OpenAI claims GPT-6 shows major accuracy gains in legacy programming languages like COBOL

OpenAI announced on October 20, 2026, that its latest AI models have shown significant progress in handling "low-resource" programming languages. These include COBOL, Fortran, and Visual Basic. While these languages have far less available training code than modern languages like Python, IBM estimates that COBOL alone powers more than 40 percent of online banking systems and 95 percent of all ATM transactions worldwide.

According to two evaluations built by OpenAI, the GPT-6 Astra model achieved an average test pass rate of 87.4 percent across 20 languages on MultiLangBench, a substantial increase from approximately 38 percent for the previous generation, GPT-5.6 Terra. The MultiLangBench task requires models to build a SQLite-compatible database engine from scratch.

On a specific COBOL migration benchmark, which involves translating COBOL into C++, Java, Kotlin, Python, or Rust, GPT-6.1 Sol reached 86.6 percent accuracy at low reasoning effort, compared to just 5.4 percent for GPT-5.6 Sol. Increasing the reasoning effort pushed the accuracy up to 97.9 percent.

It is important to note that these figures are based on OpenAI's internal evaluations and have not been independently reproduced. Furthermore, the high accuracy rates were achieved under specific configurations, such as maximum reasoning effort, which may not reflect typical production use cases due to cost and latency constraints.