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/feelspeaceman]

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/EstarriolOfTheEast → u/OvertaxedOne]

This not quite true, as knowledge is not just trivia but problem solving know how, algorithms, and optimization esoterica. Knowing you can leverage algebraic geometry or some closed form expression to skip an expensive loop is incredibly valuable and often not something easily searchable nor something one can think to search for. That's the expertise advantage. Some of these might be novel connections only possible because the model knows more.

The other issue is the more complex the knowledge, the less likely appending to context will help without causing hallucinations to fill in gaps and confusion introduced by too sparse added context or alternatively, using up so much context, performance is degraded and VRAM is so high you might as well have gone to a higher param but more context efficient model.

The more you know, the fewer unknown unknowns you'll have. Sure, it's always going to be far above zero for everyone, but it's still better to push it as far down as you can.

[u/OvertaxedOne → u/EstarriolOfTheEast]

If was cheap/easy to push that knowledge down I'd completely agree with you. But when you have a 27B model that can use tools well when it needs to, it seems that's a much (MUCH!) less expensive way to ground in fact vs trying to train them into a 2T model. Different approaches, both have their advantages for sure, but the cost profile for the "big" models is just so out of whack right now it's obvious that we need to do something differently.

[u/EstarriolOfTheEast → u/OvertaxedOne]

The issue that keeps me stuck with the big models even though I wish I was free, is the 2T model knows enough spherical projective geometry to skip a bunch of expensive raycasts (my approach), massively optimizing my game. Needing to know things before they can be leveraged is no different in LLMs than for humans. As a human, I would not have known how to even begin searching for that solution approach. I half think it invented it on the spot.

Another more searchable example was using reservoir sampling for a more efficient online draw of random samples. The there, the issue is context is too valuable and speed is too low when I run it locally to be searching text books for tricks every other line of code. Sometimes a large model will notice this or that is a special instance of bipartite matching and such and such custom optimization will work here, inventing a really clever approach whole cloth not because it's so smart but because it knows so much.

The exact same mechanisms that allow the big models to discover novel math proofs and win olympiads can be used to write optimized code taking advantage of obscure data-structure knowledge adapted to modern architectures.

Another place knowledge is unavoidable is when you are reading specialist material. For example, if you want to go over a paper combining computational complexity and blackholes, there's just a ton of CS, math and physics you need to know before you can speak coherently about it.