It does really well on "AA-Omniscience Non-Hallucination Rate", far higher than DeepSeek, GPT 5.5 or Fable. I really like that benchmark because it's one of the few benchmarks that allows LLMs to elect not to answer if they are unsure and punishes them for trying to bullshit their way through the benchmark
Yes. Most benchmarks just measure how many answers are correct. The best way to optimize that is to confidently state something, in hopes it's correct. Which is exactly how most LLMs behave, despite plenty of evidence that they do know whether they "know" something
Bullshitting is how LLMs work. It doesn't require active encouragement. All it takes is a machine without consciousness or physical access to the world and an actually-lived life. A training set that contains lots of confident answers and few to no refusals doesn't help either.
They are, especially multiple choice questions. The same happens with humans exams:
Let's say there are 100 questions, with 4 answers each. A good answer is worth 1 point. By just guessing you get an average of 25/100, way more than 0/100 by not replying.
If instead a wrong answer is -1 point, by just guessing you get on average -75/100, way worse than 0/100.
I think it will eventually result in regulation and a potential grey market, and/or implosion of the centralized LLM services — I doubt they can keep hardware from becoming cheaper forever, and diminishing returns will make consumer hardware suitable for all but the hardest problems. At that point, the hardware “moat” will be completely gone and have become an extreme unrecoverable sunk cost.
PS: Just added a cool feature, so you can filter the leaderboard for multiple models at once, by using a comma, like: https://aibenchy.com/?q=glm,claude
Where do you see that? I see they have GPT-5.5 (xhigh) at 55, GPT-5.5 (high) at 53, and Muse Spark at 43. Muse Spark does beat GPT-5.4 mini (xhigh) which scores 40, but the key there is "mini".
In the coding index, GPT-5.5 gets 59.1, 58.5, 56.2, and 52.1 for xhigh, high, medium, and low while Muse Spark is behind at 47.5. For agentic, GPT-5.5 gets 74.1, 72.0, 69.4, and 59.7 (xhigh, high, medium, low) while Muse Spark gets 62.0 (beating only GPT-5.5 low).
GPT-5.5 only gets beaten by Opus 4.8 in their general index, is the top spot for coding, and is #3 behind Opus 4.8 and GLM-5.2 for agentic (excluding Fable 5 which takes the top spot, but is unavailable).
There’s no way Anthropic can keep jacking up the prices like this for every marginally better model. I think even tokenmaxxing companies are going to soon balk at $50/million output tokens.
It does really well on "AA-Omniscience Non-Hallucination Rate", far higher than DeepSeek, GPT 5.5 or Fable. I really like that benchmark because it's one of the few benchmarks that allows LLMs to elect not to answer if they are unsure and punishes them for trying to bullshit their way through the benchmark
It took me a while to figure out how to interpret the benchmark correctly, because on the overview page it says "AA-Omniscience Non-Hallucination Rate," but on the benchmark page https://artificialanalysis.ai/evaluations/omniscience#aa-omn...
it said "the lower, the better." Eventually, I realized that the "non" reverses the scores. And indeed, the results are consistent.
This implies that other benchmarks (for which every AI provider is optimizing?) are actively encouraging bullshitting?
Yes. Most benchmarks just measure how many answers are correct. The best way to optimize that is to confidently state something, in hopes it's correct. Which is exactly how most LLMs behave, despite plenty of evidence that they do know whether they "know" something
Bullshitting is how LLMs work. It doesn't require active encouragement. All it takes is a machine without consciousness or physical access to the world and an actually-lived life. A training set that contains lots of confident answers and few to no refusals doesn't help either.
They are, especially multiple choice questions. The same happens with humans exams:
Let's say there are 100 questions, with 4 answers each. A good answer is worth 1 point. By just guessing you get an average of 25/100, way more than 0/100 by not replying.
If instead a wrong answer is -1 point, by just guessing you get on average -75/100, way worse than 0/100.
Local models are already useful today. The next milestone is getting this level of performance onto truly affordable hardware.
NVidia has less than zero reason to ship cards ideal for this at low prices.
AMD’s stock price reflects a hope they launch a CUDA alternative. But this is unlikely for the near future.
There is a lot of interest in preventing China coming in with cheap AI hardware.
So I expect the direction to be good local models that few can run effectively.
The Chinese will flood the market with cheap AI chips just like they did with EV cars. As consumers we can’t thank them enough.
I think it will eventually result in regulation and a potential grey market, and/or implosion of the centralized LLM services — I doubt they can keep hardware from becoming cheaper forever, and diminishing returns will make consumer hardware suitable for all but the hardest problems. At that point, the hardware “moat” will be completely gone and have become an extreme unrecoverable sunk cost.
I also tested it[0]: quite similar to GLM 5, a few percent better, 30% faster and 50% more expensive.
[0]: https://aibenchy.com/?q=glm
benchmark where gemini flash is better than fable btw.
Well, most people were not liking Fable when it was available anyway, because it refused to answer questions very often.
PS: Just added a cool feature, so you can filter the leaderboard for multiple models at once, by using a comma, like: https://aibenchy.com/?q=glm,claude
still 1/4 of the price of anthropic and openai models though
It's always nice to see how open source models growing, hope we will have good performance with lower tier hardware some day.
I want to trust their benchmarks but when they have Muse Spark over GPT-5.5, it gives me pause.
Where do you see that? I see they have GPT-5.5 (xhigh) at 55, GPT-5.5 (high) at 53, and Muse Spark at 43. Muse Spark does beat GPT-5.4 mini (xhigh) which scores 40, but the key there is "mini".
In the coding index, GPT-5.5 gets 59.1, 58.5, 56.2, and 52.1 for xhigh, high, medium, and low while Muse Spark is behind at 47.5. For agentic, GPT-5.5 gets 74.1, 72.0, 69.4, and 59.7 (xhigh, high, medium, low) while Muse Spark gets 62.0 (beating only GPT-5.5 low).
GPT-5.5 only gets beaten by Opus 4.8 in their general index, is the top spot for coding, and is #3 behind Opus 4.8 and GLM-5.2 for agentic (excluding Fable 5 which takes the top spot, but is unavailable).
still quite verbose at 140m output tokens, but this is on max thinking. high should do better.
One or two more releases and they will reach Fable level.
by then there will be Fable 5.21, again 5% ahead of every other SotA while still only 500% the size.
There’s no way Anthropic can keep jacking up the prices like this for every marginally better model. I think even tokenmaxxing companies are going to soon balk at $50/million output tokens.
Anthropic wants to ban the alternatives through regulation and ideally provide differential access with differential pricing.
Some more discussion: https://news.ycombinator.com/item?id=48567759