Cursor
AI-native code editor built on VS Code
A code editor forked from VS Code with AI woven into every layer — multi-line autocomplete, an agent that edits across files, and access to several frontier models from one subscription.
Picked for AI Tools for Solo Founders and AI-Native Code Editors for Developers.
Cursor took VS Code as a starting point and rebuilt the editing experience around AI: autocomplete that predicts multi-line edits rather than single tokens, and an agent mode that can read a task description, plan a set of changes across several files, and apply them with a diff to review. Because it’s a full editor rather than a plugin, it can reach further into a project than a chat sidebar bolted onto an existing IDE.
It suits developers who want an agent doing real work inside their actual codebase — refactors, feature scaffolding, multi-file changes — rather than a chat window they copy-paste from. The ability to switch between Claude, GPT and Gemini per task, from inside the same editor and subscription, is one of its more practical advantages over single-model competitors.
The catch is the credit system: since Cursor moved to credit-based billing, the $20 Pro plan’s included credits get consumed quickly by anyone leaning on frontier models for agent work, and real-world costs for power users often land closer to $40–50 a month. Pricing has shifted more than once in the past year, and very large or unusual codebases can still trip up the agent’s context, producing changes that look plausible but don’t quite fit the project’s conventions.
Features
Tab autocomplete
Predicts multi-line edits and the next place you'll want to jump to, not just the next token.
Agent mode
Describe a task in plain language and Cursor plans and applies changes across multiple files, running commands as needed.
Model choice
Switch between Claude, GPT and Gemini models for a given task, drawing from a shared monthly credit pool.
Codebase-aware chat
Questions are answered against an index of the whole repository, with specific files and symbols attachable, rather than against the open buffer alone.
Rules files
Project conventions written down once and applied to every suggestion, so the same correction is not made by hand on each review.
Background agents
Longer tasks run outside the editing session and report back, rather than holding the editor while they work.
Use cases
- Refactoring or extending an existing codebase with agent-driven multi-file edits
- Fast prototyping where most of the boilerplate is AI-written
- Reviewing and explaining unfamiliar code before making a change
- Writing a repo's conventions into rules so every suggestion already follows them
- Asking questions about a codebase you did not write
- Getting a failing test to pass without hunting the cause by hand first
Compare Cursor head to head
Side-by-side comparisons, on pricing, platforms and where each one wins.
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