Neither is the primary cause, I suspect. If you look at the coding index alone, you see more plausible results from a software engineering perspective (in particular, Terra no longer outperforming Fable) [1].

That especially the small flash models that are primarily optimized for coding tasks would perform worse at non-coding tasks is not per se surprising to me. The introduction of Terminal-Bench Science 0.1 in particular hurts their aggregate scores [2].

The general question remains of how meaningful aggregating the results of very heterogeneous tasks into a single numeric score is. (Aside from general questions about individual benchmark validity.)

Note: the cyber index is also interesting. While refusals make it somewhat opaque, even if you optimistically count all refusals for Opus and Fable as successes, the results are ... fascinating [3].

I'll add that I really want to know how Opus 4.x and Codex 5.x perform on the new index. Once we have that information, this is going to give us a better understanding of how meaningful the scores are.

[1] https://artificialanalysis.ai/#coding-agents-tabs

[2] https://artificialanalysis.ai/evaluations/terminal-bench-sci...

[3] https://artificialanalysis.ai/#cyber-index-tabs

Tinfoil hat time: but the way they waited a couple weeks to evaluate Mimo 2.6 Flash, allowing GPT-6 Luna to sit on the Pareto frontier in the meantime, really did make me think they might have some bias going on... (Luna was displaced when they finally evaluated Mimo 2.6 Flash, which had been released the day before Luna)

edit to add: there totally is benchmaxxing going on, but I'd be really surprised if it wasn't across the board. After all, how can any lab tell if their model has improved over a previous iteration, if not by comparing benchmark performance?

> ...or chinese models are benchmaxxed...

It is hard to tell what people are trying to say when they use undefined words with no particular meaning. This doesn't seem to change the relative position of the Chinese models much. The Chinese are a month or few behind and the state of intelligence is so volatile that the top 10 will be completely different in 6 months. I'd expect it to still be a mix of US and Chinese models with the US models leading.