> measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer.
This is so useful because it makes you actually trust a models output. A high score on benchmarks is not as useful because a model overtrained to always answer will give confidently wrong responses. But this index measures how often it is correct while penalizing wrong responses so that a high score means you can trust this model more and when it doesn't know it is more likely to tell you that it really doesn't know rather than making shit up.
Fable also performs a lot better than opus 5 here which correlates very strongly with perceived strength despite the models performing similarly on e.g. DeepSWE
Astra is a big jump from sol and performs the same or slightly better than fable here.
This is really a great achievement: "Astra dominates the output token frontier"
Many labs used increased thinking to boost benchmark scores and performance. Most of the Chinese models were doing that for a while. Google and Anthropic as well.
Not OpenAI. 5.6 already was much more token efficient than other models and Astra beats Sol in token efficiency by a wide margin.
is any of this 'scientific'? does AA allow peer review of its processes? are these published in journals of at least medium impact? what are the sample sizes? how grounded is their mechanistic reasoning?
like they have words that are dressed in scientific language on their site like
"We estimate a 95% confidence interval for Artificial Analysis Intelligence Index of less than ±1% - based on experiments with >10 repeats on certain models for all evaluation datasets included in Artificial Analysis Intelligence Index v4.2."
but where's the outcome dataset justifying this? how did they get that probability? what was the specific methodology of the tests? what variables did they account for?
there's a major difference between scientific sounding and being truly empirically rigorous. the 'research' in AI intelligence feels somehow even less trustworthy than supplement-funded studies because those are at least subjected to scrutiny by peers without profit motives
Imo the omniscience index they have has the highest correlation to actual usefulness of the models.
https://artificialanalysis.ai/evaluations/omniscience
> measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer.
This is so useful because it makes you actually trust a models output. A high score on benchmarks is not as useful because a model overtrained to always answer will give confidently wrong responses. But this index measures how often it is correct while penalizing wrong responses so that a high score means you can trust this model more and when it doesn't know it is more likely to tell you that it really doesn't know rather than making shit up.
Fable also performs a lot better than opus 5 here which correlates very strongly with perceived strength despite the models performing similarly on e.g. DeepSWE
Astra is a big jump from sol and performs the same or slightly better than fable here.
This is really a great achievement: "Astra dominates the output token frontier"
Many labs used increased thinking to boost benchmark scores and performance. Most of the Chinese models were doing that for a while. Google and Anthropic as well.
Not OpenAI. 5.6 already was much more token efficient than other models and Astra beats Sol in token efficiency by a wide margin.
They realized Astra having the same score as Sol was silly so they rushed to update the index so it fits what people expect.
The old index was clearly bad (Astra is way better than Sol) but it's also unscientific to tweak it like this.
is any of this 'scientific'? does AA allow peer review of its processes? are these published in journals of at least medium impact? what are the sample sizes? how grounded is their mechanistic reasoning?
like they have words that are dressed in scientific language on their site like
"We estimate a 95% confidence interval for Artificial Analysis Intelligence Index of less than ±1% - based on experiments with >10 repeats on certain models for all evaluation datasets included in Artificial Analysis Intelligence Index v4.2."
but where's the outcome dataset justifying this? how did they get that probability? what was the specific methodology of the tests? what variables did they account for?
there's a major difference between scientific sounding and being truly empirically rigorous. the 'research' in AI intelligence feels somehow even less trustworthy than supplement-funded studies because those are at least subjected to scrutiny by peers without profit motives
What version the intelligence vs cost graph is using? they didn't ran v4.2 to all models.
Why have they not included ARC-Agi-3 on their index?
Clearly that would move things around.
How to view the previous version to compare?