OpenAI is publishing MultiLangBench, a set of evaluations measuring how well its newest models handle low-resource programming languages — the ones with far less training data and documentation than Python or JavaScript. The company is posting the setup, test suite and per-language results on GitHub, and says enterprise developers had told it coding models often struggle exactly here.

The headline numbers are in legacy migration: OpenAI reports its models reaching 86.6% accuracy on COBOL migration tasks. The stakes are real — COBOL still supports more than 40% of online banking systems and 95% of ATM transactions by the figures OpenAI cites, while Fortran remains load-bearing in weather forecasting and nuclear safety analysis, and the pool of engineers who can maintain these systems keeps shrinking.

OpenAI is careful to add that applying the models to production software 'still requires expert review and testing against the system's business rules and dependencies.' Even with that caveat, the push signals where ChatGPT and Codex are headed next: the messy legacy codebases companies actually live with, not just greenfield demos.