Cursor: The AI-Powered Code Editor That Changed How Developers Work

Cursor has gone from an early-stage open-source project to one of the most talked-about developer tools in the AI coding space. When it first launched, it was a relatively simple code editor with some AI features bolted on. Now it is a full-featured IDE with deep integration of frontier language models, a composer view for multi-file edits, and an inline editing workflow that genuinely changes how fast you can build software.

What makes Cursor different from just using GitHub Copilot inside VS Code is the degree to which AI is woven into the editing experience. It is not an autocomplete plugin. It is an editor built from the ground up around the idea that you should be able to describe changes in natural language, see diffs applied across multiple files, and iterate on those changes without leaving the editor.

From VS Code to Cursor

If you are a VS Code user, the migration is nearly seamless. Cursor is built on the same foundation, so your extensions, keybindings, themes, and settings all carry over. The interface looks and feels familiar because it is familiar. The team made a deliberate choice not to reinvent the editor chrome. Instead, they focused their energy on the AI features layered on top.

This means you do not have to learn a new tool from scratch. You open Cursor, and everything you know about VS Code still applies. The file explorer, the terminal, the command palette, split views, the settings menu - all of it works the same way. The difference is what happens when you start talking to the AI.

Pricing and Access

Cursor offers a free tier to get started. You get a 2-week free trial with 2,000 completions, 50 slow premium requests, and 200 uses of Cursor's smaller model. This is enough to get a real feel for the tool before committing to a paid plan.

The Pro tier runs $20 per month. With that you get unlimited completions, 50 fast premium completions per month, and unlimited slow premium requests. The distinction between fast and slow matters in practice. Fast requests use dedicated inference capacity and return results almost immediately. Slow requests queue behind other users and can take a few extra seconds.

Premium requests apply to frontier models like GPT-4o and Claude 3.5 Sonnet. The smaller models, including Cursor's own model (sometimes called Cursor Small), use a separate credit pool. This tiered approach means you can use the smaller, cheaper model for routine edits and save your premium credits for the situations where you need the strongest model available.

Composer View: Multi-File Editing

The composer view is the feature that sets Cursor apart from every other AI coding tool at the time of its release. Instead of editing one file at a time with inline suggestions, the composer lets you describe a set of changes that span multiple files, and the model applies diffs across all of them simultaneously.

Here is a concrete example. Say you have a Next.js application with a header component, a footer component, and a main page. You notice three things: the page title disappears on mobile, the footer nav items should stack into two columns on small screens, and you want a subtle gradient on the background. In a traditional workflow, you would open each file, make the changes manually, and test between each edit.

In Cursor's composer, you type all three requests at once. The model analyzes the codebase, identifies which files need changes, and generates diffs for each one. You see a preview of every change before accepting anything. You can tab through the diffs one by one, and for each one, you press Command+Enter to accept. Reject the ones you do not want. Once you accept, the changes are applied and you refresh the page to see the results.

This workflow is dramatically faster than the file-by-file approach. For a set of three related UI changes, what would normally take 10 to 15 minutes of manual editing collapses into about 30 seconds of describing what you want and reviewing the output.

Inline Editing with Command+K

Beyond the composer, Cursor has a powerful inline editing mode triggered by Command+K. This is closer to the traditional AI coding assistant experience, but with a few key differences.

You can highlight a block of code and describe what you want changed. The model generates a diff, and you see exactly what will change before accepting. This is useful for targeted edits where you know exactly which code needs to change but want the AI to handle the implementation.

The inline editor also works for generating code from scratch. Highlight an empty area, describe a component or function, and the model writes it. This works well for boilerplate, utility functions, and UI components where you know the shape of what you want but do not want to type it out manually.

One particularly useful feature: if you break your code and see errors in the Problems panel, you can click "Fix with AI" directly from the error. The model reads the error message, inspects the relevant code, and applies a fix. For TypeScript type errors spread across multiple files, this can save significant time.

Model Selection

Cursor gives you control over which model handles each request. At the time of recording, the available options include Claude 3.5 Sonnet, GPT-4o, GPT-4o Mini, and Cursor's own smaller model. Each has different strengths.

For complex multi-file refactors and architectural decisions, Claude 3.5 Sonnet and GPT-4o tend to produce the best results. For quick inline edits, formatting changes, and simple generations, the smaller models work fine and save your premium credits.

You can also bring your own API keys. If you have an OpenAI or Anthropic API key, you can plug it directly into Cursor and use it instead of the built-in credit system. This is useful for teams that already have API access and want to manage their own usage.

The Chat Interface

Cursor includes a chat panel accessible with Command+L. This is not just a ChatGPT wrapper. The chat has the ability to reference specific files, folders, your entire codebase, web search results, git history, and documentation.

The file and folder context is the most useful part. You can drag in three files and say "make some suggestions on how this can be improved." The model reads the code and returns specific, actionable improvements. For each suggestion, there is an "Apply" button that generates a diff you can accept or reject. This turns the chat from a conversation into an interactive refactoring tool.

Web search integration means you can ask questions like "what is the latest version of Next.js" without leaving your editor. The model searches the web and returns a current answer. This eliminates the constant context-switching between your editor and a browser tab.

Who Is Cursor For?

One of the most interesting things about Cursor is how broad its audience is. Beginners can use the composer to build entire pages by describing what they want. Backend engineers who do not work with CSS regularly can scaffold frontend layouts in seconds. Experienced developers can use the inline editor and chat to move faster on the tedious parts of coding while maintaining full control over the important decisions.

The speed advantage is real even for experienced developers. It is not about the AI being smarter than you. It is about the AI handling the mechanical work while you focus on the design and architecture. Writing a landing page layout, setting up boilerplate, generating CRUD endpoints, fixing type errors across a refactor - these are tasks where the AI saves meaningful time without introducing risk.

If you are coming from GitHub Copilot, the jump to Cursor is worth trying. Copilot is excellent for inline completions, but Cursor's composer view and multi-file editing are a fundamentally different workflow. It is the difference between having autocomplete that finishes your sentences and having a pair programmer that can read your entire codebase and apply changes across it.

Terminal Commands

A small but useful feature: Command+K also works in the integrated terminal. If you forget the exact command to install a package or run a script, you can describe what you want and the model generates the terminal command. This is helpful for tools with complex CLI flags or for developers who switch between package managers.

Practical Considerations

Cursor is not perfect. The model sometimes misunderstands the scope of a change, or generates code that is syntactically correct but does not match your project's conventions. The chat suggestions are not always actionable. And for very large codebases, the context window limitations of the underlying models can mean the AI misses relevant code in distant files.

But these are limitations of the current generation of language models, not of Cursor specifically. As the models improve, tools like Cursor are positioned to benefit directly. The editing interface, the diff preview system, the multi-file composer - these are the scaffolding that turns raw model capability into a usable workflow.

Where Cursor Fits in the Ecosystem

Cursor sits in an increasingly crowded space alongside Windsurf, Zed, and the growing list of AI-native editors. What distinguishes it is the polish of the editing experience. The keyboard shortcuts are well thought out. The diff previews are clear. The composer handles multi-file changes gracefully. These details matter when you are using a tool for eight hours a day.

The AI coding editor landscape is evolving rapidly. Models are getting better, context windows are getting larger, and the integration points between AI and the development workflow are multiplying. Cursor's bet is that the editor is the right place to coordinate all of this. Based on the current trajectory, that bet looks solid.