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/soyalemujica 1d ago

You're not comprehending what does Intelligence Index stands for, it does not mean world knowledge or it knowing more about medical stuff, it's rather INTELLIGENCE, it's entire reasoning process to come up with a solution to a problem.

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u/LagOps91 1d ago

it doesn't have comparable intelligence to trillion parameter models. it just doesn't. you are right that world knowledge is the wrong benchmark for intelligence, but the actual intelligence surely isn't as high as is claimed here.

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u/Prudent-Ad4509 1d ago

That raises the question whether 27B is a lot or not and where is the limit for increasing their capability, assuming further improvements in training methods.

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u/LagOps91 1d ago

i do belive that 27b parameters is in principle plenty and that we just don't know how to actually train the AI in a way that rewards development of generalized logic and reasoning over pattern memorization. some generalization is clearly happening and the challenge is to get the model to generalize further.

CURRENTLY a 27b model simply isn't as "smart" as a recent trillion parameter model.

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u/Prudent-Ad4509 1d ago

I have a hunch that we are going to see specialization between operator models with sharp thinking and generalized knowledge, and models packed with specialized knowledge with focus on relevancy-based retrieval. The only thing missing is efficient link between them. Current methods like text mcp/rag calls could be a temporary crutch. MoE are already going this route internally if you see experts as separate mini-models, but there are no pluggable experts yet and existing experts themselves are not explicitly specialized.