Code refactoring tasks (OpenAI)
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 September 15th, 2025. The post does not say how tasks are graded, so verified is assumed from the repository-task setup.
- Made by
- OpenAI
- Released
- September 2025
- Kind
- Fixing real repositories
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- Kept private
- Last in a launch post
- September 2025, dropped before it saturated
- Last model added
- September 2025
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%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-5-Codex | 51.3% | Sep 2025 | Lab model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
No launch post has listed it since September 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 |
|---|---|---|---|
| Sep 15, 2025 | GPT-5-Codex | 51.3 | Code refactoring tasks, GPT-5-Codex (high) |
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