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
OpenAI 表示其最新模型针对 COBOL、Fortran、Visual Basic 等低资源语言做了改进，这些语言训练代码远少于 Python，但支撑着全球关键基础设施。OpenAI 构建了两个评测：MultiLangBench 要求模型从零构建兼容 SQLite 的数据库引擎，GPT-6 Astra 在最高推理强度下 20 种语言平均测试通过率 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% 但成本更低。在将 COBOL 转译为 C++、Java、Kotlin、Python 或 Rust 的迁移基准上，GPT-6.1 Sol 低推理强度下准确率 86.6%，GPT-5.6 Sol 为 5.4%，提高推理强度后可达 97.9%。
推荐理由
AI 评分 71
原文给出 GPT-6 系列在 COBOL、Fortran 等低资源语言上的基准对比，可看新旧模型在遗留系统迁移上的差距。
主题
正文
当前显示原文，中文全文译文尚未就绪。
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.