Cursor vs GitHub Copilot: An Honest Comparison
The battle for the best AI code editor is in full swing. Cursor and GitHub Copilot are the two dominant players in this field, each with their own strengths and limitations. At Breathbase, we use both tools daily and share our unfiltered opinion in this article after months of intensive use in production projects.
GitHub Copilot: The Established Name
GitHub Copilot, developed by GitHub in collaboration with OpenAI, was the first mainstream AI code assistant. It integrates seamlessly into Visual Studio Code, JetBrains IDEs, and other popular editors. The core of Copilot is autocomplete on steroids: while you type, it suggests complete functions, classes, and even entire files.
GitHub Copilot's strengths:
- Broad IDE support: Works in VS Code, JetBrains, Neovim, and more. You do not have to leave your trusted editor.
- Copilot Chat: The chat feature lets you ask questions about code and get refactoring suggestions directly in your editor.
- Workspace agent: Can search your entire workspace to provide context-aware suggestions.
- GitHub integration: Seamless integration with pull requests, issues, and code reviews on GitHub.
- Enterprise features: Organization-wide policies, content exclusion, and audit logging for larger companies.
Cursor: The Challenger
Cursor has quickly built a massive following among developers looking for a deeper AI integration. Built as a fork of VS Code, it feels familiar but offers functionality that goes beyond what Copilot can currently do.
Cursor's strengths:
- Composer: Cursor's flagship feature. Describe in natural language what you want to build and Cursor modifies multiple files simultaneously. This is transformative for larger refactoring tasks.
- Model choice: Cursor lets you choose from different AI models (Claude, GPT-4, and more), so you can use the best model for each task.
- Superior context: Cursor is exceptionally good at understanding your entire codebase and using that context in suggestions.
- Tab flow: Tab completion in Cursor feels more intuitive and accurate than Copilot's suggestions.
- Inline editing: Select code, press Ctrl+K, and describe the desired change. Cursor modifies the selection without disturbing the rest of the file.
The choice between Cursor and Copilot is not a matter of right or wrong, but of which profile best fits your daily work and the complexity of your projects.
Head-to-Head Comparison
We tested both tools on a series of practical scenarios representative of our daily work at Breathbase:
- Autocomplete quality: Cursor wins narrowly. Suggestions are more often correct and better aligned with codebase context. Copilot is faster at generating suggestions, however.
- Multi-file editing: Cursor wins convincingly. Composer is a unique feature that Copilot simply does not match at the same level.
- Chat quality: Tie. Both tools offer good chat functionality, though Cursor has the advantage of model choice.
- Speed: Copilot wins. Suggestions appear faster, making the writing process smoother.
- Value for money: Cursor offers more functionality for a comparable price, but Copilot's free tier for open-source developers is a strong point.
Our Recommendation
After months of intensive use, our conclusion is nuanced. For developers who primarily write new code and make small adjustments, GitHub Copilot is an excellent choice. It is fast, reliable, and integrates seamlessly into your existing workflow.
But if you regularly perform larger refactoring tasks, build new features that touch multiple files, or want the flexibility to use different AI models, then Cursor is the better choice. The Composer functionality alone justifies the switch for many developers.
At Breathbase, we currently use Cursor as our primary editor for project development, supplemented with Claude Code in the terminal for more complex tasks. This combination provides us with the most productive development experience we have ever had.
Tips for Switching
Considering a switch? Here are our tips:
- Try both tools for at least two weeks before making a decision
- Test on your own projects, not just demo code
- Pay attention to how well the tool understands your specific tech stack
- Evaluate not only the code suggestions but also the chat and refactoring features
Have questions about which AI tools best fit your team? At Breathbase, we advise organizations on the optimal use of AI development tools. Feel free to get in touch.
