SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire
Scott C. Lowe (Vector Institute) and Sander W. Keemink (Radboud University) present SpireBench, in which LLMs play the full commercial deck-builder Slay the Spire at its highest difficulty, and use it to locate where long-horizon agent play fails.
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Results across 20 LLMs. Frontier models beat heuristic baselines and match the average human player, but win the full game 0-10% of the time against 46-65% for expert streamers.
The gap grows with run length. The three strongest models win 90% of Act 1 runs, as experts do (85-92%), but 40-50% of Act 2 against 65-78%, and 0-20% of Act 3 against 56-71%.
Risk misjudgment. Models preserve health instead of spending it to get stronger. At the Act 1 boss the top models hold a median 90% of maximum HP against 53% for streamers, and they rest at 57-75% of rest sites against 9% for streamers.
Memory does not help. Models do not improve from memories written across runs: 17% of stored memories are false and advice to be cautious outnumbers advice to take risks seven to one.
Prior knowledge. A consistent renaming of every game term lowers performance, so part of the score comes from knowledge of the game in pretraining.
Abstract
Agentic large language models now tackle complex tasks, yet strategic reasoning over long horizons is under-studied: most agentic benchmarks are short and episodic, and performance on long tasks is hard to dissect into stored knowledge, reasoning and luck. We introduce SpireBench, in which models play the complete commercial deck-builder Slay the Spire (StS), where early decisions decide long-term success. Across 20 large language models (LLMs) at the game's highest difficulty, frontier models beat heuristic baselines and match the average human player but remain far behind experts. This gap widens as the run lengthens: the strongest models win the first act as often as experts, then fall behind. What separates strong models from weak is how well they play each fight; all of them misjudge risk over a run, preserving health rather than spending it to grow stronger. A comprehensive, self-consistent renaming of every game term shows that part of their performance rests on prior knowledge of the game, and models do not improve from the memories they write across runs, which are often wrong and overly cautious. Our harness is the first to give a model the complete original game, and we compare it with existing harnesses. Overall, SpireBench offers an unsaturated long-horizon benchmark with tools to locate where and why agents fail.