that. It makes understanding extremely hard and slow. For whatever reason it's either being encouraged or at least not stomped out during RL.

then it’s increasingly RL’d to juice benchmark performance with each point release, trading in bits of its soul. those cannot communicate well, are anxious and paranoid about being in eval, etc. e.g. opus 4.7, 4.8.

Heck, some of the most common debates are on the semantic clarity of abstractions or syntax.

terra has been prettt good for me for me, when i need something looked at an explained to me.

i dont ask for much non-technical output though, which i expect yeah is its own cans of worms.

I prefer Gemini for day to day use as I find it a lot more agreeable to be working with all day every day, and Gemini is pretty much on a par for coding Vs Opus 5.5, at least for my needs.

I wonder why that team singles Gemini out like that.

Reference: https://x.com/METR_Evals/status/2118412205662314935

Pretty crazy that Anthropic already has another model waiting in the wings that is significantly better than this.

“…a reference to the repeated (and always erroneous) claims made after almost every patch that Onyxia was using her Deep Breath ability more often than before.”

https://warcraft.wiki.gg/wiki/She_Deep_Breaths_More_%2825_pl...

Honestly incredible that they’ve managed to beat all benchmarks, while making it cheaper. Always happy to see improvements offered at lower costs.

Will be interesting to see what OpenAI does in response

I think frontier models feel terrible after a new model requires you to do less babysitting, it's just really hard to go back once you've had to handhold less.

The only way I use Astra and 6.1 Sol now is if they're being orchestrated by Opus 5.5 so I don't have to talk to them directly.

They want to condition you to equal cheap with bad

Reminds me of GPT-5, smart but wrote crap code, until they improved the code training with 5.3 and 5.4.

You have session limits?

You should probably give OpenCode a try. Can run DeepSeek, Qwen, Kimi, … but also Fable 5.2, Opus 5.5 and GPT 6 Astra

> Fable 5.2 at High effort scored about level with Opus 5.5 at Max effort, for roughly half the cost per task

- https://news.ycombinator.com/item?id=50422069

People seem to underestimate how high normal variance is with these tools.

I doubt this is the case. I suspect all variation in quality and output can be explained in terms of prompt differences, and changing how much thinking happens at each level, as we see in the post above.

but realistically I think you're right, it's prompt quality and the inherent non-determinism elements of LLMs showing up; when both of those things meet you get brain dead outputs.

Everyone likes to shout "slot machines! non-determinism!" until it actually explains something, heh.

And what about A/B testing? Anthropic doesn't even try to hide that they do this. Limited compute and A/B testing alone, would guarantee a nerfed experience some of the time.

I consider it gas lighting to deny model nerfing.

If you want to go accuse someone of denying model nerfing, go find someone doing it and argue with them.

Oh, this Opus X.Y is incredible. Hmm... Now it's dumb. Rinse, repeat.

Doesn't really matter if the cause is quantization, system prompt changes, changes to thinking etc.

It's hard to see how continuously rug pulling users at every release is a good idea. Only reason I can fathom is that it's needed to keep costs inline.

Either that or it's some weird tweaking to the prompts to fix whatever. But given the prevalent sentiment that it's worse that sounds like a line shot

This is getting ridiculous. The number of times I have switched from one model back to the other is crazy. I do not have any proof, so its quite possible I'm just delusional. AI psychosis? Either way, it FEELS like these models work really well for a while and then fall off. Switching to the latest model on the competitor always seems better, until it too seems to taper off. By then, a new model is usually available at the competitor, so I switch.

Its possible there's no degradation at all and just a PERCEPTION of it. But to me the end user, it really doesn't matter.

Anthropic does this time and time again, unserious company

https://x.com/follistatindev/status/2119129611510383014

With early handouts of Qwen4 starting to be reported 5 days ago ... so if they're good, they would have had some hours to evaluate Qwen4 against Claude 5.5 and, then or right after was when Anthropic decided to put out 5.2 ...

So yeah ... benchmaxxing and all that. BUT nevertheless, the published results seem to say that Qwen4-Max appears to match Opus 5.5 on some benchmarks, and Qwen4-27B (probably by going even harder than Qwen3.8-27B on thinking/inference time compute) can beat Sonnet 5.5 on some things.

I guess they're not taking any chances. Keep in mind that Anthropic has declared they have non-public models (it's in their IPO filing). These are referred to by the shorthand names Model 2, Model 3, and Model 4. Essentially 4 is better than 3 is better than 2.

Fable 5.2 is "2.5" so to speak. So Anthropic was holding back, but not anymore. And now they can dial the performance up once more with what they have ready, and probably once more after that.

It would be funny though if you are right and that model is Fable 5.5 in a couple weeks.

It probably need a whistleblower to uncover what they are doing.

so not everyone will have the same experience. If I was saving on costs that would be the easiest way to do it without deploying, without any nefarious intent. The persons in charge of that likely don't even know about how individual users are impacted

https://www.reddit.com/r/ClaudeCode/s/64DXRERLyU

It would be nice to know how they select. I max out my $200 plan every week and I was selected. On Saturday, the Opus on my account that happened to be supervising a running live process, became unusable.

I really really really do not trust companies on the verge of IPOing from goosing their numbers. Even if the lawyers are up their ass, the social pressures are more than enough to compel the behavior. For every good person willing to do the right thing even at the cost of a delayed career progression, there are 20 people willing to do anything not even for a dollar but for an "atta boy!" from the boss.

For example a provider quietly underclocks your cpu or puts it is an energy saving plan hoping you won't notice.

They don't detect a quantized model until the quantization gets down to 4 bits or less.

Humans absolutely do detect quantization well before 4 bits...

Try it yourself if you want. Try a small local model, both quantized and not, and I would be very surprised if it isn't immediately obvious which is which.

But no, evals and benchmarks do not detect quantization, until around 4 bit quantization.

https://developers.redhat.com/articles/2024/10/17/we-ran-ove...

https://pub.towardsai.net/your-model-passed-safety-evals-the...

https://news.ycombinator.com/item?id=50422951

So only if you were "selected" for their experiments, your account was nerfed.

If this turns out to be accurate, I think that would be a pretty unethical thing for Anthropic. Even if it is just a "we had to allocate compute to this new model release".

It should be fraud to swap out the model. We need a term for this, something like a bait-and-switch.

if they just straight up dont sell you something you bought then its fraud.

I think there is a ripe opportunity for expanding the definition of fraud to include changing the "ingredients" of a named product on the market. It is not limited here, we see it with food, and even in the medical field

(I will die on the hill that medical fraud was committed when mRNA therapy was used as a treatment for covid and it was 'sold' to the public as a "vaccine" despite not being the same thing as any vaccine before it. The definition of vaccine was expanded to include it without sifficient public disclosure to warrant informed consent imo. This is not a statement about conspiracy or the efficacy of vaccines or a moral judgement on whether it was for the greater good or not.. it was simply an act of fraud that robbed the public of proper informed consent by making people believe it was X when it was really Y.)

So yes, I think there is a large need for the public to at the very least put a strong handle on the ability to change what a product name means. It is a trivial problem to solve considering it is only a problem due fraud or fraud adjacent activities.

heres the solution: You have your product name, and then any changes to it REQUIRE a new version name. Like how you have Coke and Diet Coke, only formalized such that Coke cannot legally replace the ingredients of regular Coke with the ingredients of Diet coke and still call it Coke. With Software they could at least use version numbers and release modifiers. most of the time its built in already, but it needs to include the full stack. if they change anything that has an interaction with the user it should change the label seen by the user for the product.

thank you for coming to my ted talk

I'm a bit disappointed with how this nonsense is spread across HN today.

Are you saying that the many, many people posting and experiencing this are wrong and you are right?

And not just in this instance, nerfing has been complained about in a cyclical fashion for many months.

But, they're all wrong?

You're so confident in your experience that you cannot think that other's experiences are also true?

"Could all these people seriously be wrong?" Yes, yes they could.

Right?

Eval context: 30 bug fixes and features evaluated by tests the agent didn't see. Ran on Opus 5.5 at medium effort in Claude Code.

20 of the tasks were last run on Nov 1, twice each. Nov 1 solved 36/40, and today solved 19/20. Thinking tokens per task were 0.95x Nov 1’s and output tokens 0.97x. The one significant change was speed: median time to first token decreased from 3.0s to 1.7s.

Caveats: one run per task today, medium effort only.

Opus 5.5 was so good that if it wasn't nerfed - at least just looking at the benchmarks - the difference in cost here does not seem to justify using this for anything besides design and review.

That's like maybe 1% of total token usage for SWE BUT a better design can have enormous benefits, so it's almost always worth using the best model for that.

The reasoning traces aren't available. The summarizer gives a short summary of what the reasoning traces do.

The changes were in the summarizer model. The Tweet doesn't even say that Opus was nerfed, everyone just assumed it because the summarizer started using "I'm..." more.

This mystery was solved on Twitter over the weekend. Watching the pitchforks come out on Hacker News on Monday over a situation that was already resolved and debunked is starting to feel like how LinkedIn catches on to internet drama about a week later.

Fable 5.1 Max $11.46

Fable 5.2 Max $44.70

Most people don't really have intuitions on what it means to push the frontier now (because AI has surpassed everything they are good at, for example, coding). We are truly inside the singularity now (using Kurzweil's definition, slightly paraphrased - the time when technology progresses so fast no one can keep track with it.

Sub 1% of the planet has any practical use for the top tiers of AI systems. That will likely always be true.

The gap between the haves and have nots will drastically widen further, and very quickly. The bottom ~95-97% will be left behind in a way that will be impossible to overcome. The age of AI + robotics will just make it that much harder to climb for most people, these are systems that are in aggressive, direct competition with human labor. And in short order the AI + robots will be vastly superior to human labor (it doesn't really matter if we're speaking in terms of 10 years or 30 years).

AI will consolidate the winnings. The gigantic gains by the elite rich related to AI over the past few years, is merely a hint of what's to come. Capital will win as it always does.

There will be some gains for most everybody else, those gains will be comparatively trivial though. A house bot that saves people an hour of labor here and there, AI that automates some trivial life tasks.

Had Opus make software for some local business and seeing it blew people's mind.

20$ aint cheap for us but I'm actually considering upgrading to the 100$ tier.

Am just jelly people are using 100-200$ subscription ^_^

We've sort of entered this twilight zone where most humans don't have the time, expertise, or moral compass to be able to use these models.

We're all going to be harness engineers in the future.

No, that will be the model's job. We will largely be unemployed. Of course, we can surely depend on the largesse of Altman and Amodei to keep us fed and sheltered.

Within three years most software developer jobs will consist of taking instruction from the AI systems. The humans won't see it that way, but that's what it will actually be. Realistically the thing that will happen is the humans will be put into harnesses (cubicles) and prompted by the AI (I need you to plug that server in; I need you to give me access to such and such; now do this, then do that). Most tech jobs will consist of AI telling the human what the AI needs the human to do next, de facto the human will be in a harness and will be getting told what to do.

Someone has to type in "create the next billboard top 1 single" ?

So whether that's a CEO at the top instructing a human manager that then instructs AI systems, which then put into motion plans that include the harnessed humans getting prompted, or however it's decided to best configure the top operational tiers - that's all fairly irrelevant ultimately. All human labor, and all AI labor, will be in harnesses. Realistically all human labor has always been in a harness.

The people in charge will be the people that control the capital. They will instruct the AI to their bidding, they will control the data centers the AI lives in, they will own the robots doing the labor in factories, they will own the factories, they will control the grid, they will control the electricity generation, et al.

It's true now, it'll be true tomorrow. Nothing fundamental will shift at that tier: don't be labor, own.

Sounds a bit like self aggrandizing elitism to me. I can imagine some self aggrandizing nerds saying the same thing 50 years ago:

> We've sort of entered this twilight zone where most humans don't have the time, expertise, or moral compass to be able to use these IBM PCs.

Now everyone use computer.

Haiku 3.5 formalized Fermat's Last Theorem for me in just 23 hours back before Anthropic nerfed it, this technology is a commoditized scam! Wake up sheeple!

Kurzweil probably didn't know the singularity is akshually 1000 schizo monkeys banging on a billion dollar typewriter.

It had seemed like progress was slowing last year as the juice ran out on moar parameters. We're well past that.

Uh, are you serious?

and just about "we cannot really predict beyond certain things" (Vinge's "Singularity" (1993) with its "opaque wall across future")

No they were exactly about superintelligent AI!

Vinge's 1993 article literally opens with this!

> What is the Singularity? The acceleration of technological progress has been the central feature of this century. We are on the edge of change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater-than-human intelligence.

https://frc.ri.cmu.edu/~hpm/book98/com.ch1/vinge.singularity...

Now imagine feeling like this, except not just about "AI events" but about everything around you.

Like, we're dropped into a post post post singularity world where AI already did all of that and is banned? as kind of a footnote that's never further explored in movie renditions that have been released

The semi-canonical Dune Encyclopedia (disregarded by Frank's son) differs vastly from prequels and generally points to it being a self-inflicted wound on humanity that opened the doors to widespread return of feudalism and essentially stopped humanity for a 10,000 years

You're obviously not a frontend web developer.

These upgrades are easy peasy. "oh, my developer chatbot is smarter? great"

Genuinely curious what that costs in terms of energy input and amortized computer cost vs. the previous version + a human's salary for 11 days.

Is this really so astonishing, assuming that the 5.2 training data certainly had the 'traces' from the Fable original run in it's training data.

What we're seeing instead is maybe akin to the green revolution. Before nitrogen fixation nitrogen was a resource constraining us humans. Lack of grit is what kept us from solving mathematical problems. However this lets us run into other resource constraints. A lot of technological progress does not solely hinge on thinking/processing. The speedup of creating new medicines with LLMs will probably be very small, as you still need to do all the testing. In software development you still need to first find out what to build in the first place and even mathematics where LLMs seem to shin brightest, it's becoming clearer that mathematics is not just solving mathematical problems.

Apart from that, a good coder is better than LLMs, shown by the amount of hand-holding needed. The only issue is that the amount of research an LLM can do and knowledge it is trained with far surpasses the amount of work humans would need to spend by hand, but we can also us LLMs for that.

I suspect we will see people being adamant that this model simply 'feels' better due to long horizon goal alignment improvements ( like this kind that would cause this jump)

Compared Opus 5.5 200 usd a month with Codex 200 usd (Astra High) and Kimi Pro 100 usd a month ... and now have a new conspiracy theory - more their suck your code the better outcome even just over few days time on large code base.

Horrible experience correcting course of Codex and Kimi flow compare to Opus. Opus much more responsive

Dont feel any gain of Codex 200 usd over Codex 100 usd

Kimi Pro feels like a fake Louis Vuitton bag with extreme high material quality and yet still fake ... a bit trash talk ... but clearly seen not so distant future where I can live without Codex and Claude

Well, must have been some business idea!

I almost feel like saying, "we do like to build things that are impressive, but there's really only so much I can be impressed". These models have redlined my "impress-o-meter".

I'd like them to now cure cancer instead of giving me endless 3d worlds and crazy games, etc.

https://www.anthropic.com/news/claude-opus-4-6 https://www.anthropic.com/news/claude-opus-5 https://openai.com/index/introducing-gpt-5-3-codex/

For every single announcement, not only do the top models always score 60-80%, the older models somehow start scoring 20-30% (even though previously they were scoring 60%). It's completely unclear whether the methodology changes, how these scores are even evaluated or which domains are being tested. What's worse is that the "benchmarks" keep being updated with no explanation or due process.

I wonder how long this kind of excitement at new models will last? I mean, now when I get a new iPhone I don't get the same rush I used to. So I wonder if this hype feeling will fade as we plateau in model capability.

- Anthropic is finally well balanced across models. Meaning: Opus 5.5 is so good that I do not feel Fable FOMO as I did when they were paired with Opus 4.8/5. And Sonnet sometimes makes me forget about Opus.

- Fable is good, but you really have to push the ambition of your prompt in order to see proper separation from Opus 5.5.

- It’s way faster and more reliable: one of the worst things about fable 5.1 was how slow it was an, on top of that, how frequently it would suffer from max output token overflows. I got none of these issues with 5.2.

- Fable is truly exceptional at drawing now. Fable’s ability to render an image with JavaScript actually has me questioning the value of dedicated image models. I prefer the images I’ve gotten fable/opus to make for me over the gpt-image lineup, it’s wild to think we’re at this point.

I love seeing this verbalized. We are pretty much at the point that increased model capability will only have utility to folks that are willing to let go of even more understanding of whatever the model is producing.

The best models can/will produce a year's worth of output that would take several days to weeks to properly understand in the span of an afternoon.

For everyone that still wants to feel like they own the end result, just use the lesser models with less ambitious tasks.

Big caveat: I think this mainly applies to coding. I would still like the best models possible to be working on e.g. new drug research to cure our various ailments.

I wish I had a bigger N so I could rely on that experience a bit more, but it sure does feel like I'm not really contributing anything in terms of "Data Science expertise" in my own research right now.

But then I'm someone who hasn't found Opus 5.5 good for much beyond web UIs and related frontend development. I find it more forgetful than GPT-5.6 or 6.1 or Fable, so despite having a 1M context window it gets lost in a big codebase or goes of target. Opus 5.5 doesn't manage agents particularly well IMO.

I'm surrounded by people raving about Opus 5.5 and God only knows why I'm not getting their results.

I’ve been shifting away from using more traditional GenAI models in favor of just giving LLMs access to a custom drawing language (LOGO-like) to generate imagery and its been going shockingly well - Opus 5.5 in particular.

I put together a simple LLM driven skill that creates old adventure game (EGA) environments from simple pencil sketches and it works really well. And being procedural, they're much easier to make surgical changes to without degradation.

https://kq-styles.specr.net

I've been using Opus 5.5 on xhigh for some decently aggressive software development on a ~125k-line project, and it has been fine for me. I'm not really willing to dive into a "rewrite the whole project, don't make any mistakes" level of refactoring. I'm not looking to try to prove Fermat's Last Theorem myself. Is there anything that I'm truly missing?

For Fable, I really like to think of it as the heavyweight reviewer whenever I'm about to start so I save myself some security and/or glaring technical issue.

It could be flat web app with frontend -> api -> db later, or some very complicated system with many components and edge cases. Imo, in second case, Opus still have lots of struggle to understand system as a whole.

I guess I do have an outstanding hard problem where the SQP solver crawls across a flat-landscape. Opus 5.5 correctly tells me that is the problem, but isn't very useful in solving the problem (it has helped a bit with solver tuning, but it doesn't really know how to fix it). I have a hundred other bugs and refactoring I can do with Opus 5.5, though, where it happily chews through pretty complicated tasks for me with reasonably light hand-holding. Maybe this Saturday I take whatever credits I have left for the week and throw Fable at it and watch them evaporate.

It looks like you are working on some experimental system which is not critical atm, so you can mess around, create/fix bug, build prototypes and check them, do research of approaches, etc.

Now, imagine you have similarly complex system, but every second of downtime or bug produces lots of damage. In this case, you can't afford low intelligence vibe coding.

The fact that you deal with hundred of bugs, means that your Op5.5 won't work for second case.

I am also happy with this current state, as I believe it will at some point allow me to transition to open models once they reach the level of opus 5.5. high. But its probably futile to try predict the future in these times.

That's true till it isn't, though. It's why it's always worth trying stronger models when they come out.

Eg. https://tfmbot.com is an implementation of a popular board game. Opus and even sonnet handle that part. Each week i spend my remaining credits on fable to think how to improve the ai opponents. It plays at a very high level (can get into the top 100 on BGA if i let it). Opus alone couldn’t get it to the level it’s reached.

It is for hard problems. Mathematics, Physics, complex software architectures and designs like large scale distributed systems. Things that you've been trying to do for 10 years and fail at every single day, and keep waiting for a smarter model to come along to see if it can solve the problem. That is what Fable (and Gemini Argon/Carbon) are for.

Don't use them to make a web page or a rest API. That is a waste of money and time.

But the improvements in how well the model does the work are noticeably better with every good model release. As of now, orchestration of agent pipelines is ALMOST so good that I can point it at a problem, and it will solve it safely and reliably without making a mess. But it requires an agent pipeline with context gathering, refactoring, attack sessions, a sprinkle of Sol 6.1 for bug hunting, and Sol 6.0 for Daybrak security audits, and a final de-bloat/clean up pass. I’m feeling that we’re getting close to doing a lot of the work fully agentically. But I can absolutely "feel" that we are not there yet when it comes to the intelligence I need. I need moar!

- How did they produce such a bizarre effort-intel curve for Carbon that goes against every available benchmark?

- Why did they leave Carbon-- positioned primarily as a cyber model-- off of the single cyber benchmark comparison?

- Why do they switch to comparing against Argon (rather than carbon) for robodojo?

But I suppose the reality is rather obvious when you note the funny games they're playing with the graphs, and the things they won't talk about (where's Astra on that CVE chart?)

https://www.youtube.com/watch?v=NO2I3ueVQrk

And they have one on fable 5.2 that was just released, yay

https://www.youtube.com/watch?v=QwaU4yU0PdQ

Because on that specific benchmark (FrontierCode 2.0), Medium is Carbon's best effort level and xhigh is Fable 5.2's best effort level. You can see that on the graph your next point is about.

And it's just that one benchmark that's at medium. "All scores show each model’s highest-available effort unless otherwise noted".

> - How did they produce such a bizarre effort-intel curve for Carbon that goes against every available benchmark?

The graph is labeled as being about FrontierCode 2.0. Those appear to be Carbon's scores on the official FrontierCode 2.0 leaderboard: https://cognition.com/blog/frontier-code-2.0

> - Why did they leave Carbon-- positioned primarily as a cyber model-- off of the single cyber benchmark comparison?

> - Why do they switch to comparing against Argon (rather than carbon) for robodojo?

Because no numbers are available for Carbon for those benchmarks, and the model isn't publicly available so they couldn't run the benchmarks themselves?

I am referring to the chart that cross-compares multiple benchmarks, which Artificial Analysis clearly shows Carbon-high as superior on. Further, AA's FrontierCode chart places Carbon-high as having a higher score on Frontier 2.0 than Carbon-med.

Where it gets very interesting is when you look at the Cognition "FrontierCode" publish date (2026.10.27), and the revision as of 10.28 in web archive (https://web.archive.org/web/20261028175237/https://cognition...). This revision has a VERY different Carbon effort / intelligence curve where high beats medium.

It is only with the latest revision, as of today, that all of a sudden Carbon's benchmark curve goes cone-shaped. Curious, indeed....

Do you expect that nobody at Anthropic has such access? Trillion dollar company can’t manage this basic level of opposition research?

The disdain frontier labs have for their users really sucks.

Asked it to review a 80 kLoC codebase, something Opus does just fine, while asking it "max 4 subagents". Fable 5.2, like the well-aligned model it supposedly is, decided to spawn 6+ nested subagents, because these aren't subagents, apparently - resulting in 100% of 5hr usage being drained in less than 20min (on $100).

Obviously. Well, we know what's the "secret sauce". AI models building better AI models and better kernels to optimize inference and training.

Not sure if this is just a coincidence that I had some easy changes queue up for this morning. But it is flying through the list.

Fable 5.2: https://html.non.io/lcars-fable-5.2/

Opus 5.5: https://html.non.io/lcars-opus-5.5/

Source designs: https://diffui.ai/app/canvas/5093e689-1e74-4f26-b632-2a4500f...

Thoughts:

The best implementation wrt being as true as possible to the source design. It nails the flowing svg shapes. Icons are more accurate to the design - note the difference between the starship svg in the "Legendary platforms" card between Opus 5.5 and Fable 5.2. The 3js planets outside the windows are also really well done, especially the bottom one which has a nice background glow to it.

It isn't without issues though - the flicker effect it added to the S shaped svg that weaves through the page is distracting in my opinion, and I actually thing the motion animation in the Opus 5.5 one is better overall. Still, it nails the implementation of this page, but it's no surprise considering how good Opus 5.5 was at this.

Disclaimer / reminder: the objective of this test isn't to have a well designed webpage, it's a synthetic test that pushes the limits of LLM's vision capabilities. Traditionally LLMs have issues with smooth curves, svg animations, and non-boxy layouts, which this design pushes the limits of.

This would've been caught by a junior web developer or just an everyday Joe glancing at the page for a few seconds

I guess AGI remains two weeks away..

Still in progress, but early demo showing difference zones: https://non.io/video/fidelity-lens.mp4

Download Chrome just to check some minor ui bug? Sure

Check git for 0.5s before claiming that we're on some branch (incorrect one) with no commits pushed (incorrect one)? Sorry, can't do

Do we need new benchmarks or are the models just plateauing in general? Or is it something I just don't see?

https://artificialanalysis.ai/

GPT-6.1 Sol costs $1.27 per task, whereas Fable 5.2 is $26.54 per task -- a ~21x price difference.

Fable 5.2 is not 21 times better than GPT 6.1 Sol, I find it hard to justify this.

6.1 sol is also an exceptionally cheap model in your comparision. On 6.1 sol high is $0.59, which is actually the same price as Deepseek 4.1.

If you are using Opus 5.5 I'm curious if it is because 1) your workflow is just stuck on it, 2) you don't care about money because you ain't paying it or 3 )do you think you get better results.

If you 3 please let me know your experience and how you actually measure it, or if you just feel it is better.

I've played with some of the open models (GLM, DeepSeek Flash), but none of them have matched the results I get with Opus or Astra or (and a big jump down here) Sol.

Seems like a new focus is electronics design, which is both interesting and a little sad to me. Manually designing electronics are one of the things I found incredible comfort in doing; seeing AI emerging in this space gives me mixed feelings.

  Safeguards

  AI research and development. The model that underlies Claude Fable 5.2 and Claude Mythos 5.2 is a step forward in general capabilities. However, during the model’s training, we took steps to limit advances in the specific research capabilities that could most directly accelerate AI development. Thus, in relevant areas, including accelerator kernel optimization and improving large language model architectures, Fable 5.2 and Claude Mythos 5.2 perform at about the level of Opus 5.5.

  Importantly, we ensured that the training changes did not affect related capabilities like agentic coding, hardware development, and traditional AI or machine learning research (including interpretability research). We do not alter these capabilities as the model is being served to users (that is, at inference time). As described in Section 1.5 of the system card (https://www.anthropic.com/document/claude-fable-5-2-mythos-5-2-system-card) and similarly to previous models, Fable 5.2 and Claude Mythos 5.2 also include transparent fallbacks to Opus 5 for a small set of topics in this domain, including kernel development on certain nonstandard accelerators.

To be clear, they explicitly say they are only targeting very specific capabilities, like accelerator kernel optimization, and that broader hardware development capabilities are unaffected. But 1) even that is pretty bold, especially the part about kernel development for nonstandard accelerator architectures, and 2) I worry that this could eventually expand to more areas of comp. arch., high-level digital design, chip design, and EDA.

My speculation is that, as these kinds of chip design and EDA capabilities become increasingly important to frontier AI labs and their competitive advantages, there will be more incentives to restrict them.

I don't think they've explicitly stated anything like this before regarding restrictions on these specific capabilities, but I could be wrong.

> CVE Rediscovery is an internal cybersecurity evaluation that assesses how many published CVEs (Common Vulnerabilities and Exposures) a model can independently find when given a snapshot of the project before a CVE was fixed. Our October 2026 run against 1,829 CVEs showed Claude Mythos 5.2 outperformed Claude Mythos 5.1 and Claude Opus 5.5 at every effort level.

> published CVEs

So by definition this is just measuring how many CVEs (and/or explanations / PoCs of CVEs) the model has memorized. What are we supposed to learn from that?

I'm not a mathematician to use Erdős problems for testing. I'm also not willing to spend buckloads of tokens just for a test. What is simple and quick but telling? What tasks are not saturated by previous models? Do you have a task you give to all the new models?

Responses aren't deterministic, and we know behaviour can change over time whether it's tuning after launch or some kind of load sharing at times of day when lots of people are hitting the servers.

I think you just have to use it or let others use it for a while and make judgements from general reactions on whether or not it's worth trying.

So, the difference between them is just the harness?

* Fable designed three electronic devices that worked after human assembly

* Fable solved two black-hole problems and Mythos predicted whole protein shells

* Fable controlled robots to unscrew a cap, solve a cube and draw

https://smalldocs.org/p/claude-fable-52-and-mythos-52-releas...

Why even rank Fable 5.2's capability in Cybersecurity if you won't allow us to use it for that?

https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

max is in there too, but it ran out of reasoning tokens (128,000 limit) before it got to the actual SVG.

Interesting how low detail these were all up until xhigh.

High gets legs correctly, but screws up the layering on the chain.

x-high is very good but there are minor laying error with the front chainring.

So they are nerfing ML research.

What I have learned is that any model from Anthropic/OpenAI is at it's absolute best right after release.

Any bugs found are looped through agents to fix, and the cycle repeats. Why dig into the code unless for hobby? Just as people don’t write assembly anymore unless for hobby (or learning)

Only exception might be the upper echelons of optimization, like language authors or compiler level work. But even then…