Head-to-head

Jules vs Devin

Both hand off coding work, but one is a GitHub-first background worker and the other is a broader engineering capacity platform.

Last updated April 2026 · Pricing and features verified against official documentation

Jules and Devin both try to turn software chores into something you can assign, supervise, and review later. That makes the comparison useful for teams that have already accepted delegated coding and are now deciding whether they want a narrower GitHub worker or a broader managed engineering system.

Jules is the more focused product. Google has built it around asynchronous tasks in GitHub-connected repositories, with a plan-first flow and cloud execution that pushes it toward bounded fixes, test work, and cleanup jobs.

Devin is the more expansive product. Cognition has wrapped it around autonomous sessions, Devin IDE, review tooling, knowledge features, and team-level controls, so it behaves more like machine capacity than a single task runner.

The choice is simple: pick Jules if your coding work looks like a queue of small repo tasks, and pick Devin if you want the agent to sit inside a wider engineering operation.

The Core Difference

Jules is optimized for delegation inside GitHub. It is narrow, visible, and easiest to trust when the task has clear boundaries and ends in a reviewable diff.

Devin is optimized for capacity. It is stronger when the organization wants parallel work, richer review surfaces, and a product that can stretch beyond one task loop into a managed workflow.

That difference shapes the buying decision. Jules is the cleaner worker; Devin is the larger system.

GitHub Task Loop

Jules wins. Its whole shape is built around a cloud VM that clones the repo, installs dependencies, proposes a plan, and then makes changes asynchronously. That makes it a better fit for bug fixes, version bumps, test additions, and other repo chores that are easy to scope but annoying to do by hand.

Devin can handle the same class of work, but it is less singular about it. Because it also spans IDE, review, knowledge, and scheduled workflows, the GitHub task loop is one part of a broader operating model rather than the product’s defining promise. If the buyer wants the cleanest “take this issue and hand back a PR” workflow, Jules is the tighter fit.

Surface Area And Workflow

Devin wins. The product now includes Devin IDE, Ask Devin, Devin Review, DeepWiki, scheduled sessions, an API, and integrations with Slack, Teams, GitHub, Jira, Linear, and custom git providers. That gives it more ways to fit into a real engineering stack, especially when the team wants the agent to do more than one kind of job.

Jules is intentionally narrower. It has a web app, GitHub issue triggers, a REST API, and the Jules Tools CLI, but it still feels designed around a single rhythm: assign task, wait, review result. That discipline is a strength when the job is repetitive, but it becomes a limit when the team wants broader automation or deeper coordination.

Pricing

Jules wins on entry simplicity, while Devin wins on scaling logic. Jules starts with a free tier that is actually usable for sampling the workflow, then moves to Google AI Pro at $19.99 per month and Google AI Ultra at $249.99 per month. The packaging is easy to understand, but it is also clearly tied to Google’s consumer AI plans, which makes it feel less like a dedicated developer SKU.

Devin starts at $20 with pay-as-you-go ACUs, then moves to a $500 per month Team plan that includes 250 ACUs and unlimited concurrent sessions. That structure is more complicated, but it also maps better to real team usage because the buyer can see the difference between sampling the product and buying operating capacity.

The practical takeaway is that Jules is easier to try, while Devin is easier to justify once the product starts acting like infrastructure.

Privacy

Jules has the simpler repo-specific promise: Google says it does not train on private repository content, and you have to connect GitHub and accept the privacy notice before the agent can run. That is a straightforward statement for a GitHub-native product.

Devin has the stronger business posture. Cognition says customer data is not used for training by default unless you opt in, and enterprise customers are excluded from training under their agreements. Devin also offers dedicated or on-prem tenant isolation on enterprise deployments, which matters more once the agent has to touch repos, tickets, and chat systems at scale.

Who Should Pick Jules

• The solo developer with a steady stream of small GitHub chores should pick Jules because the plan-first workflow turns narrow repo tasks into clean handoffs.
• The engineer who already thinks in issues, PRs, and reviewable diffs should pick Jules because it stays close to the GitHub task loop instead of adding a larger operating layer.
• The Google AI Pro subscriber who wants higher task limits without changing tools should pick Jules because the product is packaged to be a low-friction add-on to that ecosystem.

Who Should Pick Devin

• The engineering manager buying backlog reduction should pick Devin because it behaves like additional capacity, not just a coding helper.
• The platform or infrastructure team with disciplined review habits should pick Devin because its review, knowledge, and parallel-session features match a more process-heavy workflow.
• The organization that wants the agent to spread across Slack, Jira, Linear, GitHub, and internal knowledge should pick Devin because its surface area is built for that kind of coordination.

Bottom Line

Jules and Devin both delegate coding, but they do it at different scales. Jules is the narrower, cleaner worker: GitHub-first, review-first, and best when the job is a bounded repo task with a visible end state. Devin is the broader platform: more surfaces, more team controls, and a clearer fit for organizations that want machine capacity instead of a single async worker.

If your work is mostly a queue of small GitHub chores, pick Jules. If your work is mostly backlog reduction, parallel execution, and engineering workflow management, pick Devin. The right answer is the one that matches whether you need a worker or a system.