GPT-6 Astra lifts low-resource coding languages on MultiLangBench
OpenAI says GPT-6 is much better with legacy programming languages such as COBOL and Fortran
Legacy COBOL systems still run banking and government payroll worldwide, and a 97.9 percent migration accuracy claim suggests AI-assisted mainframe modernization is becoming practical rather than experimental. Teams maintaining COBOL or Fortran codebases should evaluate GPT-6.1 Sol against Anthropic's Claude and other coding assistants on their own migration test suites. The MultiLangBench scores also give enterprises a benchmark for comparing models beyond mainstream languages like Python.
OpenAI reports major gains for its models on low-resource programming languages such as COBOL, Fortran, and Visual Basic. On the new MultiLangBench, GPT-6 Astra achieves an 87.4 percent test pass rate across 20 languages, up from about 38 percent for GPT-5.6 Terra. For COBOL migration specifically, GPT-6.1 Sol reaches up to 97.9 percent accuracy. OpenAI says the result is relevant to modernizing critical financial infrastructure still written in legacy code.
OpenAI says GPT-6 is much better with legacy programming languages such as COBOL and Fortran
OpenAI improved its latest models for low-resource programming languages like COBOL, Fortran, and Visual Basic. On the new MultiLangBench, GPT-6 Astra hits an 87.4 percent test pass rate across 20 languages, up from about 38 percent for GPT-5.6 Terra. For COBOL migration specifically, GPT-6.1 Sol reaches up to 97.9 percent accuracy, a result that could matter for modernizing critical financial infrastructure. The article OpenAI says GPT-6 is much better with legacy programming languages such as COBOL and Fortran appeared first on The Decoder .
