Cursor AI Review (2025): Coding Will Never Be the Same Again

There’s something surreal about the first time you use Cursor AI.

You’re typing away in your usual VS Code flow, neck-deep in messy logic and duplicated functions—and then you ask the AI to clean it all up. 

You don’t expect much. Maybe a suggestion or two, a polite refusal. Instead, it rewrites your files, updates your references across the repo, opens pull request-style tabs, and quietly waits for your approval.

It doesn’t just give you answers. It understands your codebase. It works in your IDE. It moves with you.

It’s the closest I’ve ever felt to coding with a mind-reading co-pilot.

Cursor AI isn’t trying to replace developers. It’s designed to make you 10x faster, more focused, and less prone to soul-crushing repetitive tasks. 

This isn’t Copilot humming in the background. This is an active agent—one that analyzes your files, answers deep project-specific questions, and rewrites code like it knows what you’re building better than you do.

So what’s the catch? Is it all hype? Let’s take it apart—from its raw power to its rough edges—and figure out if Cursor is worth bringing into your workflow.

What Exactly Is Cursor AI?

Cursor is a full-fledged code editor built on top of VS Code. So if you’re used to working in that environment, it feels instantly familiar—but smarter. Behind the scenes, it’s a thin layer over VS Code that unlocks some deeply powerful AI integrations.

You don’t lose your extensions, settings, or themes. You just gain an AI that lives inside your editor and helps you write, refactor, understand, and debug your code. Unlike most coding assistants that sit in the corner making suggestions, Cursor’s AI feels integrated, like it’s thinking through the code with you.

The key idea? Context.

Cursor scans your entire repo. It understands how your modules talk to each other. It doesn’t just look at the file you’re working on—it understands the system as a whole. 

You can literally ask it: “Where is the logging configured?” or “Can you refactor this function to use async logic?” and it’ll not only respond—it’ll take action.

Cursor’s Core Features (What It Does in Your Editor)

Using Cursor isn’t just about getting fancy auto-completions. It’s about unlocking a toolbox of developer power moves that feel like cheating—but aren’t. 

Let’s break down the major features you’ll notice when you spend time inside Cursor.

1. Context-Aware Chat

This is your direct communication line to the AI, like ChatGPT, but living inside your actual code editor, with full awareness of your project.

Highlight a block of code and type: “Can you add error handling here and make sure it matches the pattern we use in authService.js?”

The cursor will read your actual codebase, examine how similar logic is written elsewhere, and then generate contextually appropriate code, right inside your file.

What’s notable here is that this chat isn’t just reactive. You can ask it to find a bug, summarize what a component does, and create an entirely new file from a prompt. 

Also, you can go ahead and answer deep architecture questions, like “Where are we sanitizing inputs before DB writes?”

It is like pair programming with someone who has already read every line of your repo twice.

2. Multi-File Agent Mode

Now here’s where things get wild.

Agent Mode lets Cursor act as a kind of autonomous code assistant. You give it a goal.

Cursor will:

1. Identify all the relevant files.
2. Rewrite them.
3. Add new ones if needed.
4. Suggest changes in a clean, side-by-side diff format.
5. Present them in review tabs—you decide what to accept, change, or reject.

It’s like getting a PR from a smart teammate who understands your stack.

This isn’t just one-file-at-a-time stuff. It scans your repo, understands dependencies, and shows surprising competence when updating cross-file logic.

3. Background Agent & Bug Bot

This is Cursor quietly flexing behind the scenes.

If enabled, the Background Agent keeps an eye on your codebase and proactively suggests improvements or simplifications. And if you paste a stack trace or failed test result, Bug Bot jumps in to analyze the error and propose a fix. It can even trace through logs or test output and link back to the offending code.

So instead of hunting for line numbers and breakpoints, you can just drop the error into Cursor and get: “Here’s likely what went wrong. Want me to fix it?”

4. Bugbot

When Cursor suggests code edits—whether from a refactor, a bug fix, or a generated feature—it doesn’t blindly change your files.

Instead, it opens up review tabs. Think of these like local pull requests:

• You get a clear “before and after” different view.
  
• Changes are broken into chunks by file.
  
• You can approve, reject, or tweak each one.
  

This makes it easy to stay in control and trust the AI, because you’re never left guessing what it changed.

You’re always in the driver’s seat.

5. Model Flexibility & Token Control

Cursor lets you choose from a handful of top AI models:

• GPT-4
• Claude Sonnet
• Claude Opus
• Gemini 1.5
• Mistral

You’re not locked into one. Some are better for writing tests. Others are faster. Cursor even lets you set a “default” model and context token limit (up to 200K tokens for supported models).

You can also view and control your token usage so you don’t accidentally burn through your quota. 

This is especially useful for devs working on large monorepos or enterprise codebases where context depth is everything.

6. .cursorrules & Customization

Cursor supports a hidden gem called .cursorrules, a file you can create at the root of your repo to customize how the AI behaves.

You can define Coding style preferences, Project conventions (naming, error handling, architecture patterns), and Auto-reply behavior (e.g., “Always use try/catch with DB access”)

This means the AI adapts to your team’s way of working instead of pushing its own style.

What Cursor Does Better Than Other AI Coding Tools

Cursor doesn’t aim to be a background assistant that occasionally suggests a line of code. It positions itself as a full-fledged coding partner—one that reads your entire project, adapts to your structure, and works across files with intention and precision. 

Here’s how it distinctly outperforms other tools in the AI development space.

1. Codebase-Wide Awareness

While most AI tools limit their scope to the active file or function, Cursor understands and operates across the full codebase. 

It indexes the repository to gain context about project structure, function dependencies, naming conventions, and module relationships. 

This means it generates responses that are aligned with your actual architecture, not generic boilerplate. The result is more accurate suggestions that fit seamlessly into the existing code.

2. Structured, Multi-Step Execution

The cursor AI doesn’t just react to a single prompt—it interprets the broader task and breaks it into logical steps. 

Whether it’s a refactor, feature addition, or migration, the AI performs a detailed sweep of the code, applies consistent changes, and presents those edits in a structured, reviewable format. 

This level of task-based reasoning is what makes Cursor feel more like an intelligent co-developer rather than a glorified autocomplete engine.

3. Review-Oriented Change Management

One of Cursor’s strongest advantages is how it handles proposed changes. It presents edits in a PR-style interface with clear diffs, broken down by file and change type. 

This format encourages deliberate review and allows developers to maintain full control over what gets committed. It reduces guesswork, builds trust in AI suggestions, and maintains development discipline—something most AI tools lack.

4. Intelligent Cross-File Refactoring

Cursor handles multi-file edits with a level of sophistication that other tools often miss. Instead of offering isolated suggestions, it analyzes how changes in one file will affect others, and proactively updates those references. 

This includes import paths, method usage, naming conventions, and interface dependencies. The process ensures that code remains consistent, functional, and aligned with existing patterns—without the developer needing to track every touchpoint manually.

5. Developer-Centric Customization

Unlike fixed AI tools that operate on predefined behaviors, Cursor offers customization features that adapt the AI to your team’s style and standards. 

Through features like .cursorrules, developers can instruct the AI to follow specific patterns, avoid certain practices, or prioritize a preferred structure. 

Combined with the ability to choose between multiple AI models and control privacy settings, Cursor provides a more flexible, developer-focused experience than its competitors.

The Downsides—Because It’s Not All Perfect

Let’s talk pain points. Cursor isn’t flawless—and some of these might be deal-breakers depending on how you work.

Tab Overload

When the AI goes into Agent Mode or makes suggestions, each one pops open a new tab. If you asked for a big refactor? Prepare for a dozen tabs. It’s manageable, but can get chaotic fast.

Learning Curve

There’s some friction in learning what it can and can’t do. You’ll need to explore features for custom guidance) or the “background agent” to really unlock its power. It’s not as plug-and-play as Copilot.

Linux Quirks

Cursor uses AppImage for Linux distribution, which means it doesn’t integrate as smoothly with some desktop environments. Also, syncing some global settings from VS Code might require manual tweaks.

Pricing Complexity

While the pricing is fair for what you get (more on that next), the mix of token usage, model selection, and quotas can be confusing. If you’re not tracking your usage, you might run out of context tokens mid-sprint.

Cursor AI Pricing (Accurate as of June 2025)

Cursor’s pricing is split into two tracks: Free and Pro. Here’s how it works:

Free Plan

• Use of GPT-3.5 (no GPT-4 or Claude)
  
• Limited repo indexing and codebase context
  
• No background agent
  
• Works for hobbyists, personal use, or just trying things out
  

Pro Plan — $20/month

• Access to GPT-4, Claude Sonnet, Gemini Pro, and more
  
• Up to 200k tokens of context
  
• Background agent support
  
• Full repo indexing
  
• Unlimited codebase questions and agent actions
  
• Use in commercial projects
  

There’s no per-seat pricing—this is per user. So for individuals and freelancers, it’s simple and flat. For teams, they’re rolling out organization features, but those are still evolving.

Is it worth the price? Honestly, if you spend 10+ hours a week coding, $20/month for this level of assistance feels more like an investment than a cost. Just the time saved on boilerplate and refactoring easily covers that.

Final Verdict: Is Cursor AI Worth It?

If you’re a developer who likes staying in flow, hates context switching, and wants smarter help without losing control—Cursor is a game-changer.

I’ve used AI tools that suggest code, and I’ve used ones that explain it. Cursor does both—but then goes a step further: it acts. It rewrites. It pulls in project-wide context. It makes intelligent changes across files and wraps them up neatly like a mini pull request. And it lets you decide what goes through.

You still need to think. You still need to steer. But Cursor makes coding smoother, cleaner, and honestly… a little more fun.

It’s not perfect. The UI can get a bit chaotic with tabs. Linux support could use polish. And the learning curve isn’t zero. But after a couple weeks, it felt indispensable—like tab completion once did, or Git before it was second nature.

For $20/month? If you’re serious about shipping better code faster, it’s a steal.

FAQs – What Everyone’s Asking About Cursor AI