Advancing Coding in Low-Resource Programming Languages

Our latest model advances bring stronger capabilities for building and migrating software in low-resource programming languages such as COBOL, Fortran, and Visual Basic. These languages have less code and documentation available for models to learn from than languages like Python and JavaScript, yet support critical infrastructure and essential services around the world. COBOL supports⁠(opens in a new window) more than 40% of online banking systems, 80% of in-person credit card transactions and 95% of ATM transactions, while Fortran is used in weather forecasting⁠(opens in a new window) and nuclear reactor safety analysis⁠(opens in a new window). Improving AI’s ability to work in these languages could help teams maintain and modernize the software that essential services depend on.

This work is guided by feedback from developers and enterprise customers who have told us that coding models often struggle with these languages. Maintaining and modernizing this software can be difficult: documentation may be incomplete, original developers may have left the company, and important business rules may only exist in code. Whether teams are fixing bugs or migrating to another language, they need to preserve the behavior their systems depend on.

To measure progress, we developed two evaluations. MultiLangBench tests whether a model can build a SQLite-compatible database engine from scratch across 20 different programming languages. Our COBOL migration evaluation tests whether a model can migrate existing programs to another language while preserving their behavior. Together, they test two complementary skills: building new software and preserving what existing software does.

Our latest models show substantial improvements across both evaluations, delivering much higher accuracy at lower cost.

Together, these results show that our models are becoming significantly better at working with a wide range of programming languages, while also becoming more cost-efficient.