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Appears to be benchmaxxing
"Opus 5 states the hidden rules before its first action, then plays a byte-identical optimal solution in 5/5 seeds at temperature 1.0. Zero exploration."
I guess there is no way this can happen without benchmark being part of the training data??
I have been claiming that I don't think Chinese AI companies are benchmaxxing harder than American AI companies, which has gotten mixed reception: sometimes people agree, sometimes they disagree.
It seems I was wrong. American AI companies might actually be benchmaxxing harder.
I was just thinking they need to mark each model per benchmark as "model released before the benchmark was released" and "model released after the benchmark was released".
And being worst than previous model...
"...The traces tell the why: (1) On our most classic Witness-style game, Opus 5 states the hidden rules before its first action, then plays a byte-identical optimal solution in 5/5 seeds at temperature 1.0. Zero exploration. It already knows this genre. (2) But on our most novel game (unusual mechanic combinations you can't pattern-match), Opus 5 regresses below Opus 4.8. Where rules must actually be discovered through interaction, the new model is worse than the old one..."
>That decomposition (perfect on templates, regressed on novelty) is the signature of “scaffold-then-internalize” training on genre-specific data, not a general gain in interactive abstract reasoning.
They're smuggling a claim that benchmarks like ARC-AGI measure "interactive abstract reasoning" here, which is what is claimed by the people that make these benchmarks, and also not proven.
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I'm shocked, astonished even, that enterprises on which trillions of dollars are being poured would consider cheating on marketing benchmarks.
The last time I checked, for the arc agi 3 leaderboard, the models are given a simple prompt and the game input and asked to play the game, no harness/tools. If harnesses were allowed, I would expect the benchmark to be saturated. There were a few harness attempts, but they could only be evaluated on the public set, so it's not an apples to apples comparison.
My guess is, the large score jump for Opus 5 is mainly because of getting the right RL envs for training.
It's becoming harder and more expensive to build and run meaningful benchmarks, it would be interesting to see what they do with arc agi 4, maybe just give it gameboy/steam games and see how they compare vs a human baseline? The latency requirements and very long horizons in games could be an interesting challenge for llms.
The exclusion of harness's feels really weird given that companies are recognizing the value of what harness's can do. By excluding them the benchmark is becoming less relevant.
Why Anthropic models are always leapfrogging these benchmarks, but in real life work I do feel like after 3 weeks I am back to Claude Opus 4.5? (regardless of the model I use, Fable was exception for 1 day when it was released)
It could be that the set of your day-to-day workload which could feasibly be accelerated by AI just happens to be saturated around Opus4.5, but you can still see lots of “reasoning” which makes you think the model is more performant in the first days of use. That’d mean you couldn’t perceive any meaningful difference in more powerful models’ results, even though you can see a difference in the raw output due to the length of reasoning traces leading up to the result.
So for example, if your workload was literally just addition of sets of numbers, you’d never have noticed progress in the result beyond GPT3.x level models. But you would perceive a difference in the now-Tolstoyan length reasoning text accompanying the result.
Some 20 years ago, the telecommunications sector in Germany was liberalized. Many telephone card providers entered what had previously been a barely competitive market. They advertised their products with aggressive claims like: “Buy our €10 top-up card and get 660 minutes to destination X.”
For the first few weeks, they would actually provide those 660 minutes to establish trust in their cards. But after a while, they would quietly start reducing the number of minutes on subsequent top-ups—say, from 660 minutes down to only 300. They wouldn’t do this for every card, so it was difficult to prove. Instead, they relied on averages across their customer base to make the economics work.
Lately, I’ve found myself wondering whether something similar may be happening with frontier AI models. Companies launch with an exceptionally strong model and generous compute limits to build adoption. Once the model is established as a market leader, the incentives change, and users may start perceiving the service as becoming more constrained or less capable over time.
I don’t have evidence that this is what’s happening with Anthropic—or with any other AI company. It’s simply a pattern that the current situation reminds me of.
It's called frog boiling.
We get used to the new level of intelligence so fast, any deviation feels like going back to the stone age.
If you don't believe me, create something complex with Opus 5 and then with Opus 4.5, and notice the difference.
5 seems incredibly smart to me in my conversations today about some pretty niche ideas in.longitudinal modeling. It.felt.like a big step up.from 4.8, to me
Well, what kinds of things do you see Opus 4.5 completely fail at? Maybe those are not the ones that newer models have improved on.
Going to call it user error if you find Opus 4.5 better than 5, sorry.
Same, like I prefer 5.3 codex over the “stronger” models.
I honestly just use GPT models nowadays, Claude models are too restrictive and more of a quitter and fable/whatever is just too expensive to be worth it.
Enshittification.
I've worked with these systems for four years now and they have not meaningfully improved in that time frame.
We still have:
- statistical correlation between two things will always cause one thing to lead to the other, no matter how much you prompt it to not have that connection (to be expected with a stochastic system)
- Math completely fails in longer contexts
- "thinking" token generation being on the correct track just to 'no, wait' on an already correct conclusion
- smearing of properties between logically distinct objects (a red ball and a green cube can quickly become a red cube and a green ball)
ARC-AGI-3 launched a few months ago which would suggest that prior models likely had no knowledge of ARC-AGI-3 or training on similar challenges.
I could be wrong, but given the large outsized jump solely in the ARC-AGI-3 score, it would suggest that the model didn't become significantly more intelligent overall, but significantly better at solving those specific problems.
This could mean one of two things (I think):
- Opus 5 was not benchmaxxed on ARC-AGI-3, but has benefited significantly from discussions about the various challenges and mechanisms deployed in ARC-AGI-3 such that it has far better heuristics to solve its challenges.
- Anthropic looking for buzz around their latest model picked a well regarded benchmark with significant room for improvement and focused some of Opus 5's training compute on ARC-AGI-3-style problems.
Or it could be some combination of both. Personally, given how much of an outlier the ARC-AGI-3 jump is I struggle to see it being the product of a significant improvement in general intelligence.
> it would suggest that the model didn't become significantly more intelligent overall, but significantly better at solving those specific problems
We would actually need a test that shows the ability of a model to export its skills to more problems ("interdisciplinarity" etc.).
I also noticed that Opus 5 doesn't show a corresponding gap on the ARC-AGI-2 leaderboard. There is a significant increase in performance between Opus 5 and 4.8 on ARC-AGI-2 though.
> Only systems which required less than $10,000 to run are shown. (Notes[1])
Am I lost or are their many models on this ranking (Opus 5 included) that clear this?
Many models are much cheaper through their subscriptions' included usage. That could be what's happening here.
Claude gives you something like $5000 of tokens on a $200 plan.
Why is there no Kimi 3, and GLM5.2 didn't run the third benchmark? I am more interested in knowing the abilities of open weight models.
ARC-AGI is a beauty contest for pigs where the pig's owners compete to see who can apply the lipstick most convincingly.
Also top on the freshly released Frontier-Bench, by a large margin: https://www.frontierbench.ai/
I have a suspicion that they are just trained on puzzles by now
Why is Fable not on here? I wish Fable hadn’t come out because it’s taking the wind out of every release because that feels like the cap above which the US government will not let LLMs improve anymore and everything they’re releasing from this point has to be worse than that.
It's actually crazy to see the difference between opus 5 and the next best model on ARC AGI 3 when you actually look at the ARC AGI problems
ARC-AGI3 doesn't seem like a great benchmark to me in the first place. It assumes a lot of human like tendencies which an AI either shouldn't or wouldn't have. Particularly in the genre of "gameplay" where unspoken assumptions from prior games inform our understanding of rules.
I think it's way too easy to be deceptive with these benchmarks now. You don't even have to "train" the model on a new variant each time. The base models are powerful enough. All you need is a naughty little markdown document that provides explicit instructions regarding how to solve the new puzzle variant, and a willingness to be deceptive about the presence of that document.
If you want a know why the model providers are locking down and encrypting their reasoning process, this sort of workaround is potentially why. You can play this game of whack-a-mole indefinitely if the state of the system is concealed. They could have added something like:
> ### When solving arc-agi-3 puzzles: First convert the grid into a scene description. Identify connected components, colors, shapes, positions, symmetries, repeated structures, and relationships between objects. Do not reason directly from individual pixels... use this python script to help blah blah...
Any benchmark is not an accurate benchmark anymore, the moment the model makers can freely access it and had the time to train their models on it.
Not possible. I don't get how Opus 5 gets so high. Have they run it against the private and held-out games?
ARC-AGI is a terrible benchmark for testing LLMs because LLMs are not made, trained, or tuned for playing games.
They are trained on text to respond well to text based questions and do tasks involving modifying text files.
They are not designed for playing games, looking at games, or visual puzzles. Also translating games into text input for the LLM skews the test completely.
Imagine trying to get a human to solve visual puzzle but they can’t look at the puzzle but it has to be explained to them in textual format, we would be terrible at it.
But yet we persist in wasting time on this benchmark. It doesn’t mean anything.
Are these typically the type of tasks that are genuinely worth tens of thousands of dollars?
games are great (as for a human)
but I kinda wish I could select level... I accidentally pressed redirect button and when I came back I was once again shown level 1, all progress lost :(
Why isn’t Fable 5 included on the leaderboard?
solving ARC-AGI and being useful turned out to be two different problems
cost 20k ???? man
those are like software engineer from third world country
Deepseek V4 and Kimi 3 still missing, at least GLM is there.
this is not a good measure of current model capability. we need to test agents in harnesses, not models with a single prompt
test Codex, not Sol. test Claude code, not Opus
When we started talking about AGI a few years ago there seemed to be a relatively common consensus that LLM models could not be considered AGI because of how they work. Even if there was some changes to train the model on the fly, I still just don’t feel like this is AGI. It’s just a more convincing version of the existing “party trick” we have been doing all along. It convinces us it’s “AGI” the same way current models would convince someone 10 years ago that it was intelligent.
Nobody has any right to take anything I say seriously, because I’m just some random on the internet. But I don’t think true AGI is any closer than about 10-20 years away. That would be to create an actual analog for a human brain.