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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Neither OpenAI Codex nor Claude Code is a defensible all-purpose winner. The better fit depends on the kinds of changes your team makes, how you want agents to work in your repositories, and what usage limits and controls your plan provides. A 2026 study of 7,156 pull requests found that acceptance varied substantially by task type, while the products also offer different ways to delegate and supervise coding work. Use the study as context—not as a universal leaderboard—and compare both tools on representative tasks from your own codebase.
What does the benchmark actually say?
The paper “Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance” by Pinna, Gong, Williams, and Sarro analyzed 7,156 agent-attributed pull requests in the AIDev dataset. Revised May 7, 2026, and accepted to the MSR ’26 Mining Challenge Track, it highlights why results should be separated by task rather than compressed into one ranking.
| Study finding | How to interpret it |
|---|---|
| Documentation PRs had 82.1% acceptance; new-feature PRs had 66.1%. | In this dataset, task type was a major factor. The difference is not a prediction of acceptance for an individual team’s work. |
| Claude Code had 92.3% acceptance for documentation and 72.6% for features. | These are study-specific results for those categories, not a guarantee about current models, prompts, or repositories. |
| Codex ranged from 59.6% to 88.6% across nine task categories. | The spread reinforces that a single overall score can hide meaningful differences between kinds of work. |
This was not a randomized head-to-head trial in which both agents received identical prompts on the same repositories, hardware, and model versions. The measured outcome was pull-request acceptance. It does not establish which tool is faster, produces more secure code, saves more developer time, or will perform better on your project.
Which work do you need the agent to do?
Start with the team’s task mix, not a headline rank. Documentation updates, feature work, fixes, and other change types can have different review and acceptance patterns. The study’s results make task category a sensible axis for evaluation, but they do not tell you which agent will win on your codebase.
#1 Best Overall
List a few recurring jobs and evaluate each separately. For example, a team might choose a documentation change, a small bug fix, and a modest feature, then check whether each result is accepted as-is, how much correction it needs, and how much review it creates. Those are practical pilot measures, not outcomes established by the cited study.
How do their workflows differ?
OpenAI describes Codex as an agent for writing, reviewing, and shipping code. Its documented access routes include desktop, CLI, IDE extension, web, and cloud. In cloud use, tasks run on OpenAI-managed computers; local workflows run on the user’s device. The available usage allowance and limits vary by ChatGPT plan. See OpenAI’s Codex plan guide.
Anthropic describes Claude Code as an agentic coding tool that reads a codebase, edits files, runs commands, and integrates with development tools. It is documented for terminal, IDE, desktop, and browser use. Most of these surfaces require a Claude subscription or Anthropic Console account. See Anthropic’s Claude Code overview.
Codex’s announced app workflow supports multiple agent threads and isolated Git worktrees. That can matter to teams that want to run work in parallel while keeping changes separated. Claude Code’s documented surfaces provide several ways to use the agent, but the cited overview does not establish an equivalent worktree-based workflow. Choose based on how your team prefers to assign, isolate, inspect, and merge agent work—not on surface count alone.
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What should you compare about permissions and deployment?
Both vendors describe controls for limiting what an agent can access or do. These are vendor descriptions of product behavior, not independent evidence that one tool is categorically safer. Actual boundaries depend on configuration, the work being requested, and the environment in which the agent runs.
- Codex: OpenAI says the app workflow defaults to limiting edits to files in the working folder or branch and requests permission for commands requiring elevated access, such as network access. Cloud tasks run on OpenAI-managed computers rather than the user’s local device. Details may change; consult OpenAI’s Codex app announcement.
- Claude Code: Anthropic documents manual and auto permission modes, sandboxed Bash with filesystem and network isolation, and prompts for access to files outside the working directory in Manual mode. Anthropic also says users remain responsible for reviewing proposed code and commands. See Anthropic’s Claude Code security documentation.
Before a team rollout, check the precise settings available on the plan and surface you intend to use. Decide which repositories, files, shell commands, network access, and secrets the agent may reach, and who reviews changes before they are merged. A vendor’s description of safeguards does not replace your own access-control and code-review decisions.
Rank #4
How should you compare pricing and usage?
Do not treat “included with a plan” as unlimited or compare subscription prices without accounting for limits. OpenAI says Codex is available across ChatGPT plans, but allowances and limits vary; the applicable cost therefore depends on the plan, market, and expected use. Check OpenAI’s current plan details for the account you would use.
Anthropic’s pricing page, checked October 3, 2026, listed Claude Pro at $20 when billed monthly or $17 per month with annual billing, and Claude Max starting at $100 monthly. Anthropic notes that plans and prices can change. These figures describe the listed subscription billing, not a guarantee of a particular coding workload’s capacity or total cost. Confirm the current terms on Anthropic’s pricing page.
Best Value
For a team decision, estimate usage against real work and include any relevant organizational requirements and plan controls. The evidence cited here does not establish a general cost per accepted change or a productivity gain for either product.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to run a fair pilot on your repository
A short, structured trial is more useful than choosing from a broad benchmark. This recommendation follows from the study’s task-category variation and the products’ different execution workflows; it is not a claim that either tool was tested here.
- Select representative work. Choose a small set of realistic tasks from different categories your team handles, such as documentation, a fix, and feature work.
- Make the comparison fair. Use equivalent repository states, task descriptions, permissions, and review expectations. Record which product version, plan, and workflow surface you used, since these can change.
- Apply the same acceptance criteria. Have reviewers assess correctness, maintainability, tests, and whether the change is ready for the team’s normal review process.
- Record practical effort. Track acceptance, correction effort, review burden, and usage against the plan limits. Do not substitute a single pass/fail score for the work your team actually cares about.
- Review boundaries as well as output. Confirm that each tool’s access and execution behavior fits the repositories and policies where it would be used.
Keep results task-specific. If one tool is a better fit for a particular category or workflow, a blended team approach may make more sense than naming a universal winner.
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