Devin, built by Cognition AI, was marketed from launch as one of the first AI software engineers rather than a coding assistant: instead of suggesting lines as you type, it takes a task description and works through planning, writing, debugging, and shipping code with comparatively little back and forth. It can run on OpenAI, Claude, or Gemini models, as well as Cognition's own SWE 1.6 models trained specifically for coding tasks.
The framing around Devin generated significant hype when it launched, with early demos showing it completing real engineering tasks end to end, including fixing bugs and deploying changes. Like most autonomous agents, real-world results have been more mixed than the demos: it performs well on well-scoped tasks with sandboxed execution and test runs, and less reliably on ambiguous or large-context problems that need deep codebase knowledge.
Pricing runs from a free tier through Pro at \(20 per seat per month to Max at \)200 per seat per month, billed using Cognition's own Agent Compute Units rather than a flat per-task rate, so actual cost depends heavily on how much agent time a task ends up needing.