Cursor vs. GitHub Copilot

The AI-powered IDE revolution#

AI code assistants have shifted from autocomplete toys to serious developer companions. Both Cursor and Copilot aim to:

• Autocomplete code based on natural language prompts.

• Understand your project context and codebase.

• Refactor, explain, and debug your code.

• Act like a helpful pair programmer inside your IDE.

IDE immediately pinpoint the issue and clearly explain how to fix it, just like having a senior engineer sitting next to you.

That’s the promise AI-powered coding assistants like Cursor and GitHub Copilot are making today. Both tools claim they can help you code faster, improve quality, and simplify development.

But when faced with an empty editor and a real-world coding challenge, which tool delivers on those promises? I decided to answer that question myself, not by trusting marketing claims or staged demos, but by building something meaningful.

Cursor vs GitHub Copilot: A quick comparison#

Before we dive into the differences between the two based on my project, let's take a quick look at the

Both powerful. But very different personalities.

Setup and installation#

• GitHub Copilot: Dead simple. Install the extension inside VS Code, log into GitHub, and you’re coding within minutes.

• Cursor: A separate app based on VS Code core, designed around AI-first workflows. Installation took slightly longer, but it felt polished and smooth.

Cursor instantly felt like a full IDE purpose-built for AI coding, while Copilot slipped into my existing VS Code like a natural extension.

First impressions#

Both tools felt familiar, resembling modern, minimal code editors, but they subtly shape the experience:

• Cursor: This is a clean interface with AI naturally integrated into the workspace. The chat panel is typically docked on the side but can be detached or repositioned according to your workflow preferences. It is ready for questions, code explanations, or multi-file refactors. Inline suggestions appear as you type, keeping the AI actively available for deeper interaction whenever needed.

• Copilot: Integrated directly into VS Code, Copilot offers inline suggestions as subtle ghost text as you type, with seamless recommendations triggered by comments. Activating Copilot Agent creates a more prominent interface, opening step-by-step diffs and terminal panels. The chat panel remains tucked away until opened explicitly.

While both aim for an unobtrusive experience, their approaches differ notably. Cursor surfaces AI more visibly from the outset, integrating naturally into your workflow. In contrast, GitHub Copilot starts minimally but can become more prominent, opening diff views and terminal panels,  primarily once you activate Copilot Agent. Cursor maintains a consistent AI presence, while Copilot transitions from subtle inline suggestions to a more interactive environment as needed.

Must-know shortcut keys#

Before I show you what I built using these tools, it’ll help if you see some useful Cursor and Copilot shortcuts.

The following are Cursor’s shortcut keys:

The following are GitHub Copilot’s shortcut keys:

Note: Keybindings often reflect what the user selects during their initial setup.

The project: Building a personal budget tracker#

To give both tools a fair challenge, I chose a project I’d wanted to build for a while—a personal budget tracker. It’s a practical, logic-based application that represents the kind of real-world coding tasks developers regularly tackle. I used the free tier for both Cursor and GitHub Copilot.

Goal:

• Track income and expenses.

• Warn when you’re overspending.

• Add visual feedback with charts.

Let’s get started!

Note

• For Cursor, we used claude-4-sonnet agent.

• For GitHub Copilot, we used a 4.1 GPT agent.

Step 1: Create basic functionality#

To get things started, I wanted the AI to scaffold the initial structure, something I could immediately run and iterate on.

Prompt: "Create a simple personal budget tracker web app with the ability to add income and expenses."

Copilot: Instantly generated a basic HTML/CSS/JS setup with input forms, simple logic for adding transactions, and updating totals. Clean starter code. Minimal handholding required.

Cursor: Similar strong response, but went one step further: added modularized JavaScript files, separated logic into clearer functions, and even created initial folders for organization.

Both had runnable code that executed well from the start. Cursor’s structure felt a bit more maintainable right out of the gate.

Step 2: Add logic for budget warnings#

Next, I wanted the app to handle basic calculations and alert me whenever my spending crossed the budget limit.

Prompt: "Add logic to calculate remaining budget and warn if expenses exceed income."

Copilot: It effectively suggested inline updates. It calculated the remaining balance and displayed warnings if overspending occurred. However, it required me to guide it when handling edge cases (like deleting transactions).

Cursor: Not only implemented the warning logic, but also offered a few variants: one with color indicators, one with alerts, and one with both. When I challenged it further, Cursor explained its approach in detail.

Cursor felt more aware of my end goal. Copilot was lightning fast but sometimes needed nudges to grasp complex conditions fully.

Step 3: Add visual feedback with color indicators#

To make it easier to spot budgeting issues at a glance, I wanted the app to visually indicate the budget status.

Prompt: "Add color indicators: green if under budget, yellow if close, red if over budget."

Copilot: Added basic conditional logic for color-coding totals. The implementation worked, but was a bit simplistic.

Cursor: Suggested a more nuanced color scale, even recommended threshold percentages for a better user experience. Also updated the UI styling directly for better visual polish.

Step 4: Add a bar chart for expense categories#

With the core logic working, I wanted to visualize where the money was going, breaking expenses down by category.

Prompt: "Add a simple bar chart to visualize expenses by category."

Copilot: Suggested using Chart.js with correct setup, but needed my guidance to properly map data to categories.

Cursor: Proposed multiple charting libraries, defaulted to Chart.js, and automatically transformed my expense data into grouped category totals. The result? A functional, good-looking chart without much intervention.

After getting the charts up and running, the core functionality of my budget tracker was in place. Now it was time to clean up and polish the codebase.

Step 5: Refactor and clean up#

Finally, I wanted the AI to tidy up the code, making it cleaner, more modular, and easier to maintain.

Prompt: "Refactor the code to improve readability and modularity."

Copilot: Refactored functions into smaller chunks but in a single file. Solid, but sometimes over-eager with unnecessary abstractions.

Cursor: Delivered clean, maintainable code with excellent separation of concerns and functions into separate files with unique functionality. Even added helpful comments explaining changes.

My takeaways#

Note: Cursor and GitHub Copilot now solve approximately 56% of SWE-bench-Verified tasks, highlighting their strong debugging and refactoring capabilities.

Curious how Cursor and GitHub Copilot stand against Windsurf? Read on: Cursor vs. Windsurf vs. GitHub Copilot!

Final thoughts#

Both Cursor and Copilot are redefining how we write code. They serve different mindsets:

• If you want a quick autocomplete powerhouse to accelerate your typing, Copilot excels.

• Cursor shines if you’re looking for an AI partner who thinks along with you, helps structure your project, and reasons through more complex tasks.

For me? I leaned toward Cursor for new projects, where architecture and logic evolve. Copilot remains unbeatable for small scripts or when I’m simply moving fast.

The future is exciting. This is only the beginning of AI-powered development.