Side-by-side comparison
Cursor vs Devin
vs
Side-by-side comparison based on our agenticness evaluation framework
At a glance
Quick Facts
| Feature | Cursor | Devin |
|---|---|---|
| Category | Coding Agents | Coding Agents |
| Deployment | On-device / local | Cloud-hosted |
| Autonomy Level | Semi-autonomous | Fully autonomous |
| Model Support | Multi-model | Single model |
| Open Source | No | No |
| MCP Support | Yes | No |
| Team Support | Enterprise | Small team |
| Pricing Model | Freemium | Usage-based |
| Interface | ide, cli | gui, chat |
36-point evaluation
Agenticness
21/36
Adaptive Collaborator
Cursor
17/36
Adaptive Collaborator
Devin
Dimension Breakdown (0-4 each)
Action Capability
Cursor
3
Devin
3
Autonomy
Cursor
3
Devin
3
Planning
Cursor
3
Devin
3
Adaptation
Cursor
2
Devin
2
State & Memory
Cursor
1
Devin
2
Reliability
Cursor
3
Devin
1
Interoperability
Cursor
2
Devin
1
Safety
Cursor
2
Devin
2
Scores from our agenticness evaluation framework. Higher is more autonomous.
Features & Use Cases
Cursor
Features
- AI agents that can use an integrated browser and terminal to browse the web and run shell commands as part of workflows
- Rich context system with semantic search, ignore rules, skills, subagents, and rules to keep agents grounded in large codebases
- Model Context Protocol (MCP) support for connecting external tools and services as agent capabilities
- Automation-focused CLI with headless mode, permissions, parameters, and output formatting for scripting and CI/CD use
- Prebuilt CLI cookbooks for tasks like automated code review, fixing CI issues, secret audits, translating localization keys, and updating documentation
- Cloud agent API with HTTP endpoints and webhooks for embedding Cursor-powered agents into web and mobile applications
- Integrations with Git, GitHub, GitLab, and deeplinks to align agents with existing repository workflows
- Editor-like environment with inline editing, terminal integration, configurable keyboard shortcuts, themes, shell configuration, and multi-language support (e.g., Python, JavaScript/TypeScript, Java, Swift)
Use Cases
- Automating code review in CI using the Cursor CLI and GitHub Actions to comment on pull requests and suggest changes
- Diagnosing and fixing failing CI pipelines by letting an agent inspect logs, run terminal commands, and propose patches
- Running recurring security and hygiene checks such as secret audits or config reviews across repositories
- Translating and maintaining localization key files with an automated workflow instead of manual editing
- Keeping documentation in sync with code changes by using CLI recipes that scan code and update docs automatically
Devin
Features
- Delegates coding tasks to cloud agents
- Breaks work into ticket, plan, test, and PR stages
- Integrates with Slack, Teams, Linear, and Jira
- Tests code changes before handing them off
- Creates pull requests for native review
- Handles migration and refactor workflows
- Supports parallel work across multiple subtasks
Use Cases
- Large codebase migrations where repetitive refactoring needs to be delegated
- Modernizing monoliths into smaller modules
- Fixing high-volume lint or style issues across many files
- Delegating engineering backlog work to an AI agent while engineers review changes
- Running structured refactor tasks that need testing and PR creation
Pricing
Cursor
- **Hobby (Free):** 2000 completions/month, limited slow requests
- **Pro ($20/mo):** Unlimited completions, 500 fast requests/month
- **Business ($40/seat/mo):** Everything in Pro + admin dashboard, usage analytics, SAML SSO
Devin
Pricing not publicly available.
Analysis
Our Verdict
Pick Cursor when you want AI to act as an engineer-in-your-repo with a developer-centric interface (editor-like experience), a powerful automation CLI for CI hygiene/code review/security/doc updates, and tighter grounding via repo context (semantic search/skills/rules) plus MCP-enabled tool integrations; pick Devin when you want team/workflow-driven delegation of big chunks of engineering work (migrations/refactors/backlog cleanup) through a structured ticket→plan→test→PR pipeline integrated with Slack/Teams/Linear/Jira, delivering PRs for human review with a fully autonomous cloud agent model.
Choose Cursor if...
- +Choose Cursor if you want an AI workflow tightly embedded in your day-to-day repo work—using an editor-like environment plus an automation-focused CLI with headless mode/permissions/output formatting to run things in CI/CD (e.g., automated code review, CI-hygiene fixes, secret audits, doc updates) rather than a ticket-driven agent that mainly prepares PRs.
- +Choose Cursor if you need agents that can execute repo-aware actions using grounded context—semantic search with ignore rules/skills/subagents and MCP support to connect external tools/services as agent capabilities.
- +Choose Cursor if you want more control over how the agent operates (Cursor is described as semi-autonomous) and if you’re embedding AI into your own web/mobile product via the cloud agent API (HTTP endpoints/webhooks), where the agent understands your codebase and context.
- +Choose Cursor if your priority is repository-wide debugging/maintenance loops that involve inspecting logs, running shell commands, and proposing patches (e.g., the described Bugbot and “browser + terminal” agent workflows).
Choose Devin if...
- +Choose Devin if your main goal is delegating large, structured engineering tasks (migrations, refactors, backlog cleanup) to parallel cloud agents that move through a ticket→plan→test→PR workflow for human review.
- +Choose Devin if your team already runs work from Slack/Teams plus Linear/Jira, and you want the agent to integrate directly with those systems to coordinate subtasks and deliver results in pull requests.
- +Choose Devin if you want a more hands-off “agentic” execution model where the system is described as fully autonomous—taking the work through planning, testing, and PR creation rather than requiring you to drive the tooling via an interactive editor/CLI.
- +Choose Devin if you’re optimizing for PR-based change delivery for high-volume cleanup (e.g., repeated lint/style changes across many files) and want it to handle splitting work into subtasks and producing a PR for review as the end artifact.