Devin

Autonomous AI software engineer that plans, codes, tests, and delivers pull requests in a sandboxed environment.

By The Codegen Team · Updated March 27, 2026 · Originally published March 26, 2026

Verdict

The most autonomous coding tool available. Best for teams with backlogs of well defined tasks that can be delegated without continuous oversight. Independent testing shows 15% to 30% success rates on varied tasks, so set expectations accordingly and start with contained bug fixes.

What does Devin do?

Devin by Cognition AI is an autonomous AI software engineer that handles end to end development tasks in its own sandboxed environment. You describe a task through the interface, Slack, or Jira, and Devin plans the approach, writes code across files, runs tests, debugs failures, and delivers a pull request. It operates with a shell, code editor, and browser, the same tools a human developer uses.

Devin 2.0 launched in April 2025 with a dramatic price reduction from $500 per month to $20 per month for the Core plan. The update introduced Interactive Planning (review and approve the approach before code is written), Devin Search (natural language codebase navigation), and Devin Wiki (automatic repository documentation that updates every few hours). Cognition reports Devin 2.0 completes 83% more junior level tasks per ACU compared to version 1.

Performance data is mixed. On SWE bench, Devin resolves 13.86% of real GitHub issues end to end, a 7x improvement over previous AI systems. Independent testing consistently shows 15% to 30% success rates on varied real world tasks. The tool works best on well specified, contained tasks: bug fixes, small features, framework upgrades, code migrations. Open ended or architecturally complex work still requires heavy human guidance.

The Cognition acquisition of Windsurf in December 2025 for $250 million signals a roadmap toward merging IDE assistance with autonomous execution. As of March 2026, the products remain separate, but the combined entity has the potential to offer the most complete AI development workflow if integration goes well.

Who it's for

Best for

Engineering teams with large backlogs of well defined, contained tasks. Product managers who need to clear routine engineering work without consuming developer time. Startups testing autonomous coding on small features and prototypes.

Not for

Teams that need real time pair programming or rapid iteration cycles. Developers working on architecturally complex or ambiguous problems. Budget constrained teams, since effective cost per completed task can be high given current success rates.

Where it excels

Limitations to know

Frequently Asked Questions