Well either AA is biased bribed riged whatever or chinese models are benchmaxxed or maybe its both

rbehrends an hour ago | parent | next [-]

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

▲

wyrdcurt 2 hours ago | parent | prev | next [-]

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?

▲

lisplist an hour ago | parent [-]

I’ve very mixed feelings on Mimo. The new models do surprisingly well on some tasks, but they don’t allow you to set the thinking level and the model often times spends next to zero time thinking about a problem. If you coax it just a little bit, you can get it to solve it, but this isn’t an issue with other models. Also it’s incredibly easy to get it to get caught in a thinking loop.

	▲	wyrdcurt 35 minutes ago \| parent [-]
		I've personally experienced overthinking and the thinking loop issue more often than seeing it not think enough, I know what you mean though. I tried Haiku 5.5 and the most impressive thing about it to me was that it seemed not to overthink when it wasn't necessary, yet it somehow knew when it should spend some more time reasoning about a task. American models are too rich for my blood though. I use GLM 5.3 Flash more than any other model and it has the same issues as Mimo. Deepseek 4.1 Flash is turning out to be a sweet spot for me; it's cheap and blazing fast on CoralBricks where I've been using it, so I don't mind when it overthinks (I try to pay attention to what my agents are doing, and it tends to finish tasks before I can skim half of its reasoning)

▲

2 hours ago | parent | prev | next [-]

[deleted]

▲

roenxi 2 hours ago | parent | prev [-]

> ...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.

	▲	Gareth321 an hour ago \| parent [-]
		Benchmaxing is defined as targeted post-training attempts to optimise for higher benchmarks. This doesn’t change the aggregate intelligence or capability of the model, so it can make a model appear more capable than it is.