Cursor IDE Review 2025: The AI Code Editor That Changes How You Write Code

Cursor IDE Review 2025: The AI Code Editor That Changes How You Write Code

I switched to Cursor six months ago after years on VS Code with GitHub Copilot. I was skeptical — VS Code is excellent, my configuration was dialed in, and I had low expectations that a rebranded editor with AI marketing would change my workflow.

I was wrong. Cursor changed how I write code in ways that I couldn't get from Copilot as a VS Code plugin. Not because the AI models are different (Cursor uses the same frontier models — GPT-4o, Claude 3.5), but because the integration is fundamentally deeper.

The distinction matters: GitHub Copilot adds AI capabilities to your existing IDE. Cursor builds an IDE where AI is a first-class participant in your entire coding workflow — not just completion, but understanding, debugging, refactoring, and feature implementation.

Here's my honest assessment after six months of daily use.

What Cursor Actually Is

Cursor is an IDE forked from VS Code. If you use VS Code, the transition is minimal — all your extensions, keybindings, and settings transfer. The interface is identical. What changes is a layer of AI features built directly into the editor's architecture rather than added as a plugin.

This architectural difference is significant. Copilot operates at the level of the currently open file and recent context. Cursor indexes your entire codebase and builds a semantic understanding of the project — how files relate to each other, what types and functions are defined where, what patterns your codebase uses.

When you ask Cursor to implement a feature, it writes code that uses your actual implementations. When you ask it to explain a bug, it understands the full call stack, not just the function in front of it.

The Features That Actually Change Your Workflow

Composer: Multi-File Code Generation

This is the feature that makes Cursor genuinely different. Press Cmd+I (Mac) or Ctrl+I (Windows), describe what you want to build, and Cursor writes it across multiple files — creating new files, modifying existing ones, adding imports, and wiring the pieces together.

Real example: I described a new API endpoint in our Next.js application: "Create a POST endpoint at /api/users/[id]/preferences that validates the request body with Zod, updates the user preferences in Prisma, and returns the updated preferences."

Cursor created:

• The API route file with proper Next.js App Router structure
• A Zod schema for the request body, using the same pattern as other schemas in our project
• The Prisma query using our actual schema
• A utility function consistent with our existing patterns

This would have taken me 15–20 minutes of writing and looking up our existing patterns. Cursor did it in about 30 seconds, and it was correct on the first generation (minor type adjustment needed).

Where it doesn't always work: Complex architectural changes, anything touching infrastructure configuration, and features requiring deep domain context that Cursor doesn't have.

Chat with Codebase Context

The chat interface (Cmd+L) understands your entire codebase. You can ask questions like:

• "Where is the authentication middleware applied?"
• "What's the difference between UserService and UserRepository in this project?"
• "Why might this function return undefined sometimes?"

The answers reference your actual code, not generic explanations.

@ Mentions: You can reference specific context in prompts:

• @filename.ts — include a specific file
• @codebase — include relevant codebase context
• @url — include content from a URL (useful for documentation)
• @git — include recent git history

The @ system is powerful once you learn to use it — giving Cursor the context it needs for accurate responses.

Tab Completion (Multi-Line)

Cursor's Tab completion goes beyond next-token prediction. It suggests multi-line completions based on what you appear to be doing — not just completing the current line, but anticipating the next several lines.

If you write const fetchUser = async (id: string) => {, Cursor often suggests the complete implementation including the fetch call, error handling, and return type — based on similar patterns in your codebase.

The completions are faster and more contextually accurate than Copilot's in my experience, particularly for TypeScript where the type system gives Cursor more information to work with.

Error Explanation and Fixing

When TypeScript or your compiler flags an error, Cursor adds an inline "Explain" and "Fix" option. The explanations are often more useful than the TypeScript error message itself — particularly for complex type errors.

Fixes are correct more often than not for common error patterns. Less reliable for complex type system issues.

Where Cursor Falls Short

Privacy and Security

Cursor sends your code to AI model providers. For proprietary codebases, this is a significant consideration. Cursor offers a Privacy Mode that doesn't store prompts or completions on their servers, and Enterprise plans with stronger data handling commitments.

If you work with genuinely sensitive code (security systems, financial algorithms, healthcare data), evaluate carefully whether any cloud AI coding assistant is appropriate.

Context Window Limitations

Even with full codebase indexing, large projects hit context limits. If your project has 500+ files, Cursor can't include everything in a prompt. It selects the most relevant files, but sometimes misses important context in large codebases.

Occasional Confident Incorrectness

AI coding assistants are confidently wrong sometimes. Cursor generates code that looks correct but has subtle bugs — particularly with complex async patterns, edge cases in type handling, or integration code where the exact API contract matters.

The generated code always requires review. Cursor is a capable collaborator, not an autonomous developer. The risk is that its confident, syntactically correct output may reduce skepticism in code review.

Performance on Large Projects

With very large codebases, Cursor's indexing and context operations can be slower. Projects over a few hundred thousand lines of code may experience noticeable delays in some operations.

Cursor vs. GitHub Copilot: The Honest Comparison

My recommendation: If you primarily use AI for inline completion and single-function code generation, GitHub Copilot at $10/month is sufficient. If you regularly implement features that span multiple files, debug complex issues, or work in large codebases where understanding context across files matters, Cursor Pro at $20/month delivers meaningfully better results.

Setup and Getting Started

1. Download from cursor.com (available for Mac, Windows, Linux)
2. Import your VS Code settings on first launch — takes under a minute
3. Your existing extensions transfer automatically
4. Add your Anthropic or OpenAI API key to use your own models (optional — Cursor's bundled access is included in the subscription)

The settings worth configuring:

• Enable "Codebase Indexing" in settings for full project context
• Set your preferred AI model (Claude 3.5 Sonnet is my choice for most tasks)
• Configure Privacy Mode if required for your workplace

Frequently Asked Questions

What makes Cursor different from GitHub Copilot?

Cursor is a full IDE with deep codebase indexing and multi-file editing (Composer). Copilot is a plugin with excellent inline completion but limited multi-file context. Cursor uses frontier models of your choice; Copilot uses OpenAI models.

Is Cursor worth the price?

For professional developers who write code daily, Cursor Pro ($20/month) delivers productivity gains that easily justify the cost. The free tier is useful for evaluation but too limited for heavy daily use.

Does Cursor work with all languages?

Best with TypeScript, Python, Go, Rust, and popular languages with large training datasets. Quality declines for niche languages and proprietary DSLs.

What are Cursor's best features?

Composer for multi-file code generation, codebase-aware chat with @ context references, multi-line tab completion, and integrated error explanation and fixing.

Final Thoughts

Cursor is the first AI coding tool that genuinely changed my development workflow rather than just making existing patterns slightly faster. The Composer feature — describing features and having Cursor implement them across multiple files with awareness of your project's patterns — is something Copilot as a plugin can't match.

The productivity gain is real but requires adjustment: learning to write effective prompts for Cursor's capabilities, building the judgment to review AI-generated code appropriately, and developing a workflow that uses Cursor's strengths (multi-file generation, debugging context) rather than just treating it as a better autocomplete.

Six months in, I wouldn't go back to a coding workflow without it. The question isn't whether AI coding assistants improve productivity — they do — it's which one best fits your workflow.

For a comparison of productivity tools that complement your development environment, the Notion vs. Obsidian vs. Roam comparison covers the knowledge management tools that pair well with an AI-enhanced development workflow.