MMSciCode: Real-world Evaluation of Multilingual Multi-Discipline Scientific Research Coding

We introduce MMSciCode, a comprehensive expert-level, multilingual multi-discipline benchmark for evaluating foundation models in scientific code generation. It includes 624 expert-annotated research coding problems spanning six core scientific disciplines. Compared to prior benchmarks, MMSciCode features three key advancements. First, it challenges models to integrate domain-specific knowledge with algorithmic reasoning to implement core functions from research papers. Second, each problem is meticulously annotated by domain experts through a rigorous paper-grounded process, with strict quality controls implemented to ensure dataset integrity and authenticity. Finally, each problem is equipped with comprehensive unit test suites and con-tainerized environments, enabling reproducible and diagnostic evaluation of both functional correctness and domain validity. We conduct an extensive evaluation of 23 state-of-the-art foundation models and 2 coding agents on MMSciCode. We identify substantial performance gaps between models and human experts, providing actionable insights for advancing expert-level scientific code generation.