The Editor That Made Me Question Everything I Thought I Knew About Coding
A designer friend of mine — who also writes Python scripts for automating her workflow — sent me a voice note about three months ago. She said, “AJ, I just built a working FastAPI backend in like two hours and I have no idea how it happened.” I laughed it off. Then she sent me a screen recording. She wasn’t exaggerating.
That was my real introduction to Cursor. Not a press release, not a sponsored tweet — a confused-but-delighted non-developer building actual working software and not fully understanding why it was going so smoothly. I’d heard the name before, seen it trending on dev Twitter, skimmed a few comparison posts. But that recording made me actually sit down and use it seriously.
So I did. For the past three months, I’ve been running Cursor as my primary editor across three real projects: a React dashboard app, a Python REST API, and a gnarly multi-file refactoring job on an older codebase. Here’s what I actually found — not the marketing pitch, the reality.
What Cursor Actually Is (And What It’s Not)
Let me clear something up right away, because a lot of reviews muddy this. Cursor is not a plugin. It’s not an extension you bolt onto VS Code. It’s a fork of VS Code — meaning it looks almost identical, supports the same extensions, uses the same keyboard shortcuts, and imports your existing settings in about 30 seconds. If you’re already a VS Code user, the migration friction is basically zero.
What Cursor adds on top of VS Code is a deeply integrated AI layer that goes well beyond autocomplete. We’re talking about a system that understands your entire codebase — not just the file you have open — and can operate across multiple files simultaneously. That’s the key differentiator, and it’s what makes the comparison to GitHub Copilot more interesting than most people expect.
Cursor’s official site describes it as “the AI-first code editor,” and for once, that’s not just marketing fluff. The AI isn’t a feature here — it’s the whole architecture. Everything from how context is gathered to how responses are generated has been designed around AI-native workflows, rather than retrofitting AI onto a traditional editor.
The team behind it — Anysphere — has been iterating fast. The version I tested (in mid-2025) feels significantly more polished than what people were talking about even six months ago. The rough edges are mostly gone. What’s left is a tool that, for the right kind of developer, genuinely changes the pace of work.
Composer and Agent Mode: What They Actually Do
This is where most reviews either get too technical or stay too vague. Let me be specific about what these modes actually feel like to use.
Composer Mode
Composer is Cursor’s multi-file editing interface. You open it with Cmd+I (or Ctrl+I on Windows), type what you want to build or change, and Cursor generates edits across however many files are relevant — showing you a diff before applying anything. It’s less like autocomplete and more like pair programming with someone who can type 200 words per minute and never complains about refactoring.
I tested this on my React dashboard project. I typed: “Add a dark mode toggle to the navbar. Store the preference in localStorage and apply the class to the root element.” Cursor identified four files that needed changes — the Navbar component, a custom hooks file, the root App component, and a CSS module — and generated all the edits simultaneously. The whole thing took about 8 seconds to generate. It got it right on the first try, minus one small CSS variable name that didn’t match my existing convention. I fixed that in 10 seconds.
That’s the experience in a nutshell: not magic, but genuinely fast and usually correct enough that you’re editing rather than rewriting.
Agent Mode
Agent mode is a step further. Instead of you describing what to change, Agent can autonomously plan and execute multi-step tasks — running terminal commands, reading error output, fixing the error, running tests, and iterating. It’s the closest thing to “delegate this task and come back later” that I’ve seen in a code editor.
I gave it a real test: “Set up a pytest testing suite for this FastAPI project, write tests for the three main endpoints, and make sure they all pass.” It took about four minutes. It installed the missing dependencies, created the test file, wrote fifteen tests, ran them, caught two that were failing due to a fixture issue it had created, fixed them, and ran them again. All passing. I watched it work like I was watching a very fast intern who actually knows what they’re doing.
It’s not flawless — more on that later — but Agent mode is genuinely impressive for the kind of setup and scaffolding work that developers universally hate spending time on.
Codebase Context: Where Cursor Pulls Ahead
Here’s the thing that doesn’t get talked about enough in AI coding tool comparisons: context is everything. An AI suggestion that doesn’t know how your project is structured is just a fancy autocomplete. An AI that understands your entire repo is something else.
Cursor uses what it calls codebase indexing. When you open a project, it indexes the whole thing — your file structure, imports, function signatures, type definitions, existing patterns — and keeps that context available when generating suggestions. You can also use @codebase references in chat to explicitly pull in relevant files, or use @docs to reference external documentation directly.
I compared this directly against GitHub Copilot on the same React project. With Copilot, when I asked it to generate a new component that matched my existing patterns, it gave me something generic — correct JavaScript, but not styled with my CSS module conventions, not using my custom hooks, not following the component structure I’d established. With Cursor, the same request produced a component that looked like it belonged in my codebase. It used the right hook patterns, the right file structure, even the same prop naming conventions I’d been using throughout.
That’s not a small difference. That’s the difference between AI-generated code you have to rewrite and AI-generated code you actually ship. I’ve written more about how different AI models approach context in my Claude 3.5 Sonnet vs GPT-4o comparison — the same principles apply here, but Cursor operationalizes them at the editor level in a way that standalone AI assistants simply can’t.
Real Project Testing: The Honest Numbers
I kept rough notes across three projects. Here’s what I actually observed, without inflating the results.
React Dashboard App
Over about six weeks of active development, I used Cursor for probably 80% of my coding time on this project. My rough estimate: Composer suggestions required no editing about 45% of the time. Another 35% needed minor tweaks — a variable name, a missing import, a small logic adjustment. About 20% were either wrong enough that I rewrote from scratch or the approach was so different from what I wanted that it wasn’t worth editing.
The wins were mostly in repetitive structural work: creating new pages that followed existing patterns, wiring up state management, writing prop types. The losses were mostly in complex business logic with lots of conditional edge cases — which, honestly, is where any AI model struggles.
Python FastAPI Project
This was where Cursor impressed me most. Python with a clear framework like FastAPI gives the AI a lot of structural context to work with, and it shows. Endpoint generation, schema definitions, middleware setup — Cursor handled these with a hit rate I’d estimate around 60% first-try, no edits. The Agent mode test I described earlier was part of this project.
One specific win: I asked it to add rate limiting to my API using the slowapi library, which I hadn’t used before. It generated the correct implementation, including the proper decorator syntax and the middleware setup, without me needing to look at the docs at all. Generating the full setup took about 6 seconds. That’s time I genuinely got back.
Multi-File Refactoring
This was the hardest test. I had an older Node.js codebase — about 15,000 lines — where I needed to migrate from callbacks to async/await across multiple files. The kind of job that’s tedious, error-prone, and takes forever manually.
Cursor’s Agent mode handled it reasonably well for simpler functions, but it struggled with deeply nested callback chains and some older Promise patterns. I’d estimate it got about 70% of the migration correct autonomously. The remaining 30% required careful manual review and edits. Still, that’s a meaningful time saving on a job that would have taken me a full day manually. It took me maybe three hours with Cursor doing the heavy lifting.
Speed and Accuracy: The Real Talk
Speed is good. Generating a 300-word code block with Composer typically takes 5–10 seconds. Chat responses are similarly fast — comparable to what you’d get from Claude or GPT-4o in a browser interface, but with the advantage that results are applied directly to your code rather than pasted in.
Accuracy is where nuance matters. Cursor is not a replacement for understanding what you’re building. It’s more like a very capable collaborator who needs you to review their work. The developers who get the most out of it are the ones who can quickly scan generated code and spot what’s off — not the ones hoping to skip the understanding step entirely.
One thing I noticed: Cursor is particularly strong on well-documented, popular frameworks (React, FastAPI, Express, Next.js). It gets shakier with niche libraries, older codebases with unconventional patterns, or highly domain-specific logic. This makes sense — the underlying models are trained on what’s out there, and popular frameworks have more training signal.
The model selection matters too. Cursor lets you choose between Claude 3.5 Sonnet, GPT-4o, and other models depending on your plan. In my testing, Claude 3.5 Sonnet consistently produced the most coherent multi-file edits. If you want to understand why I rate that model highly for coding tasks, my Claude 3.5 Sonnet vs GPT-4o breakdown goes into detail on the reasoning capabilities that make the difference.
Pricing and the Free Tier: What You Actually Get
Let’s talk money, because this matters for whether Cursor makes sense for you.
The free tier (Hobby plan) gives you 2,000 completions per month and 50 “slow” premium requests. The completions are the inline tab suggestions — for light use, 2,000 is decent. The 50 premium requests for Composer and chat go fast if you’re using it seriously. You’ll probably hit the ceiling within a week of real usage.
The Pro plan is $20/month. This gets you unlimited completions and 500 fast premium requests per month, with additional slow requests available after that. For a professional developer using Cursor as a primary tool, this is the tier that actually makes sense. Twenty dollars a month is nothing if it saves you even a couple of hours of work.
The Business plan runs $40/user/month and adds team features, privacy controls (no code stored on servers), and SSO. For any team working with proprietary or sensitive code, this is probably the minimum acceptable tier from a compliance standpoint.
One thing worth noting: the request limits can feel restrictive if you’re running Agent mode heavily, since complex multi-step tasks can burn through several requests in one session. I hit my 500-request monthly limit around week three of heavy usage, which then throttled me to “slow” mode. Not a dealbreaker, but worth budgeting for if you plan to use Agent mode aggressively.
For a fuller picture of what’s available across AI coding and productivity tools at different price points, the AI Tools Starter Pack is a good reference point.
Cursor vs GitHub Copilot: The Honest Comparison
I’ve used GitHub Copilot for over two years. It’s good. It’s fine. It does what it says. But after three months with Cursor, going back to Copilot feels like going back to a flip phone.
The core difference isn’t the quality of individual suggestions — both use strong underlying models and both produce reasonable code. The difference is scope. Copilot works at the line or function level. Cursor works at the project level. Copilot completes what you’re typing. Cursor can plan, build, and execute across your entire codebase.
For someone who writes simple scripts, works in a single file at a time, or mostly needs autocomplete help, Copilot is cheaper ($10/month) and does the job without the overhead. For someone building real applications with multiple components, modules, and files that all need to work together, Cursor isn’t even a close competition.
The other notable difference: Cursor’s chat is genuinely useful for debugging and architectural questions in a way that Copilot’s inline suggestions aren’t designed for. Being able to select a chunk of code and ask “why is this causing a memory leak?” — and get a specific, contextual answer — is a workflow that Copilot simply doesn’t offer in the same integrated way.
What Cursor Gets Wrong
No tool is perfect, and I’d be doing you a disservice if I didn’t flag the real frustrations.
Context window limits bite. On very large files or codebases, Cursor can lose track of earlier context and produce suggestions that contradict things it generated earlier in the same session. This is more of a model limitation than a Cursor-specific problem, but it’s real.
Agent mode can go off the rails. I had one session where Agent mode decided to restructure my project’s folder layout in a way I hadn’t asked for and hadn’t approved — it just did it as part of a broader task. The changes were technically reasonable, but they weren’t what I wanted. Always review Agent mode’s planned changes before confirming.
The tab completion can be aggressive. Sometimes Cursor’s inline suggestions auto-complete in a direction you didn’t intend, and if you’re not paying attention, you accept something that’s subtly wrong. This is a habit adjustment more than a flaw, but it catches people off guard early on.
Performance on large codebases varies. Indexing a 100,000-line repo takes time, and on older machines, you’ll notice the RAM usage. Not a dealbreaker, but worth knowing if you’re on a less powerful setup.
Who Should Switch to Cursor (And Who Shouldn’t)
I’ll be direct about this, because vague “it depends” takes help nobody.
Switch to Cursor if: You’re building real applications — web apps, APIs, internal tools — where multiple files need to work together. You’re a mid-level or senior developer who can review and guide AI output. You do frequent refactoring or greenfield development. You’re already on VS Code and comfortable with AI-assisted workflows. You’re spending meaningful time on boilerplate and setup work that you’d rather automate.
Stay where you are if: You primarily write scripts or single-file programs where Copilot’s inline suggestions are sufficient. You’re on a team with strict data security requirements and can’t use the Business plan. You’re early in your coding journey and need to actually understand what you’re writing — Cursor’s power can become a crutch that slows learning if you’re not careful. You work primarily in languages or frameworks that are less well-represented in training data.
One more honest note: Cursor is a productivity multiplier, not a skill replacement. The developers I’ve seen struggle with it are the ones who expect it to substitute for understanding. The ones who love it are the ones using it to move faster on things they already know how to build.
Final Verdict
Three months in, Cursor is my primary editor. That’s not something I say lightly — I’ve been on VS Code for years and have tried probably a dozen AI coding tools. Most of them are incremental improvements. Cursor is a different category.
Is it perfect? No. The request limits are real, Agent mode needs supervision, and it won’t save you if your fundamental approach to a problem is wrong. But for the kind of development work I do — building and shipping real applications — it has genuinely changed my pace. Things that used to take a day take a few hours. Setup and scaffolding work that I used to dread is now something I hand off to Agent mode while I focus on the interesting parts.
At $20/month for Pro, it’s priced at a level where almost any professional developer should be able to justify it based on time saved in the first week alone. If you’re on the fence, start with the free tier for a few days on a real project — not a toy project, a real one. You’ll know pretty quickly whether this changes how you work.
For reference, Cursor’s full pricing breakdown is straightforward and worth reading before you commit. And if you’re evaluating AI tools more broadly and want to understand how the underlying model choices affect output quality, my GPT-5 Breakdown covers some of the model-level dynamics that affect tools like Cursor too.
My honest recommendation: if you’re a developer building anything more complex than scripts, try Cursor. Actually try it — on real code, for at least a week. I’d be surprised if you go back.
Frequently Asked Questions
Is Cursor safe to use with proprietary code?
On the free and Pro plans, Cursor may use your code to improve its models unless you opt out in settings. The Business plan ($40/user/month) offers a privacy mode where your code is not stored or used for training. If you’re working with proprietary or sensitive code professionally, the Business plan is the appropriate tier.
Can I use my own API keys with Cursor?
Yes. Cursor allows you to bring your own API keys for OpenAI, Anthropic, and other providers. This can be a cost-effective option if you already have API access and want more control over model selection and usage limits.
Does Cursor work with all programming languages?
It works with any language VS Code supports, which is essentially everything. That said, quality of AI suggestions varies by language popularity. JavaScript/TypeScript, Python, and Go get the best results. More niche languages will work, but you’ll see lower accuracy on AI suggestions.
How is Cursor different from just using Claude or GPT-4o in a browser?
The key difference is integration and context. A browser-based AI assistant sees only what you paste into it. Cursor has indexed your entire codebase, understands your file structure and patterns, and can directly edit files — not just suggest text. The workflow difference is significant in practice.
Is there a student or free plan worth using?
The Hobby (free) plan is worth using to evaluate Cursor, but the 50 premium request limit will constrain you quickly if you’re using Composer or Agent mode. For serious evaluation, I’d recommend starting the Pro trial — it gives you a much more representative sense of what the tool actually delivers.
Last updated: 2025