How we compare model quality in Cursor

Cursor has developed CursorBench, an internal evaluation suite, to assess the performance of coding agents more accurately than public benchmarks. Built on real sessions from their engineering team, CursorBench measures various dimensions of agent performance such as solution correctness, code quality, efficiency, and interaction behavior, making it more aligned with real-world developer outcomes than traditional benchmarks. The suite addresses the limitations of public benchmarks, which often fail to differentiate between models effectively due to issues like misalignment with actual coding tasks, narrow grading criteria, and contamination from training data. CursorBench's tasks are derived from actual developer queries and solutions, ensuring they are relevant and challenging, with the scope of correctness evaluations doubling since its inception. By combining online and offline evaluations, Cursor ensures that model quality aligns with developers' practical experiences, allowing for the identification of regressions that offline methods might miss. As development work evolves to involve long-running agents, Cursor plans to adapt CursorBench to maintain its relevance and effectiveness, aiming to continue improving the agent experience in production environments.

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