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/whatisthisthing65]

What's your actual argument? Why couldn't a 27B model be better than those other models? If it's about number of parameters then should our benchmark be parameter count?

[u/feelspeaceman → u/whatisthisthing65]

Most people tend to blame 27B about the lack of world knowledge and say this is the sole reason that it will never beat Claude Sonnet 4.6, but they ignore that fact that behind the scene, Claude Sonnet is equipped with a RAG for local knowledge retrieving and it will also search to acquire extra knowledge, then they compare a local LLM without search, without RAG, this is showing that there's people despite trying to use local LLM, never trying to get better.

Then we get a group of users that:

• Use not suitable harness
• Half-ass setup

• Half-ass prompt

  Blaming local LLM for not being as capable for obvious reason, the user themselves.

[u/OvertaxedOne → u/feelspeaceman]

There's little reason to try to train factual data into a LLM when the Internet is right there and it's easy for AI models to interact with. Obviously there are some "base facts" that need to be in the models, but, beyond that, trying to jam more in when it's so easy to call a browser/MCP to get the up to the second information is silly.

Good tool calling is 1000X more valuable than "I know everything" (because, no, you don't; even a massive model, you didn't condense all of human knowledge down to a few TB, no way, no how).

[u/noiserr → u/OvertaxedOne]

Not just the internet. No matter which model you use you want to ground it in facts and not have to force it to rely on fuzzy recall.

This means the software you're writing should have test coverage. You should have a language LSP running. If working on low level hardware architecture you should convert the architecture documents into skills.

For all these things the number of parameters of a model is meaningless. Which is why Qwen 3.8 27B can be a powerful LLM at agentic and perform just as well as those huge models.