By Eric Lu, Ben Pan, Deniz Birlikci, Sam Lee, Ray Wang, Rohan Choudhury, Fermi Ma, TC Qin, Carlo Baronio, Silas Alberti, and more → 06.08.26 Raising the bar from correctness to quality Today’s coding benchmarks have established that models can write correct code. But as AI-generated code becomes the dominant path to production, correctness is now table stakes. The question that we should be asking is: can models actually write good code? We’re excited to introduce FrontierCode, a benchmark that measures how well models can truly meet the standards of high-quality production codebases. What sets us apart: Would the maintainer actually merge this PR? We’re the first benchmark to measure code mergeability. Our criteria assess end-to-end code quality — correctness, test quality, scope discipline, style, and adherence to codebase standards. This employs a novel ensemble of grading techniques, including unit tests, rubrics, and new types of verifiers. Crafted by open-source maintainers.…