What does OpenAI Codex do, and how is it different from ChatGPT?

Codex is not simply a separate product sitting opposite ChatGPT. The Codex experience is available in the ChatGPT desktop app and also through the CLI, IDE extension, and cloud. I distinguish the working modes rather than the account or brand: general ChatGPT chat for understanding, comparison, or drafting; a Codex coding-agent workflow for inspecting a repository, changing files, running commands, and testing the result.

This is not a benchmark. I did not measure the CLI, IDE, and cloud surfaces side by side. It is a practical reading of the supplied official OpenAI documentation, not personal experience with untested features.

What is Codex?

Calling Codex “ChatGPT that writes code” misses the useful part. It is a coding agent that can work against a codebase and its development tools. OpenAI says the CLI can inspect local repository files, edit them, and run installed tools, either interactively or through `codex exec` in repeatable workflows.

In chat, I can request a function but still have to place and test it. In an agent workflow, Codex can gather context, apply a change, and attempt verification with allowed tools. That does not guarantee correctness; it reduces the manual gap between conversation and workspace. The useful loop is exploration, plan, edit, command, diff, and follow-up. For a broader choice, see my Codex CLI, Claude Code, and Gemini CLI task guide.

How is it different from ChatGPT?

This is not a comparison between a ChatGPT account and a separate Codex account. It is a comparison between general ChatGPT chat and the Codex coding-agent experience in the same ecosystem. I might use general chat to compare architectures, explain an error message, challenge a SQL approach, or draft a user-facing explanation. I would give the Codex workflow a repository task such as: “Find the cause of this bug, show the plan first, add these tests, and change only the relevant files.”

The difference is not that one can reason and the other cannot. Both experiences can support question-and-answer work. The practical difference is context, authority, and the expected artifact. When Codex is connected to a local workspace or a configured cloud environment, it can act on real files and commands. That access can remove busywork, but it also increases the blast radius of a mistake. A bad suggestion in general chat often remains text; a bad coding-agent decision can become a file edit or a command.

So I ask “Should this task end in a conversation or in a controlled change?” before I ask which product is smarter. If model tier and task depth are separate decisions, my GPT-5.6 Sol, Terra, and Luna guide covers that choice.

Desktop, CLI, IDE, and cloud: different working rhythms

Codex is not one window. The ChatGPT desktop app lets the user choose ChatGPT or Codex, and Codex is also available through the CLI, IDE extension, and cloud. This guide focuses on CLI, IDE, and cloud because they show the repository workflow most clearly.

The CLI fits terminal work: inspect files, edit, run local tests or lint, and review the diff in one loop. It also supports non-interactive scripts and CI. For a bounded repository task where commands must stay visible, it is my first candidate.

The IDE extension brings open files and selected code into the prompt. OpenAI lists VS Code and compatible editors, Xcode, and JetBrains integrations. Keeping source, diff, and explanation together can help with a small fix, but it changes the location of review rather than removing review.

Codex cloud delegates tasks to isolated cloud environments. OpenAI says tasks can run in parallel and each repository environment can define dependencies, tools, variables, and setup. The user later reviews a summary and diff or requests a follow-up. This suits longer work or multiple attempts that should not occupy the local machine, provided the environment is reproducible and narrowly scoped.

Which one should I use for which task?

Here is the short selection summary:

• ChatGPT: Concept explanations, brainstorming, requirements, option comparisons, and drafts that do not need to touch the repository yet.
• Codex CLI: Local repository exploration, focused file edits, tests or lint, diff review, and repeatable terminal workflows.
• Codex IDE: Explanations or small edits around an open file or selected code, with review beside the source.
• Codex cloud: Longer tasks that can run in parallel or in the background inside a deliberately prepared environment.
• ChatGPT first, Codex second: Ambiguous product work that needs conversational clarification before its acceptance criteria become a coding task.
• Neither is sufficient alone: Migrations, production deployment, secret use, or irreversible external actions; these need a separate human approval and system control.

An example task workflow

Suppose checkout occasionally sends the same payment request twice. I would first clarify the retry condition, idempotency behavior, user impact, and acceptance criterion, then create a clean branch and rollback point.

My first Codex instruction would be read-only: trace the flow and tests, name likely files, and rank two plausible causes with evidence. Once the plan is concrete, I would allow workspace edits: change only those files, demonstrate the failure in a test, apply the fix, then run focused tests and existing lint. Finally I would read `git diff`, confirm the commands ran, and try the critical behavior manually. For cloud work too, the summary is navigation; the diff and reproducible checks are evidence.

Permission, reasoning, and verification safety

Codex CLI lets the user choose models, reasoning effort, permissions, and command boundaries for the task. I do not treat these as one “power” setting. Higher reasoning does not make unrestricted network access or writes outside the workspace safe. I cover the separation in my coding-agent permission-mode guide and task guide to low versus high reasoning.

My practical default looks like this:

• In an unfamiliar repository, read and plan before granting file-write access.
• For a bounded change, allow workspace writes and only the local commands it needs.
• Treat network access, package installation, migrations, push, deployment, and secrets as separate decisions.
• Create Git checkpoints before and after a task; OpenAI’s CLI and IDE quickstarts recommend checkpoints so changes can be reverted.
• Connect only the repository, tools, and variables required by a cloud environment, then inspect the summary and diff before merge.
• If credentials are stored in a file, treat it as a password. OpenAI says `~/.codex/auth.json` can contain access tokens and must not be committed, pasted into tickets, or shared in chat.

Verification is not paperwork added after generation. It belongs in the acceptance criteria. A diff, focused tests, broader tests, type/lint/build checks, and a manual check of critical behavior cover different failure modes. My seven-step coding-agent output verification checklist turns that into a repeatable workflow.

Authentication changes the administrative boundary. OpenAI documents ChatGPT or API-key sign-in for local work, while cloud requires ChatGPT sign-in. The method determines whether workspace or API-organization controls and data-handling policies apply, so teams should match it to their deployment.

Bottom line

Codex is not best understood as a separate account replacing general ChatGPT chat. It is the coding-agent experience available in the ChatGPT ecosystem and through CLI, IDE, and cloud surfaces, carrying work through files and tools. General chat can clarify the problem and acceptance criteria; Codex CLI or the IDE can implement a focused local change; cloud can take longer work into a prepared environment. Those can be stages of one workflow rather than competing products in every task.

I choose by the surface the task must touch. If the result is text, chat may be enough. If the result should be a tested diff, Codex is the better fit. If the result changes production, I do not let any agent have the last word by itself.