Claude Code vs Codex vs Gemini CLI: Which AI Coding Agent to Use?

Claude Code, Codex, and Gemini CLI are all AI coding agents that work in your terminal, read your codebase, edit files, and run commands. The honest answer to “which one is best” is that each has different strengths, and many developers get the best results by using more than one. The right pick mostly depends on which AI account you already have, how you like to work, and the kind of tasks you give it.

This guide compares them at a practical level, without benchmark tables or hype.

The short version

• Claude Code is Anthropic’s agentic coding tool. It is known for careful, thorough work across large codebases and a strong focus on following instructions.
• Codex is OpenAI’s coding agent. It fits naturally if you already use ChatGPT, and it can work both locally and on delegated tasks.
• Gemini CLI is Google’s open-source command-line agent using Gemini models. It appeals to people who want an open codebase and who already live in Google’s ecosystem.

All three can do the core job well: understand a project, make multi-file changes, and run your tests. The differences show up in the details.

What they have in common

Before the differences, it helps to see how similar these tools are. Each one is an agent, meaning it works in a loop: plan, act, observe the result, adjust. If that concept is new, start with the beginner’s guide to AI coding agents.

All three can:

• Read and search files in your project.
• Edit code across multiple files.
• Run shell commands such as tests, linters, and builds.
• Ask for permission before risky actions, with options to allow more autonomy.
• Read project instruction files so you don’t repeat your conventions every session.
• Connect to external tools through MCP, the open standard for plugging tools and data into AI apps.

So the choice is less about capability on paper and more about fit.

Side-by-side comparison

	Claude Code	Codex	Gemini CLI
Made by	Anthropic	OpenAI	Google
Models	Claude	OpenAI models	Gemini
Sign in with	Claude account or API key	ChatGPT account or API key	Google account or API key
Open source	No	The CLI is open source	Yes
Where it runs	Your terminal, on your machine	Your terminal, plus OpenAI’s cloud options	Your terminal, on your machine
Feels best for	Deep, multi-step changes in an existing codebase	Developers already using ChatGPT; handing off tasks	Open-source fans and Google-centric teams

Treat the last row as a general tendency, not a rule. Every one of these tools improves often, and your own project is the real test.

Claude Code

Claude Code works directly in your terminal on your local codebase. You sign in with a Claude account or use an API key. For a full introduction, see what is Claude Code.

Where it tends to shine

• Understanding large, unfamiliar projects before changing them.
• Multi-step tasks where it needs to plan, implement, test, and fix.
• Following detailed project rules and conventions.
• Explaining its reasoning and summarizing what it changed.

What to watch

• It can be thorough to a fault on small tasks; a tight brief keeps it focused.
• Usage depends on your Claude plan or API budget, so long sessions are worth keeping scoped.

Codex

Codex is OpenAI’s coding agent. You can use it with a ChatGPT account or an API key, which makes it an easy step for anyone already paying for ChatGPT.

Where it tends to shine

• Quick iterations in the terminal on well-defined tasks.
• Workflows where you hand off a task and check back later.
• Teams already standardized on OpenAI tools.

What to watch

• Like any agent, it benefits from clear acceptance criteria: tell it which tests must pass.
• Settings for how much it can do without asking vary, so review its permission options before giving it more freedom.

Gemini CLI

Gemini CLI is Google’s open-source command-line AI agent, powered by Gemini models. Because the code is open, you can read how it works, and the community can contribute.

Where it tends to shine

• Developers who prefer open-source tooling they can inspect.
• Projects where a large amount of context needs to be read at once.
• People already using a Google account for their AI work.

What to watch

• As with the others, output quality depends heavily on how clearly you describe the task.
• Its defaults and feature set differ from the other two, so expect a short adjustment period if you switch.

How to choose

If you want one tool and a quick decision, these questions settle it for most people:

1. Which AI account do you already pay for? Starting with the agent that matches your existing account costs nothing extra to try.
2. Do you care about open source? If yes, Gemini CLI is open source, and Codex’s CLI is too.
3. What kind of work dominates? Large refactors in a mature codebase, quick scripts, or exploratory prototypes all reward slightly different styles.
4. How do you like to supervise? Some people want to approve every step; others want to delegate and review at the end.

Then do the most useful thing of all: give the same small, real task to two of them and compare the diffs. One afternoon of that tells you more than any comparison article.

Why mixing them often beats picking one

Here’s the less common advice: you don’t have to choose.

Different models have different blind spots. One agent might write a feature while another reviews it, and a reviewer built on a different model is less likely to share the same mistakes. Teams also mix tools for practical reasons:

• Spreading usage across accounts so no single plan hits its limits.
• Matching tool to task, for example one agent for heavy refactors and another for quick fixes.
• Cross-checking, where a second agent reviews and runs tests on the first agent’s work.
• Adding local models through Ollama for simple or private tasks that don’t need a cloud model.

The catch is coordination. Running three agents in three terminals means you become the project manager, passing context back and forth by hand. That is where a multi-agent setup helps.

BotQuarters is one way to do this on a Mac. A team lead agent splits your goal into tasks and assigns them to teammates, and each teammate can use Claude Code, Codex, Gemini, OpenCode, or a local model through Ollama, all in the same team. You bring your own AI accounts, and work only gets merged after the project’s tests and checks pass.

A simple mixed setup to try

Even without any extra tooling, you can try a mixed workflow:

1. Ask one agent to implement a small feature on a new branch.
2. Ask a different agent to review the diff and list problems.
3. Feed the review back to the first agent to fix.
4. Run the tests yourself, then merge.

You’ll quickly see where each one is strong, and you’ll catch more bugs than with a single agent working alone.

FAQ

Is Claude Code better than Codex?

Neither is better across the board. Claude Code is often praised for careful multi-step work in large codebases, while Codex fits naturally for ChatGPT users and delegated tasks. The best way to decide is to give both the same real task in your own project and compare the results.

Is Gemini CLI free to use?

Gemini CLI itself is open source, so the tool costs nothing. Using it requires access to Gemini models through a Google account or API key, and what you get depends on Google’s current plans and limits, so check their site for details.

Can I use Claude Code and Codex on the same project?

Yes. They both work on ordinary files in your project folder, so you can use one for implementation and the other for review. Use version control and separate branches so their changes don’t collide.

Which AI coding agent is best for beginners?

Start with the one that matches an AI account you already have, since that removes a setup step. All three work in the terminal and accept plain-language instructions, so the bigger factor for beginners is learning to write clear, small tasks.