Claude Code vs Codex CLI: When to Use Which

I use Claude Code as my primary development tool. That bias is worth stating upfront because the strongest comparison writing comes from knowing one tool deeply and testing the other honestly. Over 36 blind duels and hundreds of sessions with both tools, I’ve found the answer to “which is better?” is genuinely “it depends on the task.”

TL;DR

Claude Code and Codex CLI solve the same problem — AI-assisted development — with fundamentally different architectures. Claude Code governs through hooks (95 lifecycle interceptors enforcing policy deterministically). Codex governs through sandboxing (OS-level kernel restrictions below the application layer). Neither approach is strictly superior. Claude Code dominates code review and security verification. Codex excels at procedural planning and offers genuine advantages in sandboxing, cross-tool portability via AGENTS.md, and cloud task delegation.

New to both? Start with the Claude Code guide or the Codex guide first. This post assumes familiarity with at least one.

Two Mental Models

Both tools are three-layer architectures, but the layers serve different purposes.

Claude Code:

1. Reasoning — Claude Opus processes your codebase and reasons about changes
2. Execution — Bash, file operations, git commands, MCP tool calls
3. Governance — Hooks intercept actions at 15 lifecycle points; permissions gate scope

Codex:

1. Model — GPT-5.3-Codex with 272K input / 128K output context
2. Sandbox — OS-level kernel enforcement (Seatbelt on macOS, Landlock+seccomp on Linux)
3. Approval — Policies (untrusted, on-request, on-failure) gate mutations before execution

The critical difference is where governance lives. Claude Code enforces safety at the application layer — hooks are programs you write that intercept specific events. Codex enforces safety at the kernel layer — the operating system prevents disallowed operations regardless of what the model attempts.

Configuration Philosophy

Claude Code uses JSON. Codex uses TOML. Both support hierarchical scoping. The philosophies differ in how they think about context-switching.

Claude Code: Hierarchical inheritance

// ~/.claude/settings.json (user-level)
{
  "permissions": {
    "allow": ["Bash(git *)"],
    "deny": ["Bash(rm -rf *)"]
  }
}

// .claude/settings.json (project-level, inherits user)
{
  "permissions": {
    "allow": ["Bash(npm test)"]
  }
}

Claude Code’s configuration cascades: user → project → skill frontmatter. Rules files in .claude/rules/ inject domain-specific instructions. The hierarchy is implicit — you discover the active configuration by reading multiple files.

Codex: Profiles with explicit switching

# ~/.codex/config.toml
model = "codex-5.3"
approval_policy = "on-request"

[profile.fast]
model = "codex-mini-latest"
approval_policy = "on-failure"

[profile.careful]
approval_policy = "untrusted"

codex --profile careful "Review this PR"
codex --profile fast "Fix this typo"

Codex profiles let you switch between configurations with a flag. No file inheritance to reason about — the active config is always explicit. For teams standardizing on approval policies, this is simpler to audit.

Safety Models

This is the deepest architectural divergence between the tools.

Claude Code: Deterministic hooks at the application layer

Hooks intercept actions before they execute. A PreToolUse hook on Bash can inspect every command and block dangerous patterns:

# Hook: git-safety-guardian (PreToolUse:Bash)
if echo "$tool_input" | grep -q "push.*--force.*main"; then
  echo '{"decision": "block", "reason": "Force push to main blocked"}'
fi

The strength: hooks are programs. You can encode arbitrarily complex safety logic — check file paths, validate JSON, enforce naming conventions, run linters. I have 95 hooks covering everything from credential detection to quality gates.

The weakness: hooks operate at the application layer. A sufficiently creative prompt or tool invocation could theoretically bypass them, because the enforcement mechanism and the agent share the same process boundary.

Codex: Kernel-level sandboxing

Codex restricts the agent at the OS level. On macOS, Seatbelt profiles limit filesystem access, network connectivity, and process spawning. On Linux, Landlock + seccomp do the same.

# Three sandbox modes
codex --sandbox read-only      # Agent can read but not write
codex --sandbox workspace-write # Agent writes only in project directory
codex --sandbox full-access     # No restrictions (danger mode)

The strength: kernel-level enforcement is below the application. The model cannot escape restrictions by crafting clever commands — the operating system denies the syscall before it executes. This is a fundamentally harder guarantee than application-level hooks.

The weakness: kernel restrictions are binary. You can allow or deny filesystem writes, but you can’t say “allow writes to src/ but block writes to config/ unless the change passes a linter.” That nuanced governance requires application-level logic.

The tradeoff is real. Hooks provide granular, programmable safety but weaker boundaries. Sandboxing provides stronger boundaries but coarser control. The right choice depends on your threat model.

Extensibility

Both tools support customization, but with different maturity levels across different mechanisms.

Where Claude Code leads: Hooks. The 15-event lifecycle system with PreToolUse, PostToolUse, UserPromptSubmit, SessionStart, and Stop events enables governance patterns that Codex’s two-event system cannot match. If you need to enforce quality gates, detect credential leaks before commits, or inject context automatically, Claude Code’s hook architecture is substantially more mature.

Where Codex leads: Cross-tool portability. AGENTS.md works in every major AI coding tool. If your team uses Cursor, Copilot, and Codex across different projects, one instruction file covers all of them. CLAUDE.md is powerful but locked to Claude Code. Cloud task delegation is also unique to Codex — offloading long-running work to OpenAI infrastructure and applying diffs locally is a workflow Claude Code doesn’t offer.

Where Each Tool Wins

Based on 36 blind duels and daily production use of both tools:

Claude Code wins

• Code review and security verification. Claude Code won 8 of 12 decided duels in review tasks. The quality philosophy system and evidence gates catch issues that slip through Codex’s more procedural approach.
• Governance-heavy workflows. If your workflow requires pre-commit checks, credential scanning, output validation, or quality gates, hooks are the mechanism. Codex can’t match this today.
• Complex multi-agent orchestration. Explicit subagent delegation via the Task tool, combined with deliberation systems, enables workflows where multiple specialized agents collaborate with isolated context.
• Deep codebase refactoring. Opus excels at holding architectural context across long sessions. The context engineering patterns that govern Claude Code’s hook/skill/rules hierarchy translate directly to how the model reasons about large codebases.

Codex wins

• Sandbox-critical environments. If you’re running an AI agent against untrusted code, processing external PRs, or operating in a CI/CD pipeline where you need hard guarantees about filesystem and network access, Codex’s kernel-level sandboxing is the right tool. Application-level hooks cannot provide the same guarantee.
• Cross-tool teams. If your team uses multiple AI coding tools, AGENTS.md gives you one instruction file that works everywhere. No duplicate maintenance across CLAUDE.md, .cursor/rules, and Copilot instructions.
• Cloud async workflows. codex cloud exec delegates tasks to cloud infrastructure and returns diffs. For CI/CD integration or batch processing, this is a workflow Claude Code doesn’t offer natively.
• Procedural planning. Codex’s plan mode with streaming output and steer mode (Tab-based steering while planning) won duels when the task required explicit dependency chains and step-by-step processes.
• Desktop experience. Codex’s desktop app with multi-tasking, parallel worktrees, and floating pop-out windows provides a richer visual experience than Claude Code’s CLI-first approach.

Running Both

The tools don’t conflict. CLAUDE.md and AGENTS.md coexist in the same repository. Here’s my setup:

my-project/
├── .claude/
│   └── settings.json     # Claude Code project config
├── CLAUDE.md              # Claude Code instructions
├── AGENTS.md              # Codex + Cursor + Copilot instructions
└── codex.md               # Codex project config (optional)

I use Claude Code for daily development and code review. I use Codex when I need sandbox guarantees (processing external contributions) or when I want to validate my Claude Code plans against a different model’s perspective — the blind judge approach.

Key Takeaways

If you’re choosing a tool:

• Start with your safety requirements. Need kernel-level sandboxing? Codex. Need programmable governance hooks? Claude Code.
• Consider your team. Multiple AI tools in use? AGENTS.md (Codex) avoids duplicate instruction maintenance.
• Try both on a real task before deciding. The blind judge methodology works for personal evaluation too.

If you’re already invested:

• Claude Code users: write an AGENTS.md anyway. It takes 20 minutes and makes your project accessible to Codex, Cursor, and Copilot users.
• Codex users: monitor the hooks system as it matures. When AfterAgent and AfterToolUse expand to PreToolUse events, the governance gap narrows significantly.
• Both tools are improving fast. The comparison in this post has a shelf life measured in months, not years.

FAQ

Can I use both tools in the same project?

Yes. CLAUDE.md and AGENTS.md are separate files with no conflicts. Each tool reads its own instruction file and ignores the other. I maintain both in my active projects.

Which tool is better for beginners?

Codex has a lower configuration barrier — three sandbox modes and three approval policies cover most use cases. Claude Code’s power comes from hooks and skills, which require investment to set up. Start with whichever model (Claude or GPT) you’re already comfortable with.

How do costs compare?

Both use token-based pricing through their respective APIs. Claude Code runs on Anthropic’s pricing; Codex runs on OpenAI’s credit system. Actual cost depends on usage patterns — long sessions with large codebases cost more regardless of tool. Neither is meaningfully cheaper for equivalent work.

Will AGENTS.md work with Claude Code?

Not currently. Claude Code reads CLAUDE.md; Codex reads AGENTS.md. The formats are similar enough that content translates easily between them, but there’s no automatic cross-reading.

Which has better IDE integration?

Codex has a desktop app with multi-tasking and floating windows. Claude Code integrates with VS Code and JetBrains via extensions, and offers a browser-based experience. Both work well; the choice depends on whether you prefer CLI-first (Claude Code) or GUI-first (Codex) workflows.

References

• Claude Code Comprehensive Guide — Full architecture, hooks, skills, MCP, and configuration reference
• Codex CLI Comprehensive Guide — Full architecture, AGENTS.md, sandbox, approval policies, and cloud tasks
• The Blind Judge: Claude vs Codex in 12 Tasks — Blind evaluation methodology and results
• Claude Code Hooks: Why Each of My 95 Hooks Exists — Deep dive into governance through lifecycle hooks
• Why My AI Agent Has a Quality Philosophy — Evidence gates and quality enforcement
• Context Engineering Is Architecture — Seven-layer context hierarchy for AI development