OpenAI Says Its Newest Models Got Far Better at COBOL

OpenAI says its latest models are much better at writing and migrating software in low-resource programming languages such as COBOL, Fortran, OCaml and Visual Basic, languages with far less public code and documentation to learn from than Python or JavaScript. The company published the results on October 19, 2026.

OpenAI notes that COBOL supports 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 and nuclear reactor safety analysis.

The COBOL migration test

OpenAI’s second evaluation asks models to migrate COBOL programs to C++, Java, Kotlin, Python or Rust while preserving exactly what each program does. Hidden tests require byte-for-byte parity on observable outputs, and a migration only counts if every test passes. OpenAI points to details like COBOL’s fixed-point arithmetic, which drops extra decimal digits unless rounding is requested, as the kind of behavior a rewrite can silently break.

• GPT-6.1 Sol scored 86.6% at low reasoning effort.
• GPT-5.6 Sol scored 5.4% at the same setting, while costing more: GPT-6.1 Sol ran at 61% of its API cost.
• At higher reasoning effort, GPT-6.1 Sol reached 97.9%, which OpenAI says brings the test close to saturation.

Building SQLite from scratch

The first evaluation, MultiLangBench, asks a model to build a SQLite-compatible database engine in each of 20 programming languages, using tests adapted from SQLite’s own suite.

• GPT-6 Astra averaged an 87.4% test pass rate at maximum reasoning effort, up from 26.8% for GPT-5.6 Sol, and improved in every language.
• COBOL stayed the hardest language for Astra, rising from 10.9% to 72.1%. Fortran went from 21.4% to 83.5%, OCaml from 25.6% to 85.8%, and Visual Basic from 27.5% to 89.4%.
• The cheaper GPT-6 Luna reached 40.3% at an estimated $0.77 per sample, about 78% less than GPT-5.6 Sol’s $3.48.

What’s next

OpenAI says it is releasing the MultiLangBench setup and test suite on GitHub, with per-language results. It also cautions that using these models on production software still requires expert review and testing against each system’s business rules and dependencies. All of the figures above come from OpenAI’s own evaluations.