ChessFormer introduces a novel searchless chess engine leveraging transformer architecture to approximate human decision-making in chess. Trained on a vast dataset of 3 billion chess positions, our model learns its entire decision-making process directly from training data. Evaluations show an improvement in human move-matching accuracy over prior models in high-Elo ranges and the model’s ability to distinguish between human and algorithmic decision-making, offering potential applications in chess analysis or cheat detection.