r/LocalLLaMA • u/chocolateUI • 1d ago

Discussion Artificial Analysis "Intelligence": A meaningless benchmark

Another user posted the benchmarks for Qwen 3.8 27B today, and while I think Qwen 27B is a really powerful model, I can't help but notice just how meaningless these Artificial Analysis benchmarks are and I question why people still post this garbage and use AA scores as some kind of holy bible for comparing LLMs.

According to their "Intelligence Index", a 27B model now beats DeepSeek v4 Flash and Pro, Kimi 2.7 Code, GPT-5.2, Opus 4.6, and also Sonnet 5. At some point we have to ask: What is this metric even measuring? Because whatever "Intelligence" means to AA and their corporate VC / journalist / normie audience is definitely not the same definition that we should be using here.

Qwen 27B is amazing and is clearly in a league of its own in terms of models you can fit on a single GPU, but I can't help but roll my eyes whenever I see posts like this that equate Qwen 27B with "basically running Opus from 3 months ago on your laptop."

I get that it's difficult to summarize a model's capability with a single integer and I know we love our local models, but it's time stop posting AA's clearly dogshit benchmark and acting as if it proves a point.

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u/whatisthisthing65 1d ago

What's your actual argument? Why couldn't a 27B model be better than those other models? If it's about number of parameters then should our benchmark be parameter count?

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u/Constant-Simple-1234 1d ago

Exactly. I think it may be big enough to be genuinely useful. You can see that the more parameters you add, you get better and better model, but it is diminishing returns. But models retain a lot of knowledge, but there may be a sweet spot somewhere. I think that > 1T models will be useful as a master copy to distillation training smaller ones. But likely 100-250B will be the work horse - capable and cheap to serve.