Cursor AI vs. Copilot: 90 Days of Real Testing

Friends at meetups kept asking me the same thing. Coworkers in our Toronto office wanted to know too: which one's actually better for coding? After months of giving wishy-washy answers, I finally ran a proper test. Not some weekend experiment, but a real trial inside live repos, feature branches, regression tests, and those 2 a.m. bug hunts we all know too well.

Ninety days. Both tools. Same codebases, same deadlines, same ML engineer who drinks way too much chai at 2 a.m. (that's me). Across nearly 100 pull requests, I tracked acceptance rates, logged every auto-generated bug, and measured how much time each tool actually saved.

Here's what I found.

Developers love arguing about tools. Guilty as charged. My computer vision work at a startup means speed matters, and I want gear that moves, not hype that stalls. Writing tooling for internal teams also means I'm usually the one evaluating new AI assistants anyway.

The goal here is simple. You're wondering which tool wins? I'll walk through my exact setup, the metrics I collected, and the moments each tool made me smile or swear. You'll see performance across accuracy, multi-file changes, context handling, and real productivity. Plus, I'll break down how pricing shakes out when you factor in actual output instead of marketing slides.

The Testing Setup

Eliminating variables was the priority. Both tools worked inside their natural environments: VS Code for Copilot, the Cursor editor for its own. Every test ran on production code from my startup plus two side projects. One was a chess puzzle generator using CNNs for board state detection. The other was a minimal photo tagging app.

Fairness meant running tasks in pairs. Needed to implement a data loader for a new dataset? Cursor got first crack, then I'd roll back and ask Copilot to try the same thing. Or vice versa. Weekly alternation ensured neither tool got the advantage of seeing fresher context.

My controls:

• Same developer (me), same skill level, obviously
• Same repositories: Python, TypeScript, Rust
• Same prompts, copied verbatim across tools
• Same evaluation process: duration, fixes needed, retry count
• Same PR template for clean metric collection

Chat banter quality? Didn't test it. Code generation, refactoring, context reading, debugging. That's what matters when you're shipping production systems.

Code Completion Accuracy

Copilot seemed like the obvious winner here. It's been around longer and usually nails short completions. But things got interesting once I separated acceptance rate from actual usability.

Acceptance rate: how often I hit tab and used the suggestion. Usability: how often that suggestion survived in the final diff without heavy edits.

My rough numbers across PRs:

• Copilot acceptance rate: around 63%
• Cursor acceptance rate: around 54%
• Copilot usable code rate: around 41%
• Cursor usable code rate: around 47%

These are personal observations from this specific experiment, not official statistics. Your mileage will vary based on codebase, language, and workflow.

So Copilot won the raw tab count. But Cursor produced more code I didn't need to rewrite later. Honestly? That surprised me. Cursor seemed better at threading context from earlier edits, especially in TypeScript backend code. Copilot felt stronger in boilerplate-heavy Python tasks.

This aligns with a lot of the accuracy discussions I've seen online, but watching it play out in my own diffs made it real.

Multi-File Operations

Need to do sweeping refactors? Cursor Composer absolutely destroys Copilot Chat. No polite way to say it. Each tool got five multi-file tasks:

• Replace an internal image preprocessing API
• Migrate a chess evaluation script from NumPy to PyTorch
• Rename a core class in a TypeScript service and update every import
• Remove dead feature flags across the repo
• Convert a folder of utility functions into a shared module

Copilot Chat struggled. Suggestions came through, but clean application? Never happened. Finishing the job myself meant rewriting half the patch sometimes.

Cursor Composer applied consistent changes across file boundaries, updated imports, and respected project structure. Mistakes still happened occasionally, but the success rate was dramatically higher.

This is why teams start looking elsewhere. And honestly, I get it now.

Context Understanding

Large codebases break AI assistants. My startup runs a weird mix of Python, C++, and Rust modules tied together by gRPC and homegrown tooling. Things get messy fast.

Long context chains? Cursor handled them better. Asking it to update a function used in a path five files deep usually meant it traced dependencies correctly. Copilot tended to focus on the current file. Worst case? Hallucinated method names that never existed. (Ever watched an AI confidently suggest a function that doesn't appear anywhere in your codebase? You're in for a treat.)

My tiny photo tagging side project showed both tools performing solidly. The difference only emerges once your repo grows enough that you start dreading grep commands.

Working in multi-language monorepos? Context understanding becomes the deciding factor.

Pricing Reality Check

Opinions get spicy here. Cursor costs more on paper. But after 90 days, the real question isn't subscription cost. It's how much time each tool saves you.

My experience:

• Copilot saved roughly 15 to 25 minutes per workday
• Cursor saved roughly 35 to 55 minutes per workday

Not scientific, but I tracked start and stop times on each PR using a Notion board. Cursor's advantage came from multi-file operations and cleaner refactors. Copilot excelled at quick completions but struggled with repo-wide work.

Worth it for full-stack developers? Working across backend, frontend, DevOps scripts, and frequent refactors makes the answer yes. Mostly writing isolated functions or short scripts? Copilot is cheaper and perfectly fine.

The Verdict

After three months of real usage, this is where I land.

Junior developers: Copilot feels easier to start with. Suggestions seem more natural for small tasks. Cursor can overwhelm you with too much power at once.

Mid-level and senior developers: Cursor wins. No hesitation. Multi-file edits, context management, and Composer are just too strong.

Tech leads managing large teams: Mixed environments make sense. Some developers do fine on Copilot, but your power users will beg for Cursor. This matches what I see in discussions about enterprise tooling and teams exploring alternatives.

Ecosystem compatibility matters to you? Copilot integrates cleanly with GitHub and VS Code. Cursor keeps improving, but GitHub has the lock-in advantage.

Quick recommendation matrix for anyone still deciding:

• Raw code completion speed: pick Copilot
• Repo-wide changes: pick Cursor
• Tooling for an entire engineering org: test both
• Switching from Copilot and wondering if Cursor's worth it: try a two-week trial, use Composer heavily, then check your git diffs

Want to run your own test? Keep it short. Two weeks works if you alternate tasks, track acceptance rate, and count rewrites on AI-generated code.

And if you came here wondering which one wins? Real talk: the more complex your workflow, the more Cursor pulls ahead.

Share your own findings. I'm always curious what other engineers see in the wild.