Cursor vs Copilot in 2024

Quick thoughts on AI coding assistants after using both extensively. Which is better? (Spoiler: it depends.)

I have used GitHub Copilot since the original technical preview, and I have used Cursor as my daily driver for the better part of a year. People keep asking me which one to pick. The honest answer is that they are different tools that overlap on the easy questions and diverge on the interesting ones.

What they are good at, both of them

Both products give you confident inline completions for the boilerplate parts of a codebase. Tests, mappers, CRUD endpoints, prop types, the kind of work that used to feel like typing. They both understand the file you are in well enough that the suggestions feel local and contextual rather than autocomplete from a parallel universe.

If your goal is to type less while writing in a familiar codebase, either tool will earn its subscription back inside a week.

Where Copilot wins

Copilot is the safer institutional choice. It plugs into your existing GitHub identity, it ships with enterprise controls, and the new chat features are competitive for the kind of question and answer flows that used to send you to Stack Overflow. The integration with VS Code, JetBrains, and Visual Studio is mature, and the sidecar features (commit message suggestions, PR descriptions) are quietly useful.

For teams that need a single approval cycle from security and procurement, Copilot is usually the path of least resistance.

Where Cursor wins

Cursor wins when the unit of work is bigger than a single completion. The composer style flows, the project wide context, and the agentic edits across multiple files are real productivity multipliers when you are doing refactors, scaffolding new features, or porting code between frameworks.

In particular:

• Multi file edits. Cursor's ability to plan and apply changes across several files in one go is the feature I miss most when I switch back.
• Model choice. Cursor lets me reach for the model that fits the task, including longer context models for codebase wide questions.
• Codebase context. The retrieval over the whole repo is noticeably better than what I get from Copilot's chat by default.

How I actually use them

I no longer think of these as alternatives. I think of them as different tools for different rhythms.

When I am in flow, writing the next obvious line, inline completion is what I want, and either tool is fine. When I am restructuring a feature or bringing a new collaborator's code into a project, I want a model that can hold the whole change in its head. That is currently Cursor.

For client work where the codebase has to live in their environment under their controls, I usually recommend Copilot Enterprise unless the team has a specific reason to evaluate Cursor's business plan. The tooling chain matters more than the marginal capability difference.

What neither of them is good at, yet

Both tools still hallucinate APIs that do not exist. Both will confidently write a test that passes by mocking the thing under test. Neither one understands your domain unless you tell it. The teams that get the most out of these tools have invested in making their codebase legible to an LLM: clear naming, good types, focused modules, useful comments at the boundaries.

If your codebase confuses new humans, it will also confuse the AI. The fix is the same in both cases.

My recommendation in one sentence

If you are an individual developer who wants the most leverage today, try Cursor. If you are a team looking for an enterprise wide rollout with minimum friction, start with Copilot. If you can afford it, run both for a month and let your engineers tell you which one they would fight to keep.

For more on how I think about AI in engineering workflows and developer productivity, there are longer pieces in the archive.

References

• GitHub Copilot
• Cursor
• GitHub Copilot Documentation