AI-Native Coding IDE

Cursor

★★★★★4.9/5 Freemium
The AI-first IDE built for writing, editing, and running code with GPT-5.5, Claude 4.5, and Gemini 3

Cursor is the dominant AI-native IDE in 2026 with $2 billion in annual recurring revenue. Unlike GitHub Copilot (which bolts AI onto existing editors), Cursor is an IDE rebuilt from the ground up around AI. Its Composer feature can edit multiple files simultaneously, its Agents Window runs parallel autonomous coding tasks, and it lets you switch between Claude, GPT, and Gemini models mid-session. If you write code professionally, Cursor is the tool to beat.

What it can do

Key Features

Composer — multi-file AI editing

Cursor's Composer is its killer feature: describe what you want to build in plain English and it edits multiple files simultaneously, maintaining context across your entire project. Unlike GitHub Copilot's line-by-line suggestions, Composer understands what you are trying to achieve and implements it holistically — updating components, tests, and documentation in one coordinated operation.

Agents Window — parallel autonomous tasks

Cursor 3 introduced the Agents Window, which lets you run fleets of AI coding agents in parallel. One agent refactors your authentication module while another writes tests for the API layer and a third updates the documentation. You review and approve each agent's work. This fundamentally changes the ratio of human to AI coding in your workflow.

Multi-model support (Claude, GPT, Gemini)

Cursor lets you switch between Claude 4.5, GPT-5.5, and Gemini 3 Pro mid-conversation. Use Claude for complex architectural reasoning, GPT for rapid prototyping, and Gemini for tasks that benefit from its massive 1M token context. The model picker is one click away in every conversation.

Supermaven-powered autocomplete

Cursor's autocomplete engine (powered by Supermaven) is the fastest in the industry — completing multi-line suggestions in under 30ms. It is context-aware across the entire codebase, not just the current file, so it suggests patterns consistent with how your specific project is written.

Codebase-wide context (@ references)

Use @file, @folder, @docs, and @web to pull any context into your conversation. Reference an entire codebase with @codebase, pull in official library documentation with @docs, or search the web mid-conversation with @web. Cursor knows your entire project structure and uses it to make every suggestion more relevant.

Privacy Mode and local-first option

Cursor offers a Privacy Mode that ensures your code is not stored or used for model training. For enterprise teams, Cursor also offers a self-hosted option where all AI inference runs in your infrastructure. This is a key differentiator for companies with compliance requirements.

Step by step

How to get started

1

Install Cursor and import your VS Code profile

Go to cursor.com → Download (macOS, Windows, or Linux — all supported). The installer takes 2-3 minutes.

On first launch, Cursor asks: Import VS Code settings? Click Import. This copies your extensions, themes, keybindings, and settings file. You are in a familiar environment from minute one — no reconfiguration needed.

Verify the import: check your theme is correct (Ctrl/Cmd + K → T), open Extensions panel (Ctrl/Cmd + Shift + X) to confirm your extensions transferred, and test a keybinding you use frequently.

2

Configure your AI models and Privacy Mode

Open Cursor Settings: Cmd/Ctrl + Shift + J → click the Models tab.

Enable the models you want. Recommended setup:
claude-opus-4-5 — best for complex reasoning, architecture, and long context
gpt-5.5 — best for creative code and rapid iteration
gemini-3-pro — use when you need 1M token context for giant codebases

If your codebase contains proprietary code, sensitive data, or customer information: enable Privacy Mode on this screen. This prevents your code from being stored or used in model training — a critical requirement for enterprise and regulated environments.

Open your project: Cmd/Ctrl + O → select your project folder.

3

Have your first conversation with your codebase

Open the AI Chat: Cmd/Ctrl + L.

Do not start by writing code. Start by asking questions to build your understanding:

Walk me through the architecture of this codebase. What does it do, what is the entry point, how does data flow through the system, and what are the main layers (API, business logic, data)?

Then drill down:
Where is user authentication handled? Show me the files involved and explain how the JWT token validation chain works from the route to the middleware.

Type @codebase in your message to perform a semantic search across all files. Type @filename.ts to reference a specific file directly. Cursor reads your actual code — not generic documentation — and answers based on YOUR specific implementation.

4

Build your first feature with Composer

Composer implements features across multiple files simultaneously. Press Cmd/Ctrl + I to open it.

Write a specific, complete feature description. Include your stack and constraints:
Add rate-limited email verification to the user registration flow. Requirements: POST /auth/send-verification accepts an email, generates a 6-digit OTP, stores it in Redis with 10-minute expiry, and sends it via the existing EmailService. Rate limit: max 3 requests per email per hour — return 429 if exceeded. Return 422 for invalid email format, 400 for missing fields. Add 5 Jest unit tests: valid request, rate limit exceeded, invalid email, OTP expiry after 10 minutes, and resend before expiry. Follow the existing controller/service/repository pattern in the codebase.

Composer reads your codebase, plans the implementation, writes all files, and shows a unified diff. Review each file, then accept or reject.

5

Create your .cursorrules file

Create a file named .cursorrules in your project root directory. Cursor reads this file on every request and applies it automatically — no need to repeat your conventions in every prompt:

# Tech Stack
- Node.js 22 + TypeScript strict mode
- Express 5 + Prisma ORM + PostgreSQL
- Jest + Supertest for testing
- Zod for all input validation (never manual checks)

# Architecture Rules
- Controllers: HTTP layer only — no business logic
- Services: all business logic
- Repositories: all database queries (Prisma calls here only)
- Routes: define paths and middleware only

# Code Standards
- All exported functions: JSDoc with @param and @returns
- No 'any' type anywhere — always type explicitly
- Prefer early returns over nested if/else
- Tests mirror src/ structure in __tests__/

# Forbidden
- Never console.log in production (use logger service)
- Never expose stack traces in API responses
- Never hardcode credentials or secrets

6

Fix failing tests autonomously with the Agents Window

Run your test suite:

npm test
When tests fail, open Cursor Chat (Cmd/Ctrl + L) and say:
My Jest tests are failing. Here is the failure output: [paste the test runner output]. Read the failing test file and the source file it tests. Identify the root cause — do not modify the test assertions, only fix the production code. After fixing, explain what was wrong and why.

For running multiple fix cycles autonomously, use the Agents Window (click the Cursor logo → Agents).

Create an agent with this instruction: Run the test suite, read any failures, fix the production code (not tests), re-run tests to confirm fixes, and repeat until all tests pass or you hit an issue that requires human input.

The agent runs the test → fix → retest loop while you work on something else.

7

Deploy your first AI-built feature to production

Before deploying, ask Cursor for a production readiness review. Open Chat and type:
Review all the changes made to the codebase since the last git commit for production readiness. Check for: 1) security issues (exposed API keys, missing input sanitization, auth bypass risks), 2) unhandled error cases that could cause 500 errors, 3) N+1 database query patterns, 4) console.log statements left in production code, 5) missing environment variable definitions, 6) breaking changes to existing API contracts. Output as a numbered checklist with severity labels (Critical/High/Medium/Low).

After resolving every Critical and High item:

# Run full quality checks
npm test && npm run typecheck && npm run lint

# If all pass, commit and push
git add -A
git push origin main
Ask Cursor to write your commit message: Write a conventional commit message for these changes (format: type(scope): description). Include a body explaining the WHY, not the WHAT.

Pricing

Plans & Pricing

Hobby
$0/mo
2,000 code completions/month, 50 slow premium requests. Good for trying Cursor on personal projects.
Pro
$20/mo
Unlimited code completions, 500 fast premium requests/month, access to all models (Claude, GPT, Gemini). Most developers choose this.
Business
$40/user/mo
Team management, Privacy Mode enforced for all users, centralized billing, SSO, usage analytics.
Analysis

Pros

  • Built specifically for AI — not a plugin bolted onto existing tools
  • Composer implements features across multiple files simultaneously
  • Agents Window runs parallel coding tasks autonomously
  • Imports VS Code settings, extensions, themes — zero reconfiguration
  • Multi-model support: switch between Claude, GPT, Gemini freely
  • The fastest autocomplete in the industry (Supermaven-powered)

Cons

  • $20/month Pro plan — more expensive than GitHub Copilot ($10)
  • Can feel overwhelming for developers new to AI-assisted coding
  • Requires trust: Agents make changes you must review
  • Occasional model request throttling on heavy use days
  • Privacy Mode (required for sensitive code) limits some features
FAQ

Frequently Asked Questions

How is Cursor different from GitHub Copilot?
GitHub Copilot adds AI suggestions to VS Code and JetBrains — it works inside existing editors and focuses on line-by-line autocomplete. Cursor is a complete IDE rebuilt from scratch around AI. Its Composer can implement entire features across multiple files, its Agents Window runs autonomous parallel tasks, and it supports model-switching mid-session. Copilot is $10/month and faster for simple completions; Cursor is $20/month and far more powerful for complex tasks. Most serious developers use both.
Does Cursor work with my existing VS Code setup?
Yes, completely. Cursor is built on VS Code and imports your entire profile on first launch — extensions, keybindings, themes, settings files. You are in a familiar environment from minute one. All VS Code extensions work in Cursor. The only thing that doesn't transfer is GitHub Copilot's subscription (you switch to Cursor's built-in AI).
Is my code safe with Cursor?
By default, Cursor sends your code to AI providers for inference. Privacy Mode (available on all plans) disables code storage and training. For enterprise teams with compliance requirements (HIPAA, SOC2, GDPR), Cursor offers a self-hosted option where inference runs in your infrastructure with no code leaving your environment. Review the privacy policy at cursor.com/privacy.
Which AI model should I use in Cursor?
Use Claude 4.5 for complex architectural reasoning, large refactors, and tasks that require sustained logical reasoning. Use GPT-5.5 for rapid prototyping, creative solutions, and when you want faster responses. Use Gemini 3 Pro when you need its 1M token context window to analyze a massive codebase all at once. In practice, most developers keep Claude as their default and switch when needed.
Can Cursor work on large codebases?
Yes. Use @codebase to search and reference your entire project. Cursor builds a semantic index of your codebase on first open (takes a few minutes for large repos). After indexing, it can answer questions about any part of your project and make changes that are consistent with your existing patterns. Tested and used on codebases with 500,000+ lines of code.

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