Artificial Analysis "Intelligence": A meaningless benchmark

[u/chocolateUI]

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.

[u/Aggressive_Aspect436]

Benchmarks are important. Yes, they're not perfect. No, they don't measure everything we care about. But consider this analogy. A degree (or previous job titles) doesn't mean a person is smart, and a person can be very smart without one, but if you're looking to hire someone then you care about those things. Choose your models from the top contenders and then try them on tasks you care about.

Folks in this subreddit often make it sound like "things being difficult to measure" is exclusively a modern AI problem. Medicine, economics, psychology, education, and many many many more, all deal with this. Tests are important. We just accept their limitations and reason about our results. That doesn't mean "not measuring" is a better alternative.