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There is no proven universal speed winner among Claude Code, Codex, and Cursor. The strongest direct comparison found measures whether reviewed agent-authored pull requests were accepted—not how long a developer took to produce a correct, maintainable change. Its results vary by task, so your best choice depends on your work and workflow. To find out which makes you faster, compare them on the same kinds of tasks and count review, correction, and testing time as well as the initial coding.

What the direct comparison 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 agents in the AIDev dataset. It found that acceptance varied by task category and that no agent led in every category. The authors’ outcome was pull-request acceptance, not elapsed time or developer-hours saved. Read the study.

That distinction matters: an accepted pull request is evidence about an observed code outcome, not a stopwatch result. It does not establish which tool will get you to a correct, maintainable change sooner. The study is observational, its sample sizes are uneven, and it does not control for developer expertise or repository characteristics. You should not treat its acceptance rates—or a model benchmark score—as a personal speed ranking.

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Observed strengths differ by task

The study reports an 82.1% acceptance rate for documentation tasks versus 66.1% for new features in its abstract. Those figures compare task types; they do not compare tools or show that documentation work is faster. The study also reports a range of 59.6% to 88.6% for Codex across nine task categories, with 83.0% for fixes and 74.3% for refactors. For Claude Code, it reports 92.3% for documentation and 72.6% for feature tasks; the authors flag the documentation result as based on few samples. Cursor’s detailed results report 77.8% acceptance on test tasks, also with a small sample.

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The paper’s abstract describes Cursor’s strength as fixes at 80.4%, while its detailed results report 77.8% for test tasks and Codex at 83.0% for fixes. These are different summaries and task results, so they should not be collapsed into a claim that Cursor wins fixes. The sound takeaway is that observed results depend on the task mix.

A historical overall result is not a speed leaderboard

In a sensitivity analysis using a common 11-week observation window from May 19 through July 30, 2025, the study reports overall acceptance rates of 79.9% for Codex, 74.4% for Cursor, and 72.6% for Claude Code. These are acceptance rates in that selected historical sample—not time saved, a controlled same-user comparison, or a guarantee about current versions.

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What the post-merge evidence adds

A separate 2026 preprint by Obada Kraishan analyzes 37,623 provenance-labeled pull requests across 2,807 public repositories, with activity from December 2024 through July 2025, and follows merged changes for 90 days. It reports a 6.1% revert rate for Codex-authored pull requests versus 11.5% for its matched human baseline in that sample. That is a repository-outcome measure, not a speed measure, and it does not prove Codex is better for an individual developer. The paper notes limits involving repository and language coverage and the small representation of Claude Code. Read the study.

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How to choose based on your real workflow

Start with how you work, rather than assuming a product label predicts speed. The useful comparison is whether a tool fits your preferred working surface and task style, then how much time it takes to reach a change you can accept.

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  • Working surface: Decide whether you prefer an editor, a terminal, or a delegated or remote task flow. Check each product’s current documentation for the workflow it supports: Claude Code documentation, Codex documentation, and Cursor documentation.
  • Task style: Include both inline completion and broader changes made at an agent’s direction if you use both. A tool that helps with one kind of work may not help equally with another.
  • Repository fit: Measure the setup and context you need, whether project instructions are followed, and whether the tool runs the checks your project relies on.
  • Whole-task time: Count prompting, waiting, review, corrections, and tests—not just the time until code first appears.
  • Practical constraints: Check current prices and usage limits against your actual workload. They can change, and no current price or limit figures are established here.
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Run a matched trial to find your speed winner

Use the same repository state and project instructions for each tool. Choose a small set of representative tasks—at minimum, a bug fix, a feature change, and a refactor or documentation task if those reflect your work. This is a practical way to answer the personal-speed question; it is not a published experiment or a result established by the cited studies.

  1. Choose comparable tasks. Use real work that is small enough to repeat and has a clear definition of done. Avoid comparing an easy documentation edit in one tool with a difficult feature in another.
  2. Keep the starting point consistent. Start each attempt from the same repository state and provide the same project instructions and task requirements.
  3. Record the conditions. Note the date, tool and model version, configuration, and any setup needed. Product versions and workflows change, so results are most useful when you know what you tested.
  4. Track the complete effort. Record active time, agent waiting time, review and correction time, test results, whether the final change is accepted by your reviewer, and any usage or cost constraints.
  5. Repeat noisy comparisons. If one run is unusually smooth or difficult, repeat the task before deciding. Compare results by task type as well as overall; a single blended score can hide where a tool helps or slows you down.

Choose the tool that produces acceptable changes with the least total effort on the work you actually do. If the result is close, workflow preference and practical constraints can decide the matter; the available comparative evidence does not settle a universal winner.

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