[dwohnitmok]

Not anymore. This benchmark is for LLM chess ability: https://github.com/lightnesscaster/Chess-LLM-Benchmark?tab=r.... LLMs are graded according to FIDE rules so e.g. two illegal moves in a game leads to an immediate loss.

This benchmark doesn't have the latest models from the last two months, but Gemini 3 (with no tools) is already at 1750 - 1800 FIDE, which is approximately probably around 1900 - 2000 USCF (about USCF expert level). This is enough to beat almost everyone at your local chess club.

[runarberg]

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).

[dwohnitmok]

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.

[runarberg]

I did indeed miss something. I learned after posting (but before my EDIT) that there are anchor engines that they play.

However these benchmarks still have flaws. The two illegal moves = forfeit is an odd rule which the authors of the benchmarks (which in this case was Claude Code) added[1] for mysterious reasons. In competitive play if you play an illegal move you forfeit the game.

Second (and this is a minor one) Maia 1900 is currently rated at 1774 on lichess[2], but is 1816 on the leaderboard, to the author’s credit they do admit this in their methodology section.

Third, and this is a curiosity, gemini-3-pro-preview seems to have played the same game twice against Maia 1900[3][4] and in both cases Maia 1900 blundered (quite suspiciously might I add) mate in one when in a winning position with Qa3?? Another curiosity about this game. Gemini consistently played the top 2 moves on lichess. Until 16. ...O-O! (which has never been played on lichess) Gemini had played 14 most popular lichess moves, and 2 second most popular. That said I’m not gonna rule out that the fact that this game is listed twice might stem from an innocent data entry error.

And finally, apart from Gemini (and Survival bot for some reason?), LLMs seem unable to pass Maia-1100 (rated 1635 on lichess). The only anchor bot before that is random bot. And predictably LLMs cluster on both sides of it, meaning they play as well as random (apart from the illegal moves). This smells like benchmaxxing from Gemini. I would guess that the entire lichess repertoire features prominently in Gemini’s training data, and the model has memorized it really well. And is able to play extremely well if it only has to play 5-6 novel moves (especially when their opponent blunders checkmate in 1).

1: https://github.com/lightnesscaster/Chess-LLM-Benchmark/commi...

2: https://lichess.org/@/maia9

3: https://chessbenchllm.onrender.com/game/6574c5d6-c85a-4cb3-b...

4: https://chessbenchllm.onrender.com/game/4af82d60-8ef4-47d8-8...

[dwohnitmok]

> The two illegal moves = forfeit is an odd rule which the authors of the benchmarks (which in this case was Claude Code) added[1] for mysterious reasons. In competitive play if you play an illegal move you forfeit the game.

This is not true. This is clearly spelled out in FIDE rules and is upheld at tournaments. First illegal move is a warning and reset. Second illegal move is forfeit. See here https://rcc.fide.com/article7/

I doubt GDM is benchmarkmaxxing on chess. Gemini is a weird model that acts very differently from other LLMs so it doesn't surprise me that it has a different capability profile.

[runarberg]

>> 7.5.5 After the action taken under Article 7.5.1, 7.5.2, 7.5.3 or 7.5.4 for the first completed illegal move by a player, the arbiter shall give two minutes extra time to his/her opponent; for the second completed illegal move by the same player the arbiter shall declare the game lost by this player. However, the game is drawn if the position is such that the opponent cannot checkmate the player’s king by any possible series of legal moves.

I stand corrected.

I’ve never actually played competitive chess, I’ve just heard this from people who do. And I thought I remembered once in the Icelandic championships where a player touched one piece but moved the other, and subsequently made to forfeit the game.

[runarberg]

Replying in a split thread to clearly separate where I was wrong.

If Gemini is so good at chess because of a non-LLM feature of the model, then it is kind of disingenuous to rate it as an LLM and claim that LLMs are approaching 2000 ELO. But the fact it still plays illegal moves sometimes, is biased towards popular moves, etc. makes me think that chess is still handled by an LLM, and makes me suspect benchmaxxing.

But even if no foul play, and Gemini is truly a capable chess player with nothing but an LLM underneath it, then all we can conclude is that Gemini can play chess well, and we cannot generalize to other LLMs who play about the level of random bot. My fourth point above was my strongest point. There are only 4 anchor engines, one beats all LLMs, second beats all except Gemini, the third beats all LLMs except Gemini and Survival bot (what is Survival bot even doing there?) and the forth is random bot.

[dwohnitmok]

Gemini is an LLM. It playing chess is not relying on a non-LLM module of some sort. I'm just saying that as an LLM, Gemini has a peculiar profile compared to other LLMs (likely an artifact of its post-training process). In particular Gemini is very capable, but also quite misaligned (it will more often actively sabotage users).

> then all we can conclude is that Gemini can play chess well, and we cannot generalize to other LLMs who play about the level of random bot

That's overly reductive. That would be true if we didn't see improvement over time from the other LLMs but we clearly do. In particular, even if Gemini is benchmarkmaxxing, this means that LLMs from other labs will eventually get there as well. Benchmarkmaxxing can be thought of as "premature" reaching of benchmarks. But I can't think of a single benchmark that was benchmarkmaxxed that wasn't eventually saturated by every single LLM provider (because being able to benchmarkmaxx serves as an existence proof that there is an LLM capable of it and as more training gets done on the LLMs the other ones get there).

[emp17344]

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.

[famouswaffles]

Relax. Anyone who's genuinely interested in the question will see with a few searches that LLMs can play chess fine, although the post-trained models mostly seem to be regressed. Problem is people are more interested in validating their own assumptions than anything else.

https://arxiv.org/abs/2403.15498

https://arxiv.org/abs/2501.17186

https://github.com/adamkarvonen/chess_gpt_eval

[runarberg]

I like this game between grok-4.1-fast and maia-1100 (engine, not LLM).

https://chessbenchllm.onrender.com/game/37d0d260-d63b-4e41-9...

This exact game has been played 60 thousand times on lichess. The peace sacrifice Grok performed on move 6 has been played 5 million times on lichess. Every single move Grok made is also the top played move on lichess.

This reminds me of Stefan Zweig’s The Royal Game where the protagonist survived Nazi torture by memorizing every game in a chess book his torturers dropped (excellent book btw. and I am aware I just committed Godwin’s law here; also aware of the irony here). The protagonist became “good” at chess, simply by memorizing a lot of games.

[famouswaffles]

The LLMs that can play chess, i.e not make an illegal move every game do not play it simply by memorized plays.

[dwohnitmok]

> That’s a devastating benchmark design flaw

I think parent simply missed until their later reply that the benchmark includes rated engines.

[cesarvarela]

Yeah, but 1800 FIDE players don't make illegal moves, and Gemini does.

[dwohnitmok]

1800 FIDE players do make illegal moves. I believe they make about one to two orders of magnitude less illegal moves than Gemini 3 does here. IIRC the usual statistic for expert chess play is about 0.02% of expert chess games have an illegal move (I can look that up later if there's interest to be sure), but that is only the ones that made it into the final game notation (and weren't e.g. corrected at the board by an opponent or arbiter). So that should be a lower bound (hence why it could be up to one order lower, although I suspect two orders is still probably closer to the truth).

Whether or not we'll see LLMs continue to get a lower error rate to make up for those orders of magnitude remains to be seen (I could see it go either way in the next two years based on the current rate of progress).

[overgard]

I think LLM's are just fundamentally the wrong AI technique for games like this. You don't want a prediction for the next move, you want the best move given knowledge of how things would play out 18 moves ahead if both players played the optimal move. Outside of an academic interest/curiosity, there isn't really a reason to use LLMs for chess other than thinking LLMs will turn into AGI (I doubt it)

[cesarvarela]

A player at that level making an illegal move is either tired, distracted, drunk, etc. An LLM makes it because it does not really "understand" the rules of chess.

[runarberg]

I suspect the majority of these illegal moves are in blitz or bullet tournaments in game 12 of the third day, and the player touches one peace but moves another, or hits the clock with the hand that didn’t make the move, or hits the clock without making a move. I don‘t think any expert level chess player grabs a captured rook and places it on the board, or moves a light squared bishop to a dark square, unless they are hustling at the park, in which case (it can be argued) moves like this with a slight of hand is part of the game.

[famouswaffles]

That benchmark methodology isn't great, but regardless, LLMs can be trained to play Chess with a 99.8% legal move rate.

[recursive]

That doesn't exactly sound like strong chess play.

[dwohnitmok]

It's enough to reliably beat amateur (e.g. maia-1900) chess engines.

[deadbabe]

Why do we care about this? Chess AI have long been solved problems and LLMs are just an overly brute forced approach. They will never become very efficient chess players.

The correct solution is to have a conventional chess AI as a tool and use the LLM as a front end for humanized output. A software engineer who proposes just doing it all via raw LLM should be fired.

[rodiger]

It's a proxy for generalized reasoning.

The point isn't that LLMs are the best AI architecture for chess.

[runarberg]

> It's a proxy for generalized reasoning.

And so for I am only convinced that they have only succeeded on appearing to have generalized reasoning. That is, when an LLM plays chess they are performing Searle’s Chinese room thought experiment while claiming to pass the Turing test

[deadbabe]

Why? Beating chess is more about searching a probability space, not reasoning.

Reasoning would be more like the car wash question.

[famouswaffles]

It's not entirely clear how LLMs that can play chess do so, but it is clearly very different from the way other machines do so. The construct a board, they can estimate a players skill and adjust accordingly, and unlike other machines and similarly to humans, they are sensitive to how a certain position came to be when predicting the next move.

Regardless, there's plenty of reasoning in chess.

[deadbabe]

It’s very clear how, chess moves and positions are vector encoded into their training data, when they are prompted with a certain board state, they respond with the most probable response to that. There is no reason.

[famouswaffles]

Actual Researchers can't give you a complete answer but you can. Whatever you say.

[overgard]

They have literally every chess game in existence to train on, and they can't do better than 1800?

[jimbokun]

Why do you think they won’t continue to improve?

[overgard]

Because of how LLM's work. I don't know exactly how they're using it for chess, but here's a guess. If you consider the chess game a "conversation" between two opponents, the moves written out would be the context window. So you're asking the LLM, "given these last 30 moves, what's the most likely next move?". Ie, you're giving it a string like "1. e4 e5, 2. Nf3 Nc6, 3. Bb5 a6, 4..?".

That's basically what you're doing with LLMs in any context "Here's a set of tokens, what's the most likely continuation?". The problem is, that's the wrong question for a chess move. If you're going with "most likely continuation", that will work great for openings and well-studied move sequences (there are a lot of well studied move sequences!), however, once the game becomes "a brand new game", as chess streamers like to say when there's no longer a game in the database with that set of moves, then "what's the most likely continuation from this position?" is not the right question.

Non-LLM AI's have obviously solved chess, so, it doesn't really matter -- I think Chess shows how LLM's lack of a world model as Gary Marcus would say is a problem.