Claude Code vs Codex vs Gemini CLI — When to Use Each One
Three major AI coding CLIs, three very different strengths. A practical guide to which one to reach for, by task type — not by marketing.
If you use AI agents seriously, you probably already have two or three installed. Claude Code, Codex, Gemini CLI — each lab puts out a CLI, each one has genuine strengths, and choosing one per task is usually smarter than picking a single winner forever.
This post is the practical routing guide. No marketing, no benchmark theater, just "which one should you reach for" by work type.
The three, briefly
Claude Code (Anthropic). The most disciplined conversational agent. Strong at reasoning through multi-file changes, honest about uncertainty, excellent at following complex instructions without going off-script. Pricing via Claude subscription or API.
Codex CLI (OpenAI). Open source, provider-agnostic in spirit but tuned for OpenAI models. Strong at fast iteration, comfortable with loose specs, and quick to ship. Works well when you want the agent to just do the thing without extensive coordination.
Gemini CLI (Google). Also open source, generous free tier, very strong at large-context tasks given Gemini's long context window. Good for working across big codebases where you want the agent to see everything at once.
The rest of this post is about when each one shines.
Greenfield features
For starting a new feature from scratch with a real spec:
First choice: Claude Code. It is the most disciplined about reading the spec, clarifying ambiguity, and producing code that matches the instructions rather than what it would have written otherwise. If your spec is 500 words and you want all 500 words respected, Claude Code is the reliable pick.
When Codex wins: If the spec is looser ("build me a simple note-taking CLI, surprise me") Codex is faster and its output often has more character. Claude Code in the same situation tends to produce something correct but flavorless.
Gemini for: Greenfield features where you need to reference a lot of existing context. Gemini's long context makes it comfortable reading your whole codebase plus the spec.
Bug hunts
For debugging something weird:
First choice: Claude Code. The thing you want in a debugger is not speed, it is hypothesis discipline. Claude Code reliably holds a hypothesis, tests it, reports the result, and then refines. Codex has a faster tendency to "just try something else" which is sometimes right and sometimes pure chaos.
When Codex wins: When you already know roughly what is wrong and you want someone to just execute the fix. Codex ships faster when the diagnostic work is already done.
Gemini for: Bugs that span many files, especially when a subtle coupling between distant parts of the codebase is involved. Long context helps.
Refactors
For reshaping existing code:
First choice: Gemini CLI. Context length matters most here. Refactors that span five files benefit enormously from the agent seeing all of them simultaneously. Gemini does this more comfortably than the others.
When Claude Code wins: Refactors that require nuanced reasoning about invariants, or where you need the agent to be deeply cautious about breaking behavior. Claude's discipline wins.
When Codex wins: Small, well-scoped refactors where speed matters more than coordination.
Code review
For reviewing a diff:
First choice: Claude Code. The honest-about-uncertainty character shines here. Claude will say "this might be wrong, but I'm not sure" rather than confidently pointing at nothing or confidently approving everything.
Codex tendency: Reviews can be more action-oriented — "do this instead" rather than "consider that this assumption may not hold." Useful for different phases of review.
Gemini for: Reviewing large PRs. Context length wins again.
Documentation
For writing or rewriting docs:
First choice: Claude Code. Prose quality is visibly better, especially for technical explanation. Claude writes like someone who has read a lot of technical writing.
Codex tendency: More utilitarian. Gets the job done, reads a little flatter.
Gemini: Good for docs that need to ingest a lot of existing codebase context. Less opinionated on prose style.
The parallel-agent workflow
A pattern that is emerging: run Claude Code for the core work, and use Codex or Gemini as background agents for parallel tasks.
Example: you are working on a feature with Claude Code. While it is thinking, you ask Codex to draft the test file. Gemini gets the documentation pass in parallel. You sync them at the end. This is doable today with any shell that supports multiple tabs. If you want it more seamless, tools like Conductor (native Mac) are purpose-built for orchestrating it.
Alternatively, run all three agents inside MOLTamp in different tabs — one skin per agent for visual routing. Not orchestrated, but fast to context-switch.
A note on lock-in
Every one of these agents is a CLI. You can switch between them task-by-task. The cost of trying a different agent for a task is measured in seconds, not days.
The lock-in danger is the terminal wrapper you use around them. If your terminal is tied to one specific agent's UX (Warp, for instance, is best with Warp's agent), switching is more expensive. If your shell is agent-agnostic (MOLTamp, Ghostty, iTerm2 — any of them), you pay no switching cost at all.
In a space moving this fast, staying agent-agnostic is a hedge against whichever lab has a rough quarter next.
Starter routing policy
If you want a dead-simple starting rule:
- Greenfield feature: Claude Code
- Bug hunt: Claude Code, unless the bug is codebase-wide → Gemini
- Refactor: Gemini
- Quick scripts / exploration: Codex
- Code review: Claude Code
- Docs: Claude Code
Use this for a month, pay attention to when you are reaching for a different agent intuitively, and refine. After a while the routing becomes unconscious. That is the actual win of using multiple agents — your judgment about which tool fits which task gets sharper over time.