Devin vs OpenDevin
Detailed side-by-side comparison to help you choose the right tool
Devin
🔴DeveloperAutonomous coding agent
An autonomous software engineering agent designed to take assigned development work from plan through implementation.
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Starting Price
$500/moOpenDevin
🔴DeveloperAI Development Assistants
Autonomous AI software engineer that generates code, debugs applications, and automates complex development workflows in sandboxed environments.
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Starting Price
FreeFeature Comparison
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Devin - Pros & Cons
Pros
- ✓Handles work beyond a single completion
- ✓Produces changes suited to pull-request review
- ✓Useful for repetitive maintenance and parallel backlog work
Cons
- ✗Current pricing was not verifiable in static HTML
- ✗Ambiguous requirements can cause expensive rework
- ✗Broad repository and MCP access needs governance
OpenDevin - Pros & Cons
Pros
- ✓Open-source core under the MIT license, including the main openhands and agent-server Docker images, which makes the agent stack inspectable and self-hostable for technical teams.
- ✓Multiple product surfaces are available from the same project: Python SDK, CLI, local GUI with REST API, hosted cloud deployment, and enterprise self-hosted deployment.
- ✓The CLI can be powered by Claude, GPT, or other LLMs, giving teams flexibility instead of locking them into one model provider.
- ✓The SDK is designed for developers who want to define agents in code and run them locally or scale them to large numbers of agents in the cloud.
- ✓OpenHands Cloud includes team-oriented features such as Slack, Jira, Linear integrations, multi-user support, RBAC, permissions, and conversation sharing.
- ✓Public repository activity, release history, stars, forks, and contributor counts can be inspected directly on GitHub and should be checked there because those metrics change frequently.
Cons
- ✗The original OpenDevin branding has moved to OpenHands, which can create confusion when searching for documentation, releases, or current product information.
- ✗Enterprise functionality is source-available but not fully MIT-licensed; running the enterprise directory beyond one month requires purchasing a license.
- ✗The tool depends on external or configured LLMs such as Claude, GPT, or other models, so real operating cost and output quality vary by provider and model choice.
- ✗Autonomous coding agents still require careful human review before code is merged, especially when they modify application logic, dependencies, tests, or infrastructure.
- ✗Self-hosting the enterprise cloud deployment requires Kubernetes and private infrastructure experience, which may be excessive for smaller teams.
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