Choosing Between Claude and Codex: How I Decide Which AI to Hand Off Work to in the Same Repository
About This Article
This site uses two AIs — Claude Code and Codex — in the same repository. Each has different strengths, so I use them depending on the type of work. This article documents the criteria I apply.
The Basic Difference Between Claude and Codex
Claude Code is an AI that works in real time through a terminal conversation. When a task involves judgment calls — “how should this be designed?” or “what is causing this error?” — the back-and-forth conversation makes progress possible.
Codex (OpenAI) processes changes to a GitHub repository asynchronously. After giving it instructions, I wait for Codex to commit the changes. There is no real-time conversation.
This is not a comparison of which is better. The two tools suit different kinds of work.
What I Give to Claude
I give Claude tasks that require in-progress judgment, design discussions, and error investigation.
Design and implementation decisions: When the right approach is not obvious — “how should this feature be implemented?” or “does this structure cause any problems?” — working through it in a real-time conversation with Claude is useful. Because I get immediate responses like “for this reason, a different approach might be better,” I can accumulate decisions and keep moving.
Error investigation: When an error occurs, identifying the cause involves forming and testing multiple hypotheses. When a task requires dialogue — “what does this log mean?” or “what should I check next?” — Claude is a good fit.
Content review: When I want to verify article text or check whether prohibited expressions appear, I want to see the result immediately, so I run these checks with Claude Code.
What I Give to Codex
I give Codex batch tasks that follow a fixed pattern and can be repeated.
Bulk transformations following the same pattern: “Add the same frontmatter field to all files in this directory” is a task well suited to Codex. When the change pattern is clear and the AI does not need to ask for judgment, asynchronous processing is efficient.
Bulk test generation: When I hand Codex a list of target files with instructions to “add a unit test to each,” I can work on something else while Codex processes the list, which is where the advantage of asynchronous processing shows.
A Specific Handoff Example
At one point I had several English versions of blog articles to create.
I started with Claude Code. I reviewed the Japanese source content, discussed translation decisions, and built the first article together. At that stage, judgment was needed — “how do I translate this?” and “how do I convey this Japanese context in English?” — so the conversational format of Claude was appropriate.
Once the first article was done, the translation pattern was clear. The remaining articles just needed to follow the same pattern. I handed off to Codex and gave it instructions to create the remaining English versions in bulk.
The flow was: establish the approach with Claude, then hand the repetitive portion to Codex.
Decision Criteria
When deciding which AI to use, I check the following:
| Question | Points to Claude | Points to Codex |
|---|---|---|
| Does the task require judgment mid-way? | Yes | No |
| Do I want to verify results in real time? | Yes | No |
| Is the task a repeating pattern? | No | Yes |
| Can I use wait time for something else? | Not relevant | Useful |
There is no requirement that one AI handle everything. Handing off mid-task is also an option.
Summary
- Claude Code suits tasks that need real-time dialogue (design, investigation, judgment)
- Codex suits batch processing with a repeating pattern (bulk transformation, bulk generation)
- Starting with Claude to establish an approach and then handing off to Codex for repetitive work is a viable flow
- The key criteria: “does the task require judgment mid-way?” and “is real-time verification needed?”