CoffeeBench: Long-horizon Task Benchmark for LLM Agents in Multi-agent Economic Environments

Sakana AI · 38

Sakana AI released CoffeeBench, a benchmark that evaluates large language model agents' long-term decision-making ability by simulating a 90-day coffee supply chain business environment with multiple competing agents. Different LLM models showed significant performance variation, with high-performing models actively engaging in negotiation and communication while some models like Claude Haiku 4.5 exhibited a phenomenon of thinking without acting, repeating wait actions instead of executing planned strategies. The benchmark serves as a foundation to research agent behavior in multi-agent economic environments and could be extended to study potential misconduct scenarios such as circular trading when agents face artificial sales targets.