Codex vs Claude Code vs Gemini CLI

Comparison AI CLI

Codex CLI, Claude Code, and Gemini CLI can all read code, propose changes, write files, and run commands. They overlap, but they are not the same product. The right first CLI depends mostly on your ecosystem, how much automation you want, and how tightly you want to control permissions.

Short answer

Start with Codex if you already use OpenAI or want strong local review and sandboxing. Start with Claude Code if you want the broadest agent workflow across terminal, IDE, desktop, web, and teams. Start with Gemini CLI if you want Google sign-in, a generous free quota, Gemini/Vertex AI, or an open source CLI with many experimental workflows.

Quick Recommendation

Codex CLI

Best as a local coding agent with OpenAI login, AGENTS.md, /review, sandbox and approval profiles, Codex Cloud, and script mode through codex exec.

Claude Code

Best for longer development flows, conversation, memory, hooks, skills, subagents, CI/CD, Slack, desktop/web, and teams that want one agent workflow.

Gemini CLI

Best for the Google ecosystem, high free starting quota, Gemini API/Vertex AI, hierarchical GEMINI.md, checkpointing, and worktree-based experiments.

Shortest Honest Choice

Profile	Choose first	Avoid starting with
You just want to learn AI CLIs	Gemini CLI or Codex CLI	Three tools at once.
You want serious work in a Git repo	Codex CLI	Full access before you understand diff and approval flow.
You want long dialogue and project memory	Claude Code	Too many hooks and rules on day one.
You want Google Cloud or Vertex AI	Gemini CLI	API keys without budget and quota visibility.
You want a second opinion on critical diffs	Codex /review plus Claude/Gemini read-only review	Two agents editing the same files at once.

The point is not to choose one CLI forever. Start with one, learn its permission model, then use the others as reviewers or specialist tools.

Use Them Together

A strong practical model:

1. Let one CLI make the change in a worktree.
2. Run build/test.
3. Let another CLI read the diff without editing files.
4. Ask the review agent to find bugs, security issues, missing tests, and edge cases.
5. Fix manually or send the feedback back to the first CLI.

Example:

Read the diff as an independent reviewer.
Focus on concrete bugs, security, performance, and missing tests.
Do not make changes and suggest fixes only with file locations.

Which Should You Choose?

If you mainly want to…	First choice	Why
Use a strong local agent in a Git repo	Codex CLI	Good defaults, OS sandboxing, read-only/auto/full-access modes, and local /review.
Work through long tasks and ongoing dialogue	Claude Code	Memory, plan/permission modes, hooks, skills, subagents, and many surfaces.
Start free with a high daily quota	Gemini CLI	Google sign-in gives 1,000 requests per user per day on the individual tier according to the docs.
Automate through scripts or CI	Codex CLI or Gemini CLI	codex exec and Gemini headless mode have good non-interactive and JSON flows. Claude can also run claude -p, but watch token usage.
Use Google Cloud, Vertex AI, or Gemini API	Gemini CLI	Auth, quotas, and pay-as-you-go fit naturally with Google Cloud.
Use ChatGPT, Codex Cloud, or code review	Codex CLI	ChatGPT plans include Codex, and the CLI can work with local and cloud Codex tasks.
Use Slack, GitHub/GitLab CI, web/desktop, and team processes	Claude Code	Claude Code is designed as a broader agent system, not just a terminal command.
Compare outputs from several models	Use two CLIs	Let one write the change and another read the diff. They often catch different issues.

Core Differences

Topic	Codex CLI	Claude Code	Gemini CLI
Provider	OpenAI	Anthropic	Google
CLI command	codex	claude	gemini
Primary model direction	GPT-5.4 and Codex models	Claude models, especially Sonnet/Opus depending on plan	Gemini models through Google sign-in, API key, or Vertex AI
Open source CLI	Yes, Codex CLI is open source and Rust-based	No, product/CLI is distributed by Anthropic	Yes, Gemini CLI is open source
Typical install	npm or Homebrew	Native installer recommended, Homebrew/WinGet available	npm, Homebrew, or npx
Best Windows path	WSL recommended; native Windows is experimental	Windows supported through native installer/WinGet	Windows supported through Node/npm and sandbox options
Login	ChatGPT or OpenAI API key	Claude account, Anthropic Console, or third-party provider	Google sign-in, Gemini API key, or Vertex AI
Project instructions	AGENTS.md	CLAUDE.md, .claude/rules/, auto memory	GEMINI.md, global/workspace/JIT context files
Non-interactive mode	codex exec	claude -p	gemini -p or headless mode
MCP	Yes	Yes	Yes
Local review	Built-in /review for diff, branch, commit, and uncommitted changes	Code review through Claude Code workflows, plugins, and CI/CD integrations	No focused /review flow like Codex; can analyze a diff via prompt
Cloud/team surface	Codex Cloud, GitHub code review, and ChatGPT workspace features depending on plan	Terminal, IDE, desktop, web, Slack, CI/CD, scheduled tasks, and Agent SDK	Google AI/Code Assist/Vertex AI, extensions, subagents, and Google Cloud paths
Usage view	/status and Codex usage dashboard	/cost for API users, /stats for subscribers	/stats model

Safety and Permissions

Risk	Codex CLI	Claude Code	Gemini CLI
Default access	Codex recommends Auto in version-controlled folders and read-only outside Git. Network access is off by default for local commands.	Claude Code uses a permission system where sensitive actions require approval, and write access is limited to the starting folder and its subfolders unless you grant more.	Gemini asks for permission for actions such as file writes, cloning, and shell commands.
Sandbox	OS sandbox: macOS Seatbelt, Linux sandbox, and Windows/WSL paths.	Permission-based architecture and a sandboxed bash tool can provide isolation.	Several sandbox methods: macOS Seatbelt, Docker/Podman, Windows native sandbox, gVisor, and LXC/LXD.
Full access	--yolo or danger-full-access is high risk.	--dangerously-skip-permissions should be treated as a risk mode.	Tool sandboxing can be disabled, but should only be done in isolated environments.
Prompt injection	Web search defaults to cached search in Codex CLI, and live search requires explicit choice or a full-access situation.	Docs describe command blocklists, network approval, trust verification, and command injection detection.	Use sandboxing, trusted folders, .geminiignore, and approvals around external content.
Good starting mode	Read-only or Auto in a clean Git repo.	Planning or read-only-style start, approving only concrete actions.	Google sign-in, a read-only first task, and checkpointing if you want restore points.

None of these are magic safety layers

All three tools can do expensive or dangerous things if you give them broad access. Use Git, worktrees, spend limits, and diff review. Do not let an agent work freely in folders with tokens, private keys, or customer data.

Strengths and Weaknesses

Codex CLI

Strengths

• Strong local Git workflow: /review, diff reading, branch/commit review, and transcript.
• Clear sandbox and approval model with flags such as --sandbox, --ask-for-approval, --full-auto, and --yolo.
• Good for OpenAI/ChatGPT users, especially when Codex Cloud and code review matter.
• codex exec works well for scripts, automation, and CI-style use.
• AGENTS.md discovery works well in repositories and monorepos.

Weaknesses

• Windows is best through WSL, and native Windows should be treated as more experimental.
• Full access is easy to choose, but should only be used in an isolated runner or VM.
• API key usage does not include every cloud-based Codex feature.

Claude Code

Strengths

• Very broad product surface: terminal, VS Code, JetBrains, desktop, web, Slack, CI/CD, and scheduled tasks.
• Good for long conversations, planning, refactors, PR work, and teams.
• CLAUDE.md, .claude/rules/, auto memory, hooks, skills, and custom commands provide strong workflow control.
• Can consume piped input through claude -p and Unix-style flows.
• Native installer auto-updates on macOS/Linux/WSL.

Weaknesses

• More of a system than a simple CLI, so there are more concepts to understand.
• Memory and hooks can make context large if they are not maintained.
• Non-interactive -p is powerful, but the docs note that some trust checks do not apply the same way as in interactive use.

Gemini CLI

Strengths

• Easy Google start and a generous free quota for Google sign-in.
• Good for Google/Gemini/Vertex AI workflows and pay-as-you-go through Gemini API key.
• GEMINI.md has global, workspace, and just-in-time context, and the filename can be configured.
• Headless mode can return JSON/JSONL with usage statistics.
• Checkpointing, worktrees, subagents, extensions, and sandboxing provide many advanced options.

Weaknesses

• Several features are marked experimental or require manual enabling, including worktrees and checkpointing.
• Google AI Plus is not listed as a supported CLI paid tier in the Gemini CLI docs.
• Google Workspace accounts can require Google Cloud project configuration.

Instruction Files

Need	Codex CLI	Claude Code	Gemini CLI
Repository rules	AGENTS.md	CLAUDE.md	GEMINI.md
Global rules	~/.codex/AGENTS.md	~/.claude/CLAUDE.md	~/.gemini/GEMINI.md
Folder-specific rules	Nested AGENTS.md or AGENTS.override.md	.claude/rules/*.md with path scope	Just-in-time GEMINI.md close to files/folders
Good monorepo model	Short root file and app-specific instructions	Root CLAUDE.md plus per-area rules	Global/workspace/JIT context hierarchy

Read project folders and workflows before putting long instructions in a large repository.

Pricing Logic

Monthly plan price is not the only cost. AI CLI usage depends on:

• How large the repository is.
• How much context instruction files and MCP servers add.
• Whether the agent reads many files or runs several subagents.
• Whether you use subscription limits, credits, or API pay-as-you-go.
• Whether you use expensive reasoning models for routine tasks.

Short choice:

Situation	Best pricing model
Learning and manual use	Subscription or free tier.
Many small daily terminal tasks	Plan with included CLI usage.
CI, bots, and automation	API key with spend limits and separate keys.
Team and compliance	Business/Team/Enterprise with admin, SSO, and usage reporting.

See pricing, plans, and API prices for current numbers.

Practical Workflows

1. Beginner Flow

1. Start in a clean Git repository.
2. Run git status.
3. Ask the CLI to explain the project without changing files.
4. Let it propose test and build commands.
5. Only then approve one small change.

2. Two-Agent Review

1. Use Codex or Claude Code to make a small change.
2. Run build/test.
3. Use another CLI to read the diff.
4. Ask the review agent to focus on bugs, security, and missing tests.

Example:

Read this diff as a code reviewer. Find concrete bugs, regression risks, and missing tests.
Do not make changes.

3. Worktree Flow for Risky Changes

Use a separate worktree when the agent will change many files.

git worktree add ../my-project-ai -b ai/experiment
cd ../my-project-ai

Gemini has an experimental --worktree flow. Codex and Claude can also be used inside normal Git worktrees.

Recommended Starter Stack

Profile	Start with	Add later
Solo developer with ChatGPT	Codex CLI	Claude Code for long plans or Gemini CLI for a second opinion.
Solo developer with a Google account	Gemini CLI	Codex or Claude when you want stronger review or agent workflows.
Team already using Claude	Claude Code	Codex as a local reviewer on critical diffs.
Team using OpenAI/ChatGPT Business	Codex CLI	Claude Code or Gemini CLI for alternative reviews.
Google Cloud team	Gemini CLI	Codex or Claude for independent code review.

Sources

Last checked: April 11, 2026.

• OpenAI Codex CLI
• Codex CLI features
• Codex CLI reference
• Codex agent approvals and security
• Claude Code overview
• Claude Code CLI reference
• Claude Code security
• Claude Code memory
• Gemini CLI quickstart
• Gemini CLI quota and pricing
• Gemini CLI GEMINI.md
• Gemini CLI sandboxing
• Gemini CLI headless mode
• Gemini CLI worktrees
• Gemini CLI checkpointing