OpenAI Codex vs Devin: Cloud Coding Agents Compared

Two cloud agents, different bets

Codex and Devin both run your code in the cloud. That is where the similarities end. Codex bets on speed and affordability: spin up a sandbox, execute a task, return the result, tear down the container. Devin bets on autonomy and scope: give it a full VM, let it work for hours, and come back to a finished feature.

The pricing reflects the bet. Codex rides on your existing ChatGPT subscription at $20/month. Devin starts at $500/month for teams. The 25x price difference reflects a different execution model: ephemeral sandboxes measured in tokens versus persistent VMs measured in compute-minutes.

Execution: sandbox vs full VM

Codex runs each task in an isolated container with kernel-level sandboxing. Your codebase is uploaded, the agent executes its plan, and the container is destroyed. Nothing persists. This makes Codex fast (most tasks complete in under 5 minutes) and safe (a compromised task cannot affect your system). The limitation is scope: Codex cannot install system packages, run long test suites, or maintain state between tasks.

Devin gives each session a full virtual machine. The agent can install Node, Python, Docker, databases, or anything else it needs. It runs tests, reads error output, modifies code, and retries. Sessions can last hours. The VM persists until the task is complete or you end it manually.

This means Devin can handle tasks Codex cannot: setting up a new project from scratch, implementing a feature that requires running integration tests against a real database, or modifying code across multiple repositories that need different dependency versions. The tradeoff is speed. Devin is slower to start (VM provisioning) and slower to iterate (full test cycles instead of quick sandbox runs).

Feature comparison

Pricing: tokens vs ACUs

Codex pricing is token-based. You pay for the text the model processes: $1.50 per million input tokens and $6 per million output tokens with codex-mini-latest. A typical coding task (read 500 lines of code, write 50 lines of output) costs a few cents. The $20/month ChatGPT Plus subscription includes a generous allocation for interactive use.

Devin pricing is ACU-based. Agent Compute Units represent minutes of VM time plus model inference. The $500/month Team plan includes 250 ACUs. A simple task might use 5 ACUs (5 minutes). A complex multi-hour feature could use 60-120 ACUs. Once you exhaust your allocation, you buy more at per-ACU rates.

For a developer running 30 small coding tasks per day, Codex costs roughly $20/month (subscription) or $5-15/month (API). The same volume on Devin would burn through ACUs in days. For a team assigning 5 large autonomous tasks per week, Devin's 250 ACUs may cover the month while Codex's token costs for equivalent complexity would be comparable.

The pricing models reward different behaviors. Codex rewards short, focused tasks. Devin rewards delegating entire features.

Enterprise: different case studies

Codex has the larger adoption footprint. Over a million developers use it. Cisco cut code review times by 50%. Duolingo increased PR volume by 70%. The enterprise pitch is: your developers already have ChatGPT, now they have a coding agent too. Minimal incremental cost, immediate productivity gain.

Devin's enterprise story is different. Goldman Sachs is a reported customer. The pitch is not "make developers faster" but "let an agent handle tickets that would take a junior developer a full day." Cognition positions Devin as a teammate, not a tool. The $500/month price point reflects this: you are paying for an autonomous worker, not a code completion engine.

The distinction matters for procurement. Codex fits into existing ChatGPT Enterprise contracts. Devin is a separate vendor, separate contract, separate security review. Teams that already pay for ChatGPT can add Codex for free. Teams evaluating Devin need a new budget line.

What each cannot do

Codex limitations: 192K token context window (smaller than Claude Code's 1M). No model choice beyond OpenAI. No persistent environment between tasks. Cannot install system-level dependencies. Cannot run multi-repo workflows in a single session. Limited browser access.

Devin limitations: No free tier. No open-source components. Proprietary models only, no option to bring Claude or GPT. ACU pricing can surprise teams that underestimate task complexity. The agent sometimes over-engineers simple tasks because it has the VM capacity to do so. Slower startup than Codex.

Neither tool supports local models. Neither gives you model flexibility. If model choice matters, look at Claude Code (Claude models) or OpenClaw (any model).

Which to choose

Choose Codex if you want a fast, cheap AI coding assistant. Individual developers, small teams, well-scoped tasks. The ChatGPT Plus subscription you may already have includes it. Best for: writing tests, refactoring, scripting, DevOps automation, PR reviews.

Choose Devin if you want an autonomous agent that can own full features. Engineering teams with budget for $500+/month who want to assign entire Jira tickets to an agent. Best for: new feature implementation, environment setup, multi-repo changes, tasks that require iteration and self-correction.

Use both if your team has the budget. Codex for the daily grind of small coding tasks. Devin for the weekly handful of larger features that benefit from autonomous, multi-hour execution. This is the pattern emerging at well-funded engineering teams.

Is Devin worth $500/month compared to Codex at $20/month?

It depends on the tasks. If you need an agent that can own multi-hour features end-to-end, set up environments, and self-correct across multiple repos, Devin's full VM model and autonomous planning justify the price. If you need a fast assistant for well-scoped coding tasks, Codex at $20/month is 25x cheaper and fast enough. Most individual developers find Codex sufficient. Teams that assign Devin entire tickets report recovering the cost in reduced engineering hours.

How does ACU pricing compare to token pricing?

ACUs (Agent Compute Units) charge per compute-minute of VM time. Token pricing charges per input and output text processed. For short tasks (under 5 minutes), Codex's token pricing is much cheaper. For long tasks (30+ minutes of autonomous work), Devin's ACU model can be more predictable because you pay for wall-clock time rather than the volume of text the model processes. Devin's Team plan includes 250 ACUs, roughly 250 minutes of agent compute.

Can Codex do everything Devin does?

No. Codex runs in an ephemeral sandbox that resets between tasks. Devin runs in a persistent VM that can install dependencies, configure services, run test suites, and iterate over multiple attempts. Codex is better for focused coding tasks. Devin is better for end-to-end feature delivery where the agent needs to set up its own environment and debug its own failures.

Which has better code quality?

Codex uses codex-mini-latest, which is optimized for speed and cost efficiency. Devin uses Cognition's fine-tuned models, which are tuned for multi-step autonomous planning. In practice, code quality reviews are mixed. Codex produces clean, functional code for well-scoped tasks. Devin produces more complete solutions for complex tasks but can over-engineer simple ones. Neither consistently beats Claude Code on raw code quality benchmarks.