FrontierCode 榜单：Codex 里的 V4.1 Flash 52.6% 仅次于 Opus 5.5

Mahone @Mahone_AI原推发布于 2026.10.23

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Artificial Analysis just switched the coding part of its Coding Agent Index to FrontierCode, Cognition's benchmark that grades a change on whether a maintainer would merge it. That's a better question than whether a model got some unit tests to pass. A patch can go green and still be something nobody would accept. I'd still be careful reading the middle of the table. FrontierCode has 100 tasks. A failed attempt scores zero and a passing one scores somewhere between 70% and 100%, so each task is worth up to a point. At around 45%, ordinary sampling noise on 100 tasks is roughly five points either way. Fifteen of the 24 configurations land between 43% and 49%. I wouldn't rank those against each other with much confidence. The index also scores a model and its harness together. That's realistic, since nobody runs a model without one, but it means a gap between Codex with one model and Claude Code with another doesn't tell you which half earned it. Some people asked why DeepSeek V4.1 Flash ran in Codex instead of DeepSeek's own harness. It didn't seem to hurt much. Flash in Codex came second on FrontierCode at 52.6%, behind Opus 5.5 in Claude Code at 56.0%. The part a merge-or-not score doesn't capture is the second try. In real work the first approach often gets rejected. Then the agent has to change course in the same repo without dragging the failed attempt along or leaving stray edits behind. That recovery is a lot of what you actually pay for. Cost makes that more interesting. On the index's cost frontier, Codex with Flash scores 47.3 at $0.39 a task, while Claude Code with Opus 5.5 scores 61.7 at $11.42. If a cheaper setup recovers well on the second try, the gap could look quite different from how it looks on first attempts.