Claude Code vs Codex vs Devin: which job to give each one

People search "Claude Code vs Codex vs Devin" hoping for a winner. Most comparisons give you a feature table and a verdict. That is the wrong question. These tools overlap, they change quickly, and the useful question is: which job do I give each one? Below is what each tool says it does, plus how Tomi actually uses them.

A note on scope: we cite official docs for feature claims and leave out pricing, because plans change. Check each vendor's pricing page directly.

The short version

• Claude Code: a coding agent you run in the terminal and the cloud, with several built-in ways to run work in parallel.
• Codex: OpenAI's coding agent, with a terminal CLI and a cloud mode that runs tasks on OpenAI-managed computers.
• Devin: an autonomous software engineer that runs on its own cloud VMs, tests what it builds and records the result.

Claude Code: what its docs say

Claude Code's docs list five ways to work on several tasks at once: subagents, agent view, agent teams, dynamic workflows and projects. Agent teams are multiple coordinated sessions with a shared task list and messaging, managed by a lead. Subagents are specialized workers with their own context window and tool access. And worktrees give each session its own git checkout so edits do not collide.

What that suggests: Claude Code is built for orchestration. It fits the lead role in a setup with many workers.

Codex: what its docs say

The Codex CLI page says you can inspect, edit and run code from your terminal, with commands like /review to review changes and options to delegate focused work to subagents. It also lists a path to move work to Codex cloud. OpenAI's help center says Codex Cloud runs coding tasks on OpenAI-managed computers, with each task in its own isolated workspace, reachable from desktop, web or mobile.

What that suggests: Codex covers both a local terminal loop and a hosted, task-by-task mode.

Devin: what its docs say

Devin's intro calls it an autonomous AI software engineer that can write, run and test code, and lists tasks such as tickets, features, bug fixes, migrations and PR review. Its homepage says it spins up its own Windows, Mac and Linux virtual machines to build and test in a browser, and splits big tasks across parallel cloud agents. Its testing docs describe running the app after a pull request, interacting with it through the browser, and sending you a video recording.

What that suggests: Devin is strongest where a task has a clear check and a machine to run it on.

How we do it at SupaKid Studios

Tomi does not pick one. He gives each tool a job inside a larger structure, and he says the tools themselves are interchangeable once the workflow is right. Here is what he has described in his own videos.

• Engineering-manager agents dispatch the work. His engineering managers hand out tasks, and each one directs a crew of coding sub agents, each on its own cloud computer. This is the engineering-manager seat.
• Devin does QA and review. After sub agents open a pull request, Devin tests it on its own computer. Tomi sends it a packet of everything that should work, and it can run a shift of six to eight hours, recording what broke and then working on fixes. Devin's cloud agents also do independent code review.
• Claude Code runs the factory natively. Tomi says you can run a factory like his using a top-tier model as lead plus a set of Opus sub agents natively in Claude Code. That is the lead-with-subagents pattern from the docs above.
• Codex is one more agent you can connect. Tomi mentions Codex alongside Claude Code and Hermes as agents you can hook up to cloud computers. He has not described a separate Codex job in his published factory, so we will not invent one.

He also lists Factory and Claude Code's cloud agents among the tools that can fill these seats. The point he repeats: once the process is set, the tools are not what matters.

A simple way to choose

1. Start with the job. Dispatching, writing code, reviewing, testing or coordinating.
2. Match the check. If the task has an objective test, a cloud agent with its own machine suits it. See AI code review and testing agents.
3. Pick for parallel needs. If you want many workers, read how to run agents in parallel.
4. Try one task on two tools. Same ticket, same instructions. Compare the pull requests, not the marketing.
5. Keep the setup swappable. Write tasks and checks in plain language so you can change tools later.

What goes wrong

• Choosing by benchmark headline. Real repos are messier. Test on your own.
• One tool for every seat. The writer and the reviewer should differ.
• Ignoring structure. A good tool in a bad setup still needs babysitting. See how to structure an AI engineering team.
• Trusting stale comparisons. These products ship changes often. Check the docs linked above for the current state.

Want help picking and wiring them up?

Choosing tools is the easy part. Wiring them into one pipeline that ships is the work. That is what SupaKid Studios does as a managed AI agent team. If you want a second opinion on your stack, book a 30-minute call.