OpenAI 称 GPT-6 在 COBOL、Fortran 等遗留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

AI 导读

OpenAI 表示其最新模型针对 COBOL、Fortran、Visual Basic 等训练代码稀缺的“低资源”语言做了改进，并构建了两个评测来衡量进展。

摘要与补充说明

MultiLangBench 要求模型从零构建兼容 SQLite 的数据库引擎，在 20 种语言上，GPT-6 Astra 在最高推理强度下平均测试通过率为 87.4%，GPT-5.6 Terra 约为 38%；GPT-6 Luna 为 40.3%，略高于 GPT-5.6 Terra 的 37.9%，但成本低得多。

正文

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.

Average test pass rate on MultiLangBench across 20 programming languages. GPT-6 Astra and GPT-6.1 Sol both reach about 87 percent, while older models like GPT-5.6 Terra top out at around 38 percent. | Image: OpenAI

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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