Will a large language model beat a super grandmaster playing chess by EOY 2028?

Question

If a large language models beats a super grandmaster (Classic elo of above 2,700) while playing blind chess by 2028, this market resolves to YES.

I will ignore fun games, at my discretion. (Say a game where Hiraku loses to ChatGPT because he played the Bongcloud)

Some clarification (28th Mar 2023): This market grew fast with a unclear description. My idea is to check whether a general intelligence can play chess, without being created specifically for doing so (like humans aren't chess playing machines). Some previous comments I did.

1- To decide whether a given program is a LLM, I'll rely in the media and the nomenclature the creators give to it. If they choose to call it a LLM or some term that is related, I'll consider. Alternatively, a model that markets itself as a chess engine (or is called as such by the mainstream media) is unlikely to be qualified as a large language model.

2- The model can write as much as it want to reason about the best move. But it can't have external help beyond what is already in the weights of the model. For example, it can't access a chess engine or a chess game database.

I won't bet on this market and I will refund anyone who feels betrayed by this new description and had open bets by 28th Mar 2023. This market will require judgement.

Update 2025-21-01 (PST) (AI summary of creator comment): - LLM identification: A program must be recognized by reputable media outlets (e.g., The Verge) as a Large Language Model (LLM) to qualify for this market.

Self-designation insufficient: Simply labeling a program as an LLM without external media recognition does not qualify it as an LLM for resolution purposes.

Update 2025-06-14 (PST) (AI summary of creator comment): The creator has clarified their definition of "blind chess". The game must be played with the grandmaster and the LLM communicating their respective moves using standard notation.

Update 2025-09-06 (PST) (AI summary of creator comment): - Time control: No constraints. Blitz, rapid, classical, or casual online games all count if other criteria are met.

“Fun game” clause: Still applies, but the bar to exclude a game as "for fun" is high; unusual openings or quick, unpretentious play alone don't make it a "fun" game.

Super grandmaster: The opponent must have the GM title and a classical Elo rating of 2700 or higher.

Update 2025-09-11 (PST) (AI summary of creator comment): - Reasoning models are fair game (subject to all other criteria).

Update 2025-09-13 (PST) (AI summary of creator comment): Sub-agents/parallel self-calls

An LLM may spawn and coordinate multiple parallel instances of itself (same model/weights) to evaluate candidate moves or perform tree search, including recursively. This is considered internal reasoning and is allowed.

Using non-LLM tools or external resources (e.g., chess engines like Stockfish, databases) remains disallowed.

Update 2025-12-20 (PST) (AI summary of creator comment): Coding a chess engine is not allowed: If an LLM codes up a chess engine (e.g., in Python) and uses it to play, this does not count for resolution. The creator is interested in chess-playing ability as an emerging characteristic of the LLM itself, not through reliance on coded external tools.

Update 2026-06-15 (PST) (AI summary of creator comment): Clarification on what counts as a qualifying LLM vs. a disqualified specialist model:

Specialist/fine-tuned models (e.g., GPT-5.4-rosalind, GPT-5.4-cyber style releases explicitly trained for a specific task) do not qualify.

A model where strong chess ability is an emerging capability (not an explicit training target) does qualify, analogous to how Claude Mythos's cyber ability was emergent rather than targeted.

If a lab publicly admits goal-seeking chess performance (e.g., heavy post-training specifically for chess), it will not count.

If a lab secretly trains heavily on chess and the creator lacks clear evidence of this at resolution time, the market resolves YES.

Chess training as a small fraction of normal training (e.g., ~2% of flops for general reasoning gains) is considered fair game.

Manifold Markets · Answer

Likely — Manifold Markets prediction market estimates a 65% chance (2,241 traders, as of Oct 18, 2026).