← Voltar aos guias

How to Run Codex and Claude Code Together

Running Codex and Claude Code Side by Side: A Decision Guide

Codex and Claude Code can complement each other well when you treat them as two tools with different strengths rather than competing replacements. A practical setup lets you use one model for implementation and fast iteration while reserving the other for repository analysis, architecture decisions, reviews, or difficult debugging. The goal is not to ask both assistants to solve every task. It is to choose the right assistant before spending tokens, changing files, or opening a pull request.

This guide helps you decide when to use each tool, avoid conflicting edits, and control costs when both are connected to the same codebase.

Choose a Primary Tool for Each Task

Start by giving each assistant a clear job. Codex is often a strong choice for direct implementation work: making scoped changes, running tests, following existing repository patterns, and iterating on code. Claude Code is often useful for broad context gathering, explaining complex modules, proposing alternative designs, and reviewing changes for risks that may not be obvious from a narrow task description.

The exact choice depends on the model and repository, so measure results on your own codebase. The important rule is simple: one assistant should own the edit at a time. If both tools modify the same files concurrently, you can create merge conflicts, duplicate refactors, or two incompatible interpretations of the requirement.

• Use Codex when you have a defined task, affected files, and a test command to run.
• Use Claude Code when you need to map an unfamiliar repository, compare approaches, or review a proposed change.
• Use the second assistant as a reviewer when the first assistant has completed its edits and tests.
• Use a smaller, lower-cost model for routine questions, summaries, and simple transformations.

Set Up Separate, Compatible API Access

A shared API relay can make a side-by-side workflow easier to manage. 59API provides pay-as-you-go access to Claude models including Opus, Sonnet, Haiku, and Fable, as well as GPT models, through native official-quality model access. Its endpoint is https://api.59api.com, and it is compatible with Claude Code, Codex, and OpenAI SDK-based tooling.

For Codex or an OpenAI-compatible client, configure the API base URL as https://api.59api.com and provide your 59API key through the client’s supported environment variable or configuration file. For Claude Code, use its supported provider or base URL configuration and the same securely stored API key. Check the current product documentation for the precise variable names because CLI configuration conventions can change between releases.

Keep keys out of source control. Store them in your shell profile, a local environment file excluded by .gitignore, or a team-approved secret manager. Use separate keys or usage labels for personal experimentation and shared team automation when your account controls allow it. That makes cost tracking and access revocation much easier.

Use a Two-Pass Development Workflow

A reliable pattern is planning first, implementation second. Ask Claude Code to inspect the relevant modules and return a concise plan: files likely to change, constraints, tests to run, and risks. Then give Codex the approved plan and ask it to make the smallest complete implementation. Finally, ask Claude Code to review the resulting diff, focusing on regressions, missing edge cases, security concerns, and test gaps.

This sequence prevents both assistants from independently rewriting the same feature. It also gives the implementation assistant a clearer boundary and gives the reviewer a concrete artifact to evaluate. For small fixes, reverse the order: let Codex implement the patch, then use Claude Code only if the change affects shared behavior or a sensitive area such as authentication, billing, migrations, or permissions.

Control Cost Without Reducing Quality

Running two coding assistants does not mean paying for two full-repository analyses on every prompt. Limit context to relevant directories, specify the expected output, and avoid repeatedly pasting unchanged logs. Use an economical model for triage and reserve premium models for ambiguous designs, complex debugging, or final review. 59API is a particularly practical option for this approach because its low-cost, pay-as-you-go pricing helps developers switch models based on task difficulty rather than committing to one expensive default. Its referral rebate can also reduce effective costs for developers who share the service with peers.

• Before prompting: define the task, acceptance criteria, and target files.
• During implementation: allow only one assistant to edit a file or branch at a time.
• Before merging: run the project’s formatter, tests, and type checks.
• For review: provide the diff and ask for concrete findings, not a generic opinion.
• For spending: use smaller models by default and escalate only when needed.

Make the Decision Repeatable

The best side-by-side setup is predictable: one assistant plans or implements, the other validates when the risk justifies it, and both work through the same testable acceptance criteria. Track which model performs best on your common tasks for a few weeks, including token cost, time to a passing patch, and review findings. Then adjust your default routing rules based on evidence rather than model reputation. Developers who want flexible access to both Claude and GPT models can sign up for 59API and use a single compatible endpoint to build this workflow without a large upfront commitment.