LLMsPark: A Benchmark for Evaluating Large Language Models in Strategic Gaming Contexts

• URL: http://arxiv.org/abs/2509.16610v1
• Date: Sat, 20 Sep 2025 10:21:17 GMT
• Title: LLMsPark: A Benchmark for Evaluating Large Language Models in Strategic Gaming Contexts
• Authors: Junhao Chen, Jingbo Sun, Xiang Li, Haidong Xin, Yuhao Xue, Yibin Xu, Hao Zhao,
• Abstract summary: We present a game theory-based evaluation platform that measures large language models' decision-making strategies and social behaviors in classic game-theoretic settings.<n>Our system cross-evaluates 15 leading LLMs using leaderboard rankings and scoring mechanisms.<n>This work introduces a novel perspective for evaluating LLMs' strategic intelligence, enriching existing benchmarks and broadening their assessment in interactive, game-theoretic scenarios.
• Score: 19.97430860742638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) advance across diverse tasks, the need for comprehensive evaluation beyond single metrics becomes increasingly important. To fully assess LLM intelligence, it is crucial to examine their interactive dynamics and strategic behaviors. We present LLMsPark, a game theory-based evaluation platform that measures LLMs' decision-making strategies and social behaviors in classic game-theoretic settings, providing a multi-agent environment to explore strategic depth. Our system cross-evaluates 15 leading LLMs (both commercial and open-source) using leaderboard rankings and scoring mechanisms. Higher scores reflect stronger reasoning and strategic capabilities, revealing distinct behavioral patterns and performance differences across models. This work introduces a novel perspective for evaluating LLMs' strategic intelligence, enriching existing benchmarks and broadening their assessment in interactive, game-theoretic scenarios. The benchmark and rankings are publicly available at https://llmsparks.github.io/.

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