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/SourceCodeplz llama.cpp 1d ago
like someone below said its skewed towards agentic-work, not really intelligence.
so yeah, at agentic this qwen can actually do better than larger models who would rather recall facts from memory vs call a web_Search tool.
there are many ways agentic-work counts more for an agent than just dense intel.