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Devin Cloud vs OpenAI Codex
A plain-English comparison to help you choose between them.
Devin sells a fully autonomous cloud engineer while OpenAI Codex sells supervised delegation, and the gap between those postures is the real decision. Pick Devin when tickets should become pull requests without you in the loop: it plans, writes and tests end to end in its own environment, with parallel sessions clearing well-specified backlogs while seniors review results. Pick OpenAI Codex when delegation should stay closer to the developer: tasks run in parallel cloud environments but the working pattern spans CLI, IDE extension and the ChatGPT app, with automated first-pass PR review, bundled into paid ChatGPT plans. Teams that formed the delegation habit on Codex and outgrew it are Devin's natural buyers.
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Side by side
- Summary
Devin is Cognition's autonomous software engineer: hand it a well-scoped task and it plans, writes, tests and opens the pull request, working in its own environment rather than your editor. Parallel sessions run several tasks at once, which is where the leverage compounds.
Its sweet spot is the well-defined backlog: bulk migrations, dependency upgrades, repetitive fixes and scoped features that senior engineers can specify precisely and review efficiently.
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It bills through flat plans whose usage allowances refresh on a schedule, with overage metered beyond them, and rewards supervision: unsupervised autonomy on vague tasks is where budgets and codebases both suffer.
- Best for
- Well-scoped tasks executed end to end to a PR
- Parallel sessions multiplying senior engineers
- Bulk migrations and dependency upgrades
- Clearing the well-specified backlog
- Teams with review capacity to absorb agent output
- Cost
- Paid only
- Ease
- Openness
- Hosted service
- Data
- Devin works inside your repositories and environments under the access you grant; scope credentials like you would a contractor's.
- Summary
OpenAI Codex is the agentic coding assistant included with paid ChatGPT plans rather than sold separately. It plans, writes and reviews code across a CLI, an IDE extension, the web and delegated cloud tasks, and since July 2026 its standalone desktop app lives on inside the ChatGPT desktop app.
Delegation is the distinctive move. Hand it a task and it works in an isolated cloud environment, several tasks in parallel, returning diffs and pull requests for review, and it reviews GitHub pull requests in turn. Successive GPT-5-class Codex models do the work.
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Usage is credit-metered following the April 2026 repricing, with local and cloud runs sharing five-hour rolling windows and weekly limits possible, so heavy delegation argues for a larger plan. The Free tier permits only quick exploration; real agentic use needs a paid plan.
- Best for
- Delegating coding tasks to parallel cloud environments
- Agentic coding bundled into an existing ChatGPT plan
- Automated first-pass review of GitHub pull requests
- One agent across CLI, IDE extension, web and desktop
- Technical founders shipping product without engineering headcount
- Cost
- Paid only
- Ease
- Openness
- Hosted service
- Data
- Codex is closed and cloud-run. Prompts, code and repository context are processed on OpenAI's services, so check the training and retention terms of your plan tier and the workspace-level data controls it carries before connecting private repositories. Treat agent output as untrusted until reviewed: agent-written code and auto-approved pull-request reviews need a human pass before production, especially around payments, auth and regulated data flows.
Pricing
- Devin Cloud
$20·$200·from $80·Custom
Prices as of August 2026.
- Pro
- $20per monthusage billed at API rates
- Max
- $200per monthusage billed at API rates
- Teams
- from $80per monthusage billed at API rates
- Enterprise
- Price on applicationno list price published
- OpenAI Codex
Free·$8·$20·from $100$20/user·Custom
Prices as of August 2026.
- Free
- Free
- Go
- $8per month
- Plus
- $20per month
- Pro
- from $100per month
- Business
- $20per user, per month2+ users minimum; billed annually; $25 per user per month if billed monthly
- Enterprise & Edu
- Price on applicationno list price published
By area
Where each one pulls ahead, area by area.
| Area | Devin Cloud | OpenAI Codex |
|---|---|---|
| By job | ||
| Software development | Devin Cloud bills by compute unit, which prices a vague brief as an expensive lesson and makes precision the thing that pays | OpenAI Codex suits the developer shifting from typing code to directing it, clearing a queue of well-scoped changes while staying on the hard parts themselves |
| By task | ||
| Coding & software development | Devin Cloud — when you have a backlog of well-defined tasks and would rather review PRs than write them | OpenAI Codex stakes out the delegated middle of a category split between editor assistants and autonomous engineers |
Common questions
Both return pull requests, so what really differs?
The level of ceremony and commitment. Devin is bought as autonomy: an included usage allowance with metered overage, precise scoping, review capacity to absorb its output, and the documented anti-pattern of unsupervised use on vague tasks. Codex is delegation folded into a subscription many teams already hold, spanning interactive and delegated work in one agent. Devin asks you to run a programme; Codex asks you to form a habit.
What does delegation actually cost on each?
Devin is a paid product billed by usage, so cost tracks delegated volume and vague briefs become expensive lessons; the spend is visible and deliberate. Codex adds no new subscription for ChatGPT-paying teams, but draws on the plan's shared usage ceilings, which OpenAI adjusted as recently as July 2026, so heavy use argues for larger plans. Pilot either against a real backlog before believing any projection.
Which should a team try first?
Usually Codex, for the mundane reason that trying it costs nothing extra on an existing ChatGPT plan and the delegation habit transfers. Graduate to Devin when the well-specified backlog is genuinely large, parallel sessions would compound, and senior review capacity exists to drain what autonomy produces. Teams standardised on another lab's stack reverse the order or look elsewhere entirely.
Related comparisons
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Tool facts last checked August 2026