OpenAI says GPT-6.1 Sol is dramatically better at migrating legacy code

OpenAI confirmed that its newer models are much better at writing and migrating software in older programming languages.

The company lists programming languages such as COBOL, Fortran, and Visual Basic as a few examples where it has seen significant improvements.

OpenAI says COBOL supports more than 40% of online banking systems, 80% of in-person credit card transactions, and 95% of ATM transactions, while Fortran is still used in areas such as weather forecasting and nuclear reactor safety analysis.

"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," OpenAI explained in a blog post.

To measure progress, OpenAI created MultiLangBench, which asks models to build a SQLite-compatible database engine from scratch across 20 programming languages.

GPT-6 Astra reached an average test pass rate of 87.4%, up from 26.8% for GPT-5.6 Sol. In COBOL specifically, Astra jumped from 10.9% to 72.1%, while Fortran improved from 21.4% to 83.5%.

"Improvements extend to every language tested, including COBOL (10.9% to 72.1%), OCaml (25.6% to 85.8%), Fortran (21.4% to 83.5%), and Visual Basic (27.5% to 89.4%)," OpenAI noted.

GPT-6.1 Sol is much better at migrating COBOL

OpenAI also tested whether its models could migrate COBOL programs to C++, Java, Kotlin, Python, or Rust without changing their behaviour.

"GPT-6.1 Sol achieves 86.6% accuracy at low reasoning effort, compared with GPT-5.6 Sol's 5.4% at the same setting at just 61% of the API cost," OpenAI said. "At higher reasoning effort, accuracy reaches 97.9%, bringing the evaluation close to saturation."

This work could help companies modernize older software, although production migrations would still require expert review and testing.