It's a bit misleading I think because these benchmarks are for Max level, at which Anthropic newest models use crazy amount of reasoning tokens. And we know that intelligence scales with their number.
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).
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.)
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).
Which is why I said that cost might be a better metric than output tokens. But even that is somewhat misleading -- because there isn't really a single price -- there are a variety of offers / deals / subsidies, including subscription plans.
But if we're actually talking benchmarking intelligence -- as evidenced by "Artificial Analysis Intelligence Index" and the rest of the reporting -- then comparing systems with different amounts of recurrence on output tokens is flawed.
Didn't expect that. Luna pricing is crazy now. I don't think there is anything on the market that competes at this price-performance point.
For our production app, OpenAI clearly is the best provider now. Their API is very reliable and has many nice features. The price-performance of the model lineup is incredible. We used open weights model via Fireworks for a long time (e.g. Kimi K2.5). Fireworks is a great provider but we still ran into issues here and there (Same with Anthropic and Google). OpenAI just works, is fast and in my view has a better price-performance ratio across almost all levels of intelligence.
That’s what I was seeing too, and my cache hit rate was below 25% during that same time, leading to significant burn of my weekly limit (Pro 5x plan) via all the uncached input. Doesn’t prove that the overload caused the cache failure, but it does seem to point to some common infrastructure cause. No such problems (cache hit or system overload) with Terra or Luna.
Was that Codex/subsidised-usage or API? I do get overloaded in Codex-account from time to time, but API is rock solid.
They obviously load shed a bit of Codex-sub during peak times, and for the amount of tokens you get for a sub, I don't mind. I just mean the API where you pay-per-token is rock stable.
