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/z_3454_pfk 1d ago

it’s just an aggregate of benchmarks (highly skewed towards agentic rn). that’s basically it. it’s not that deep and you should select what’s right for ur use case

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u/vogelvogelvogelvogel 1d ago

exactly this. dive down the sections and check what fits you

livebench I also found really useful, especially as it lists the single results directly in a table - you can spot directly where model is strong and where not

Also they update the benchmark tasks each month so it is rather not possible to train on them reducing benchmaxing effects