How to Use Codex CLI vs Cloud Tasks

Codex CLI and Codex Cloud run the same GPT-5-Codex model, but they solve different problems. The CLI edits your local working copy and runs commands in your current directory. The cloud queues autonomous jobs in isolated containers and hands back GitHub pull requests.

Choosing wrong is expensive. Running a twenty-file migration in the CLI means babysitting approvals. Running a quick single-file fix in the cloud means waiting on a container spin-up and a PR review cycle.

Codex CLI vs Cloud: What Is the Actual Difference?

The CLI is interactive and local. The cloud is asynchronous and remote. That single split drives everything else, from how you grant access to how you review results.

• CLI: works on uncommitted changes, sees your local environment variables, no push required
• Cloud: needs a pushed branch, runs in a clean container, never touches your laptop
• CLI: approval prompts per command
• Cloud: one queued task, then a finished PR

When Should You Use the OpenAI Codex Instead of the Cloud?

Pick the CLI when the task depends on your machine. Debugging a failing local build, testing against a database only you can reach, or iterating on a prompt three times in a row — all of that is faster in the terminal.

Install it once with npm i -g @openai/codex, then run codex inside the repo. You see each proposed edit before it lands.

When Is Codex Cloud the Better Choice?

Use the cloud for well-specified work you do not want to watch. Dependency upgrades, adding tests to an untested module, or clearing three small bugs at once.

Each queued job gets its own container preloaded with your repo. You can run several in parallel, and results come back as pull requests you merge or discard.

How Do You Split Work Between the Two Surfaces?

• Long, risky, multi-file change with a clear definition of done: cloud
• Exploratory debugging where you learn as you go: CLI
• Anything needing local secrets or a private service: CLI
• Repetitive tasks you can describe once and forget: cloud
• Work you want reviewed by a teammate before merge: cloud

Does This Workflow Cost Extra?

No. Both surfaces draw on the same ChatGPT plan. Plus at $20 a month and Pro at $200 a month include it, and Team seats do too. The constraint is quota, not billing line items.

What Bugs People Most?

Cloud tasks fail silently when your plan quota runs out mid-run, and the CLI occasionally proposes a change that ignores an uncommitted edit you made minutes earlier. Commit or stash before you start, and keep an eye on quota.

Quick Summary

• Same model, two very different workflows.
• CLI is interactive and local; cloud is async and returns PRs.
• Use the cloud for parallel, well-specified jobs.
• Commit local work before queuing cloud tasks.
• Neither surface costs extra beyond your ChatGPT plan.