It's odd because I wonder if anybody actually cares about such scores. It's not like millions of end users look at the score and use it to make a decision what to buy. Subscriptions can easiliy be canceled and people anyway use the models they "feel" work best for them. Hardly anybody I know uses an even remotely scientific method to evaluate model performance. It's mostly vibes with a little bit of (mostly flawed) A/B testing sprinkled in.

So ... why bother to cheat?

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InsideOutSanta 5 hours ago | parent | next [-]

> So ... why bother to cheat?

I think part of it is to justify capex. "Yeah, we spent x billions on this, but look, we have the best models, we're beating OpenAI and Anthropic, we're winning, it's all worth it!"

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dist-epoch 5 hours ago | parent | prev [-]

From all of the terrible benchmarks of AIs, the AA one is the least terrible.

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kennywinker 3 hours ago | parent [-]

Only because it aggregates all the other benchmarks. It still feels off. It felt off before the last two re-rankings, and it still feels off. Probably because the benchmarks its based on aren’t measuring the right things.

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teiferer 3 hours ago | parent [-]

Not questioning that it's off/biased/..., but what are you basing your "feel" on? Anything empirical or really just a feel?

	▲	kennywinker 3 hours ago \| parent [-]
		Entirely vibes based for the AA rankings. For specific benchmarks, I’ve downloaded and used basically every model out there under 35b parameters - especially the tiny models: ling-3.0-tiny, spark-x2.6-4b, nanbeige, etc. etc. some of those tiny models are ranked VERY high on benchmarks like deepswe or terminalbench but are obviously incapable when you try to use them for swe or terminal based tasks. If that’s benchmaxxing or a bad benchmark, idk, but it’s made me not trust the benchmarks much. If it benchmarks poorly, it’s probably bad. If it benchmarks well, it might be good.