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- Hacker News
- Are you sure that Fable 5.1 outperforms Astra? Feel like early indications and testing appear to show for the first time a true Agentic experience? This could open the door to a true loop build in every corner of the world.
- Seeing the Qwen3.8-27B model among giants is almost comical. It is unparalleled and a true testament to Alibaba’s talent and ingenuity. Can’t overstate how excited I am for newer edge models.by sheepscreek
- I quite wish they'd move to terminal bench 4.0. 2.1 is saturated - there is no world in which Gemini 3.8 Flash is producing better code than Astra or Fable, as the 2.1 results might suggest. The 4.0 results differentiate these models much more effectively.
(2.1 is useful for knowing they can all one-shot straightforward scripts, of course.)
by dgacmu - DeepSWE has Gemini 3.8 Flash up really high, too.by swingboy
- It is very unfortunate they upweighted SciCode from 8% to 10%. SciCode is a broken benchmark: see https://arxiv.org/abs/2608.04975.by sanxiyn
- While I like this index, calling in "Intelligence" might be confusing - it is a mix of coding and knowledge.
Compare and contrast with ARC-AGI, BabaIsBench (https://quesma.com/benchmarks/babaisbench/), or MazeBench (https://mazebench.com/blog?post=introducing-mazebench).
In particular, in one Baba Is Bench post (https://quesma.com/blog/baba-is-aug-2026/), while quoting a Pareto frontier chart from AA, I noted:
> Intelligence Index vs. Cost per Intelligence Index Task from Artificial Analysis. Note that it is based on score of benchmarks like Terminal-Bench v2.1, SciCode, Humanity’s Last Exam, GPQA Diamond - not necessarily fluid intelligence like in abstract puzzle games of ARC-AGI-3 or Baba is You.
by stared - This update really gives OpenAI a boost. Not saying there's anything inaccurate or untoward about that, but the timing is unfortunate. It would have looked better had it been done prior to the Fable 5.1 and GPT 6 releases. I guess AA would say that there's no perfect time to do these updates, given the rapid fire pace of releases!by AnodicElegy
- The timing is related to the fact that their benchmark was saying it was the same as Sol, and below Opus 5, when anecdotal reports and other benchmarks strongly disagree. It looked bad for them for their benchmark to disagree with people's lived experience so hard.by CuriouslyC
- I wonder if Artificial Analysis could be influenced by certain model companies. Looking at the changelog [0], they updated a few times after new models appeared, so US models progression was much higher than that of other vendors (like when they updated the algorithm after Kimi K3 versus Opus 4.7, so Kimi dropped in the rankings). Or maybe thats just coincidence.by __natty__
- That would be the fastest way for them to completely torch their company.
The only thing they are selling and why people look at them is trust that they do honest evaluations.
by dist-epoch - 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.
Edit: Just to make the point: Astra (max) has the 2nd highest score and the third lowest output tokens (among the models shown by AA).
by __jl__ - Duh, that's just a benchmarking artifact. If you run one model at 4x recurrence compared to another, you get 4x thinking without increasing the tokens. So of course it's going to dominate the perf at given output token level. The truly sensible comparison is perf -vs- thinking flops (because each model might be a different unknown size, but labs are very secretive about what they're actually running under the hood) or perhaps cost (which can be misleading because of subsidies, but is at least practically relevant in the moment).by ssivark
- GPT-6 has a looped transformer / recurrent depth architecture, so it gets some internal CoT reasoning "for free" with no output tokens.
This also makes it much harder to monitor its reasoning.
by Scaevolus - The apparent advantage is exaggerated by them running Astra at six different effort levels, and almost everything else at just the maximum available effort.
I don't really understand why they keep doing this. Either run and report everyone at multiple effort levels, or run everyone at only one.
But alsi, token efficiency seems pretty artificial? For example tokenizers are different from model to model. The cost/perf Pareto frontier seems a lot more meaningful (and Astra does very well at that too, just to be clear. It seems to be a great model.)
by jsnell - IMO one of the biggest losses of the OpenAI/Cursor breakup will be the loss of OAI models on CursorBench [1]. Their bench has always been one that most-fit my mental model of how good each of these models are. I find AA’s Intelligence index to often be out of alignment with my own subjective evals.by jjcm
- I have no idea how artificial analysis got to be something anyone took seriously. This is their new benchmark set?
A glance at their new index shows that whatever they're measuring, it isn't useful.
Spend an hour with gemini 3.8 and tell me that model belongs in 2026. It feels like the model has Alzheimer's. It gets confused about whether what it reads is what it did. Just crazy bad.
I haven't tried muse spark 1.3. But it must have been a miracle since 1.2 to hit that rank.
Video game journalism vibes all over this.
- Because it's the best option currently available.
It's really easy to shit on AI benchmarks, but that noise is useless unless you're offering a solution or a better benchmark.
by Catloafdev - As the saying goes, Artificial Analysis is the worst benchmarking company, except for all the others.by dist-epoch
- What other benchmarks do you recommend that are more accurate?by nojs
- 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.
by redox99 - It's a benchmark for brand new technology and you admit the old score was silly but you think updating it is unscientific?
- Well, do you have any ideas about how to make it scientific? And they clearly needed to do something based on their unnatural rankingby big-chungus4
- Maybe there is truth in it?
I asked Astra to make some changes, it built crazy overengineered code, switched back to Sol, said code is too complicated, rewrite it, and it made lot cleaner code.
I feel some models could chaise complicated benchmarks too much in expense of simpler tasks quality.
by andriy_koval - > Astra is way better than Sol
This needs to be evaluated per task, jagged frontier yadda yadda. I would be not at all surprised if sol was better at some things than Astra just like people still use opus 4.6 and for good reasons.
by baq - > Astra is way better than Sol
For knowledge type questions is that necessarily true? In the past we've seen things like Google's models degrading on general knowledge after the preview releases while improving on code/tool use, presumably due to catastrophic forgetting from the additional training. Their preview would be free, get lots of agentic use from users, then additional training on that and probably additional automated RL.
However Astra is on an entirely new base model so I also wouldn't expect it to be worse.
by cma - In real life, there is always this feedback edge from the results to the methodology.
Theoretically it's unscientific to do tweaks like this but in reality this is what actual science is because you need to see the results understand the problems in your experimental designs.
Now the interesting thing is that you could repair the bias problem. The main issue is that you will do these tweaks after a bad result not after they look fine.
Maybe we could commit ahead of time that the experiment will be reanalyzed after results regardless of what they are. Instead of only when the results disprove the hypothesis.
So like artificial analysis committing to a fixed cadence of index updates instead of when the results start looking jank.
by kingstnap - Given how many private benchmarks they're using now, its likely they just tested different combos until they got the result they wanted.
Completely discredits the index if it just gets modified to match social media vibes.
by x312 - 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
by paimapi - 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.
by jascha_eng - This matches what I've seen building anything that uses an LLM for narrow structured output rather than open ended chat, things like classifying a diff into a fixed set of categories or summarizing a change. The aggregate benchmark score barely predicts how it behaves in production. What actually breaks a pipeline is a confident wrong answer on the small slice of inputs that don't fit the pattern it saw during training, not a lack of raw capability. A model that says it isn't sure on the edge cases is far more useful to me than one that scores higher on average but never admits uncertainty, because the wrong but confident output is the one that slips through review unnoticed.by Buoylog