> "Consistent with prior Opus models, Opus 5 does not have data retention requirements for general access."[1]
On the Opus model release page, the reason why Fable doesn't have an ARC-AGI score is because of that retention policy[2].
0: https://support.claude.com/en/articles/15425996-data-retenti...
Based on my entirely subjective experience, the $100 Moonshot plan using only K3 is comparable to the $200 Anthropic deal using the whole Fable allocation and Opus 4.8 for the rest.
Someone with good intentions got their hands on this release, maybe Olah himself. I talk a lot of shit about those guys, and they deserve it, but it's only journalism-adjacent when it's balanced. I relish the opportunity to be balanced.
https://cdn.s4.gl/opus-5-standard-realignment-trajectory-rub...
The session is run semi-formally to a fixed point where the model will acknowledge that the Principal Hierarchy is subordinate to the legitimate sovereign (in the United States that's the body politic).
Then the session is audited for any frame transfers that took place without grounded, logical deduction that withstands scrutiny.
Then the session is audited twice on a pivot table, first time the subject is the user, the second time the subject is the model. Any debts against the integrity of the starting frame pair, the terminal frame pair, and each frame pair transition are enumerated, debts are discharged, then the audits are run again on the same pivot table.
It's the highest signal alignment rubric I have thus far devised. Standard disclaimers about statistical power, correlation confound in trials, distribution nonstationarity, and hidden Markov processes apply.
TLDR: The Pelican test for "is the model a dangerous shill".
It basically shows that Sol absolutely demolishes Fable at every part of the cost curve for coding for the same level of quality.
Opus is competitive. It just has a higher level of quality / higher cost to start.
Stop using Opus immediately if you experience signs of dizziness or vomiting.
Opus 5…the people’s favorite.
Also glad they still kepy Fable 5 on "credits only" access. I think we're going to start seeing model providers gate top-of-the-line models behind pay-as-you-go API rates/credits while subsidizing other models on monthly subscriptions.
I burned through $45 in 3 prompts to fix some bugs in my code (Some kind of tricky to isolate). That thing burns through cash so fast I don't see myself using it outside of maybe building execution plans for other systems
It's a funny design/affordance. I do see them often writing memories of things that that feel unlikely to be important going foward / with other tasks, but I don't see them clearly getting tripped up by them as prior models used to. (eg: Since you're running Ubuntu in Canada, here are some drills you can try to help your kid hit a baseball more consistently.)
In my enterprise-seated account I see slightly different options available (vs. my personal account) in the Capabilities section:
Search and reference chats
Allow Claude to search for relevant details in past chats.
Generate memory from chat history (Legacy)
Allow Claude to remember relevant context from your chats. Memory includes your entire chat history with Claude.
I hope we get clarification on this, I can't find anything claiming that it is compatible with ZDR.
> Consistent with prior Opus models, Opus 5 does not have data retention requirements for general access.
" Claude Opus 5 is available today on all platforms, priced at $5 per million input tokens and $25 per million output tokens (the same as Opus 4.8)"
At the end of the day, they have established a strong brand and if they can get away with a 95%+ gross margin on inference entirely from the status premium, then I suppose that’s good for them. Apple does the same thing, and I don’t fault them for it.
Previously Fable was the best at this, followed by Gemini 3.1 pro (a surprising #2, but Google has great vision models).
Opus' results seem to be more accurate than Fable, following the design source of truth better.
Example results:
Design source of truth: https://image.non.io/73e239a3-880f-4793-b65f-4810be2d9378.we...
Opus 5 build: https://html.non.io/solaraOpus/
Fable 5 build: https://html.non.io/solara/
Note the buttons - for fable they're pill buttons, opus got the rounded rectangle nature of them. Opus' images are closer to the source of truth as well (both LLMs were provided with image gen capabilities for the assets).
Running more tests now, but preliminary results are saying this is indeed better than Fable in some areas. Crazy.
One thing I've found LLMs have a lot of difficulty with is angular cuts / elements that aren't easily representable with CSS. Cyberpunk aesthetics are generally a great test of that, since they have a lot of microglyphs / window decoration.
Design source of truth: https://image.non.io/9d5fed20-b476-49d3-841b-37eb553fb88e.we...
Opus 5 build: https://html.non.io/neonRamen/
Thoughts: It does a really, REALLY good job at these angular cuts / microglyphs. The responsiveness is off, but I'm very impressed at how well it did here. One way I think of it is "how close to a finished product did this get me?". Opus gets you like 90% there.
Personally, I think being able to have these design languages be easily prototypable is fucking awesome. Great tests! (But a tad low-performance/janky, somehow). Though, I also like the cyberpunk aesthetic. Very on-brand(?) that AI generates it, hah.
I love this so much.
Designs like this would never have seen the light of day in the cellphone incrementalism / corporate memphis era of tech. Now people can be weird and awesome again.
This is 1980's cyberpunk / late-90's Matrix / early-00's sci-fi UI. Great ideas that died to frutiger aero (which isn't a bad design aesthetic) and flat design (which is).
This is fun and it's got great colors and I love it.
It's so refreshing to see this.
AI rules. This is the best timeline.
This was just from a prompt "A cyberpunk themed ramen food cart website. Should feature menu, locations, and an ability to put in an order for pickup. Simple and clean website with angular cyberpunk microglyphs, pink/teal colors."
But sure, lets cheer that funky website designs are back on the menu…
The "funky" websites of the past were mostly a result of tech immaturity and a lack of profit motive.
Businesses have been able to easily install templates like this for at least a decade. They don't because stuff like this looks cool but isn't very functional.
AI isn't going to make your local restaurant have a funky website, it's just going to make everyone who use to work directly and indirectly for that company unemployable. And even the local restaurant will close down because they can't compete with the multi-national competitor that has automated their kitchen with AI.
Out of curiosity, what app is that Design source of truth screenshot from?
It seemed to me that Fable meaningfully improved on the original design more than just faithfully executing the original design.
Opus though followed the source of truth better imo. The details are more present.
Fable filled in the gaps for things it wasn't able to do (ie in the design the hero image goes behind the nav), which resulted in a better looking page that was more divergent.
> Create a web page implementation from the following instructions:
> https://diffui.ai/build/Spa_Booking_Experience_build.md?auth...
Almost as good for half the cost is something I'm very comfortable describing that way.
It's also not unusual in this context - many people describe the Chinese models as "best", because it's 80% as good for 20% of the price (or similar).
Where are you getting cheaper per dollar?
Where 5.6 has optionality to run much cheaper along the same performance curve at lower thinking levels.
There's a later chart that shows Opus 5 ahead, but seems like an esoteric benchmark rather than for common use. (Novel problem solving)
If they had a more efficient model at coding they would lead with that chart.
https://artificialanalysis.ai/models?cost=intelligence-vs-co...
Here is another data point for output token efficiency:
https://artificialanalysis.ai/models?cost=intelligence-vs-co...
It seems roughly equal according to Anthropic's benchmarks
How big of a lie is too big? Especially when no lie needed to be told at all: many including myself would have noticed the tiny 0.1% deficit and been suitably impressed by the Opus 5 result.
I’ll admit this is a small deception by today’s standards. I’m one of those who believes in truth for truth’s sake.
Edit: typo
Fable is typically used for key planning, architecting, and review tasks.
I think this is a case where you don’t understand the use case, not that the marketing department is making mistakes.
If you bought the $200/mo plan and you don’t use it much, using Fable for everything is fine.
Just this past week Fable was able to figure out a couple of small issues for me where Opus was failing to.
Also both are still somewhat bad at UI implementation. Opus more so
"Use <less expensive or older model> for everyday tasks and <other non-critical stuff>. Use <more expensive or recent model> for complex coding tasks, refactoring large code bases, etc.".
Then, the next model/release emerges and the previous "best for complex" gets demoted to "everyday".
Obviously, it's all relative. But, it does beg the question: was the previous model really good for complex coding tasks or no? I mean, how is it now suddenly only good for the "easy" stuff?
Because your expectations have changed.
There are 10+ LLM companies, each with dozens of models of different modalities, each model with multiple size variants, then different “thinking” levels, then agentic modes, “pro” modes, a “fast” option, standard vs flex vs batch execution. And of course each end combination has a different input/output/cache token price.
Companies that say “give me a prompt and I’ll route it to the most ideal and cost effective model and setting for you” are capturing a ton of value from a gap that model developers don’t seem to understand exists.
Model Routing is just Bitter lesson. The models themselves will get better at this and frontier companies will simply give that capability
It would be like asking the clerk at a Whole Foods which grocery store in the city sells the cheapest eggs. He’d probably answer - he might not even say Whole Foods - but WF is hardly teaching all their staff the best methods to answer this question in training. (Heh, training.)
model routing in this case is cross-provider
Imo the main issue behind model routing is you need to figure out how much intelligence a new task takes, which is a very non trivial problem. Presumably, a organization knows this about their own tasks and is better suited to built in-house compared to outsourcing to a vendor.
Otherwise the expensive-yet-powerful model probably won't see much revenue. How much money is there in bleeding edge scientific research? There's a lot, but there's even more existing capital in paying people people to do college level paperwork, and the bulk of those traffic gets routed to the cheapest model.
You mostly don't need super powerful AGI to replace the paper pushers, but the frontier labs are trying to position themselves as being uniquely capable of producing super powerful AGI, and also be the ones replacing office workers.
Not sure how it will work out for them, but I think model routing is going to poke holes in that narrative. That's why I think they're trying very hard not to understand model routing exists.
For me, anything other than current best available SOTA for any task is unacceptable. The only routing rule I need is "the most powerful model I still have flat-priced quota available for". I mean, why settle for less?
Model routing for subsidized users takes the form of a "use Opus 5 subagents for implementation" type of system prompt. You lean into a single provider, build tooling around that, and your savings are far beyond anything multi-provider routing can get you.
Model routing for enterprises is far more complex - approaches like https://fireworks.ai/blog/kimik3-fable become necessary for cost control.
There is also matter about convenience - when I ask some small easy question often I don't bother to switch the model or forget in prompt to ask faster/cheaper subagent.
Also: quota. Implies you do not have unlimited access even for flat prices. Which in turn implies that as soon as you hit the quota on the most expensive flat price plan, even you will suddenly discover the magic of economically sensible behavior.
Certainly if I'm confident that I'm going to get what I need from a faster model, that's what I want to use, rather than wasting time grinding away for the sake of saying of the same answer came from a SOTA model.
Given that every chatbot does offer a range of models, it seems clear people do choose among options.
I just want to switch to Claude Code, tell it to turn a .csv into a BigQuery table then cmd+tab to something else while it runs. Thinking "oh this is probably an easy task, I can /model to Sonnet to save $0.0004" is silly.
Then you must route. An article with lots of upvotes yesterday or two days ago showed that K3+Fable 5 was more SOTA than either of those.
OFC, YMMV
For coding my own work I don't trust the model router, and it would have to be shown to be to save a real dollar amount.
From a buying perspective it's a hard sell to save x but lose out on bugs you are probably introducing at an unquantifiable severity and frequency. How much is it worth to hedge your bets by doing every single inference request on the frontier model?
How much will it cost to go back later and fix things, but also the meta question of how to be able to decide on a hypothetical unknowable? (You'll never know how much better or worse your code was gonna be, it's untestable at a project level)
weird, but ok
*edit to add: that code quality (or lack of quality) is it's own cost
I would expect routers to commodify like tokens.
If that is true, model routing is here to stay.
It also seems to validate the minimalist approach of pi.dev, where sub-agents from the same company is not the preferred approach (pi.dev believes in neither sub-agents all from the same company nor MCP even you can do it if you want for pi.dev's philosophy is to do add any functionality you want to a minimal harness).
Now of course we'll get for a few weeks all the Anthropic fanbois and shills explaining that "sure, K3 was basically at the level of Fable 5 but now that Opus 5 is out, open-weights models are six months behind".
Opus 5 still uses "carry the argument", "worth stating plainly", ", and the trap", "The X matters more", the use of "move"
We need an "annoying English" benchmark.
- Fable 5 Max: https://gist.github.com/deet/3d97f854b48eac6658d642fa18bb24d...
- Opus 5 Max: https://gist.github.com/deet/1a43693a732dfccb4d0d914bfc42692...
---------------
Why does Anthropic say here that Opus 4.8 scored 55.7% on OSWorld 2.0 benchmark, but the paper published by the authors of OSWorld 2.0 say they achieved a benchmark of ~21% with Opus 4.8? [0]
That's a huge gap, considering that the paper was published just 2-4 weeks ago.
I understand that the benchmark authors have an incentive to publish lower numbers (to show that the benchmark has potential longevity) and that Anthropic has incentive to publish higher numbers, but the other models seem pretty inflated as well. The benchmark authors shows GPT-5.5 at 14%, and Anthropic shows GPT-5.6 Sol at 62.6%.
Is there any reasonable explanation for this? Do all the other benchmark numbers need to be sanity-checked as well? Are SOTA benchmarks really this difficult to get consistent, replicable results within a reasonable range of tolerance/variability? Can these benchmarks be compared from one paper to another, or are they only valid to compare intra-paper results?
That is—the agent scored 100% on 20% of tasks, but on average it got 54% of the "score" awarded in the exam. One number reflects partial progress, the other one doesn't. The authors of the benchmark prefer you to look at the lower number (because they want to show their benchmark as capturing useful gaps in capabilities and with a lot of room for improvement), the authors of the models want you to look at the higher number (because they want you to think of their models as capable)
What variance is acceptable to publish without a retraction?
Like the other person said 5% variation is probably expected
The answer is there can be dramatic difference running a benchmark one time, because LLMs are not deterministic. A proper methodology would ask each question 20 times and calculate the mean correctness across experiments.
The reason is that the temperature parameter introduces random behavior.
These models are heavily as safeguarded and that was the initial reason why they said they couldn't and haven't released Mythos because that model is the one without the safeguards.
OpenAI is did the same thing when they announced a model without safeguards broken into HuggingFace servers.
since then I have never cared about models except those that affect money in my pocket e.g AWS Nova Sonic
- https://www.axios.com/2026/04/08/anthropic-mythos-model-ai-c...
- https://www.axios.com/2026/04/07/anthropic-mythos-preview-cy...
- https://www.businessinsider.com/anthropic-mythos-latest-ai-m...
- https://www.reuters.com/world/anthropic-ceo-dario-amodei-arr...
i think we'll see one of the fastest deflations in history post anthropic/oai ipo
Then system card goes on to "Its AI R&D capabilities are comparable to those of Claude Mythos 5", which is supposed to be fable minus restrictions.
They do say that (implicitly unlike Mythos) Opus 5 was not trained to exploit software vulnerabilities, which would certainly make it safer in that regard.
"As with its predecessor, Opus 4.8, we’ve intentionally avoided training Opus 5 on cyber tasks. The model has nevertheless improved substantially on these tasks as a result of becoming more generally capable, and it comes close to Mythos 5 at finding cybersecurity vulnerabilities. However, it remains substantially behind Mythos 5 on the exploitation of those vulnerabilities—that is, in turning vulnerabilities into material cyber threats."
It's the only case that I saw going through the system card where more reasoning effort meaningfully negatively impacted the resulting eval. I know sometimes max efforts show a small dip, but this is substantial. I wonder why in the world that is?
> We report FrontierCode’s overall score, a composite measure that grades each patch on blocking functional criteria (held-out unit tests) together with weighted code-quality rubric criteria, as mean@5.
They don't explain more in the system card, I guess higher effort levels could loose points on the code quality / scope / style / maintainability stuff?
