OpenAI 称 GPT-6 在 COBOL、Fortran 等老旧编程语言上表现大幅提升
OpenAI says GPT-6 is much better with legacy programming languages such as COBOL and Fortran
OpenAI 针对 COBOL、Fortran、Visual Basic 等训练代码稀缺的老旧语言改进了最新模型。在自建的 MultiLangBench 上，GPT-6 Astra 在 20 种语言上的平均测试通过率为 87.4%，GPT-5.6 Terra 约为 38%；GPT-6 Luna 为 40.3%，成本更低。
原文给出了 GPT-6 系列在 COBOL、Fortran 等低资源语言上的评测数字，读者可据此判断旧系统迁移的可行性。
OpenAI improved its latest AI models for "low-resource" languages like COBOL, Fortran, and Visual Basic. These languages have far less available training code than something like Python, but according to OpenAI, they prop up critical infrastructure worldwide. IBM estimates that COBOL powers more than 40 percent of online banking systems and 95 percent of all ATM transactions.
OpenAI built two evaluations to measure progress. MultiLangBench (GitHub) asks models to build a SQLite-compatible database engine from scratch. With maximum reasoning effort, GPT-6 Astra hits an average test pass rate of 87.4 percent across 20 languages, up from about 38 percent for GPT-5.6 Terra. GPT-6 Luna scores 40.3 percent, barely edging out GPT-5.6 Terra (37.9 percent) but at a much lower cost.
On the COBOL migration benchmark, which translates COBOL into C++, Java, Kotlin, Python, or Rust, GPT-6.1 Sol reaches 86.6 percent accuracy at low reasoning effort, compared to 5.4 percent for GPT-5.6 Sol. Cranking up reasoning effort pushes accuracy to 97.9 percent.
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来源：The Decoder · the-decoder.com