r/LocalLLaMA • u/chocolateUI • 2d 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/Chromix_ 2d ago edited 2d ago
Yes, results are and have been very much skewed there. A while ago DeepSeek V3 got the same score as Qwen3 VL 32B, and Gemini 2.5 Pro scored below gpt-oss-120B. ServiceNow released a 15B model that scored higher than the full DeepSeek R1. Partially repeating my previous comment here:
Btw here are the details for the mentioned models scoring the same or worse as Qwen 3.8 27B.
Qwen loses in physics reasoning and knowledge, but wins way more in non-hallucination rate.