There is no proven universal speed winner among Claude Code, Codex, and Cursor. The strongest direct comparison located measures whether agent-authored pull requests were accepted—not how long developers took to produce a correct, maintainable change. Its findings vary by task, so the useful answer is to compare the tools on your own work and count review and correction time, not just how quickly code appears.
What the available comparisons can—and cannot—tell you
A 2026 study by Giovanni Pinna, Jingzhi Gong, David Williams, and Federica Sarro analyzed 7,156 reviewed pull requests from five coding agents in the AIDev dataset. It found that acceptance varied substantially by task category and that no agent led across every category. Acceptance is a useful quality signal, but it is not elapsed time, developer-hours saved, or proof that a tool will be faster for you. The study is observational, with uneven sample sizes, and does not control for user expertise or repository characteristics. Read the study.
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The study reports 82.1% acceptance for documentation tasks versus 66.1% for new features in its abstract. That comparison describes task categories, not a head-to-head speed result. The authors’ conclusion reports a different, 29-percentage-point gap between task types; those figures reflect distinct parts of the paper and should not be merged into a single estimate.
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Different tasks produced different observed strengths
- Codex: Acceptance ranged from 59.6% to 88.6% across nine categories. The detailed results report 83.0% for fixes and 74.3% for refactors.
- Claude Code: The paper reports 92.3% acceptance on documentation tasks and 72.6% on feature tasks. The authors flag the documentation result as based on few samples.
- Cursor: The abstract reports 80.4% for fix tasks, while the detailed results report 77.8% on test tasks and identify that as a small-sample result. The paper also reports Codex at 83.0% on fixes, so its summary and detailed task breakdown do not support a simple claim that Cursor wins fixes.
These are observed acceptance rates in that dataset, not predictions of how long your tasks will take. Task mix matters: a comparison dominated by documentation can produce a different overall impression from one dominated by features or tests.
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The historical window is not a current speed ranking
In a sensitivity analysis restricted to May 19–July 30, 2025, the study reports overall acceptance of 79.9% for Codex, 74.4% for Cursor, and 72.6% for Claude Code. Those are rates in a selected historical sample, not time saved. Tool and model versions change, so these figures cannot establish which current setup will make an individual developer faster.
Does post-merge quality settle the choice?
No. A separate 2026 preprint by Obada Kraishan follows merged changes for 90 days across 37,623 provenance-labeled pull requests in 2,807 public repositories. In that sample, Codex-authored pull requests had a 6.1% revert rate, compared with 11.5% for a matched human baseline. The author describes activity from December 2024 through July 2025; the paper also notes limits in repository and language coverage and a small Claude Code representation. These observed rates are neither a speed measure nor proof that Codex is superior for an individual developer. Read the study.
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Choose by the workflow you want to test
The useful distinction is not an assumed speed advantage; it is how each tool fits the way you work. Official documentation is the right place to verify current interfaces, setup, model options, permissions, background work, and usage limits. The references below do not establish a current, controlled feature-by-feature comparison, so check the documentation for the setup you plan to use.
- Work surface: Decide whether you prefer to work mainly in an editor, a terminal, or a delegated task flow. Try the tools in the surface you will actually use.
- Change size: Consider whether your routine depends more on inline completion or on asking an agent to make broader changes.
- Repository context: Track how much setup and project context you need to provide before the agent can make a useful change.
- Project conventions and checks: Observe whether it follows your project instructions and runs the checks your team expects. This is a trial criterion, not an established winner.
- Limits and cost: Check current official terms against your real workload; prices and usage limits can change.
Official documentation: Claude Code, OpenAI Codex, and Cursor.
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Run a matched trial and measure the finished change
A short, controlled personal trial is more relevant than applying an old aggregate ranking to your own work. Use the same repository state and instructions in each tool, and select tasks that resemble your normal workload.
- Choose representative tasks. Include a bug fix, a feature change, and a refactor or documentation task if those occur in your work.
- Hold the starting conditions steady. Start each attempt from the same repository state and give each tool the same project instructions. Record the model or version, configuration, and date.
- Measure the whole task. Record active work time, agent waiting time, review time, and time spent correcting the result. Do not stop the clock when code is first generated.
- Check the outcome. Record whether tests pass and whether the final change is acceptable to your reviewer. A fast draft that needs extensive repair may not be a faster completed task.
- Note practical constraints. Track usage or cost limits that affect your real workflow, and repeat tasks if the results seem noisy.
This is a practical way to answer the question for your own workload, not a published experiment. The best choice is the tool that repeatedly gets your representative changes to an acceptable, tested result with the least total effort.
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