Wait, I may be missing something here. These benchmarks are gathered by having models play each other, and the second illegal move forfeits the game. This seems like a flawed method as the models who are more prone to illegal moves are going to bump the ratings of the models who are less likely.

Additionally, how do we know the model isn’t benchmaxxed to eliminate illegal moves.

For example, here is the list of games by Gemini-3-pro-preview. In 44 games it preformed 3 illegal moves (if I counted correctly) but won 5 because opponent forfeits due to illegal moves.

https://chessbenchllm.onrender.com/games?page=5&model=gemini...

I suspect the ratings here may be significantly inflated due to a flaw in the methodology.

EDIT: I want to suggest a better methodology here (I am not gonna do it; I really really really don’t care about this technology). Have the LLMs play rated engines and rated humans, the first illegal move forfeits the game (same rules apply to humans).

The LLMs do play rated engines (maia and eubos). They provide the baselines. Gemini e.g. consistently beats the different maia versions.

The rest is taken care of by elo. That is they then play each other as well, but it is not really possible for Gemini to have a higher elo than maia with such a small sample size (and such weak other LLMs).

Elo doesn't let you inflate your score by playing low ranked opponents if there are known baselines (rated engines) because the rated engines will promptly crush your elo.

You could add humans into the mix, the benchmark just gets expensive.

That’s a devastating benchmark design flaw. Sick of these bullshit benchmarks designed solely to hype AI. AI boosters turn around and use them as ammo, despite not understanding them.