Code refactoring tasks (OpenAI)
GPT-5-Codex 51.3% Dropped
Agents perform refactor-style tasks taken from large, established repositories in Python, Go and OCaml. One example threads a ctx variable through 232 files and 3,541 lines of Gitea.
OpenAI introduced it with GPT-5-Codex on 2025-09-15. The post does not say how tasks are graded, so verified is assumed from the repository-task setup.
Made by OpenAI Released Sep 2025 Fixing real repositories Tasks kept private Last in a launch post Sep 2025 , dropped before it saturated
Last model added Sep 2025
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
1
Model scored in lab cards
1
Launch post lists it, from 1 lab
GPT-5-Codex leads with 51.3%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 GPT-5-Codex 51.3%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-5-Codex | 51.3% | Sep 2025 | Model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Reported by the lab in its model card
No launch post has listed it since Sep 2025
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 15 Sep 2025 | GPT-5-Codex | 51.3 | Code refactoring tasks, GPT-5-Codex (high) |
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