GitHub Copilot is the most widely adopted AI coding tool on the planet. Built by GitHub (a Microsoft company), it works as an extension inside VS Code, JetBrains IDEs, Neovim, Xcode and more — without requiring you to switch editors. 76% of developers worldwide have heard of it. 29% use it at work. Free tier available. Pro is $10 per month.
GitHub Copilot is an AI assistant for writing code. It installs as an extension inside the code editor you already use — there is no new application to download or learn. Once installed, it watches as you type and offers suggestions for what comes next: the next line, the next function, the entire implementation of something you described in a comment.
GitHub built Copilot in partnership with OpenAI, trained on billions of lines of public code from GitHub repositories. It was the first commercially successful AI coding tool, launched in 2022. As of 2026 it has expanded from a single autocomplete feature into a full AI development platform that covers completions, chat, code review, autonomous agent mode and issue-to-PR automation.
The defining characteristic of Copilot, compared to alternatives like Cursor, is where it lives. Copilot is a plugin — it exists inside your existing editor rather than replacing it. If you use VS Code, JetBrains (IntelliJ, PyCharm, WebStorm, GoLand, PhpStorm), Neovim, Visual Studio, Xcode or Eclipse, Copilot works inside that tool. You do not change your environment.
Inline completions — As you type, ghost text appears suggesting how to complete the line or implement the function you started. Press Tab to accept, keep typing to ignore. This is the original Copilot feature and it remains the most used. According to independent reviews in 2026, Copilot's completion speed is among the fastest available and is particularly strong on JavaScript, Python, TypeScript, Go and Rust.
Copilot Chat — A conversation panel where you ask questions in plain English: explain this function, find bugs in this file, write tests for this class. Paid plans let you choose which AI model answers — GPT-5.4, Claude Opus 4.6, Gemini, or others. Chat interactions consume premium requests from your monthly allowance.
Agent mode — Copilot plans and executes multi-step coding tasks autonomously. You describe what you want ("add validation to all form inputs", "refactor this service to use dependency injection") and Copilot decides which files to open, makes changes across multiple files, runs terminal commands and iterates until the task is complete. Agent mode became generally available in both VS Code and JetBrains in March 2026, per GitHub's official documentation.
Coding agent (issue to PR) — Assign a GitHub issue to Copilot and it works in the background, writing code in a sandboxed environment, running tests and opening a pull request for your review. This is the most autonomous feature Copilot offers — a complete issue-to-code workflow without human intervention at each step.
Code review — Copilot analyses pull requests and provides feedback. The March 2026 agentic architecture gathers full project context before analysing, making suggestions that account for how changes relate to the broader codebase. When code review identifies issues, it can pass them to the coding agent to generate a fix PR automatically.
Copilot's strongest case is for developers who do not want to leave their existing editor. If you have invested years into a JetBrains IDE, have hundreds of plugins configured, and know every keyboard shortcut — switching to Cursor is a real cost. Copilot installs in five minutes and your environment is unchanged.
It is also the default recommendation for developers who work primarily in JavaScript, TypeScript or Python — languages with the strongest training data representation and therefore the highest quality suggestions.
For teams already on GitHub, the coding agent and PR-level integration is uniquely valuable. No competing tool has the same depth of native integration with GitHub's issue tracker, pull request workflow and code review process.
Students and educators get GitHub Copilot Pro free through GitHub Education. Open source maintainers of popular repositories are also eligible for free Pro access.
Multi-file refactoring — changing the structure of a large codebase across many files simultaneously — is where Copilot falls behind Cursor and Claude Code in 2026. Multiple independent reviews note that Copilot's agent mode works well for contained tasks but is less reliable on complex architectural changes. Cursor's Composer 2 and Claude Code handle this more consistently.
Copilot also cannot match Cursor's codebase indexing. Cursor embeds your entire project into a searchable vector database. Copilot's context window is improving but does not have the same depth of whole-project understanding, according to the JetBrains developer survey of January 2026.
ChatGPT lives in a browser tab. You describe what you want, copy the code it generates, switch to your editor, find the right place, paste it in, adjust imports and variable names, and check it compiles. Every change is a manual round trip.
Copilot is inside your editor. It reads the file you have open, understands the code around your cursor, and completes your thought in place. No switching tabs, no copying, no pasting. The suggestion appears where your cursor is. Accepting it is one keystroke.
For code review and agent mode, the gap is even larger. ChatGPT cannot read your GitHub issues, review your pull requests, or open a PR to fix a bug you pointed at. Copilot can.
Yes. The Free tier provides 2,000 code completions and 50 premium requests per month with no credit card required. It works in VS Code and JetBrains IDEs. This is enough to genuinely evaluate whether Copilot improves your workflow, but not enough for professional daily use — 50 premium requests is roughly 2–3 chat interactions per working day.
Pro is $10 per month — the cheapest premium AI coding plan available in 2026. It provides unlimited completions and 300 premium requests per month. Students get Pro free through the GitHub Student Developer Pack. Verified open source maintainers are eligible for free access.
Open your editor's extension marketplace and search for "GitHub Copilot". In VS Code, go to Extensions (Ctrl+Shift+X), search "GitHub Copilot" and install the official extension. In JetBrains IDEs, go to Settings → Plugins → Marketplace and search "GitHub Copilot". Restart your IDE after installation.
Copilot will prompt you to sign in to your GitHub account. If you do not have an account, create one at github.com — it is free. Once signed in, you will be on the Free tier automatically. No credit card required.
Open any code file. Start typing a function name or leave a comment describing what you want to build. Ghost text will appear showing Copilot's suggestion. Press Tab to accept. Press Escape to dismiss. Press Alt+] (Option+] on Mac) to see the next suggestion. You will see the difference immediately.
In VS Code, click the chat icon in the left sidebar or press Ctrl+Alt+I. In JetBrains, look for the Copilot icon in the right sidebar. Ask a question about your code. Try: "Explain what this file does" or "What are the potential bugs in this function?" The chat has context of your open file.
Select a block of code. Press Ctrl+I (Cmd+I on Mac). Type an instruction — "add error handling", "add comments", "optimise this loop". Copilot makes the change inline and shows a diff. Accept or discard.
In VS Code, open Copilot Chat and look for the "Agent" mode toggle at the bottom. Switch to Agent mode. Describe a multi-step task — "Add input validation to all the form fields in this component". Watch it plan the changes, edit files and confirm its own output. This is where Copilot's real power shows.
Use the # symbol to focus context. In the chat panel, type # to reference specific files, symbols or GitHub issues. #filename gives Copilot the full context of that file. #function-name finds the definition. #issue-123 pulls in a GitHub issue. The more focused your context, the better the output.
Premium requests versus completions. Tab completions (the inline suggestions) do not use premium requests on paid plans — they are unlimited. Premium requests are spent on Chat interactions, Agent mode steps and model selection. Understanding this distinction helps you stay within your monthly allowance. Use Chat for complex questions, rely on Tab completion for routine code.
Switch models for different tasks. On Pro and above, you can choose which AI model responds in Chat. Claude Opus 4.6 is generally better for complex reasoning and nuanced code review. GPT-5.4 is strong for general coding. Gemini is good for long-context analysis. Switching costs one click and zero extra money (up to your request allowance).
Copilot Edits for multi-file changes. In VS Code, Copilot Edits (separate from the Chat panel) is optimised specifically for making changes across multiple files at once. If your task involves editing more than one file, use Copilot Edits rather than Chat for better results.
Save your prompt history. Useful prompts can be saved as Copilot prompt files — reusable prompts your team can share. Go to Settings → Copilot → Prompts to manage these. Useful for standardising how your team asks for tests, reviews or documentation.
This is the question most developers ask first. Here is the honest answer:
Copilot is better if: you do not want to switch editors, you work in JetBrains, you are cost-sensitive ($10 versus $20 per month), or your workflow is centred on GitHub pull requests and issues.
Cursor is better if: you want the strongest multi-file editing and agent mode available, you want a whole-codebase context that Copilot does not match, and you are comfortable switching to a new editor.
The practical choice for most developers: start with Copilot. It is cheaper, installs in your existing environment, and covers the majority of what most developers need from an AI coding tool. Upgrade to Cursor if you find yourself regularly hitting Copilot's limits on complex, multi-file tasks.
GitHub Copilot was originally built on OpenAI Codex, a model descended from GPT-3 fine-tuned on code. Codex was described in the paper "Evaluating Large Language Models Trained on Code" by Chen et al. (2021), available on arXiv at arxiv.org/abs/2106.09685. As of 2026, Copilot uses multiple models including OpenAI and Anthropic models, user-selectable per request on paid plans.
GitHub is a subsidiary of Microsoft, which has a significant investment in OpenAI. This relationship gives Copilot early access to OpenAI model releases and integration with Microsoft's enterprise security and compliance infrastructure.
Copilot was trained on public repositories on GitHub. According to GitHub's official FAQ, the quality of suggestions varies by programming language based on the volume and diversity of training data. JavaScript, Python, TypeScript and Go are well-represented. Less common languages produce lower-quality suggestions.
For enterprise customers, GitHub provides a contractual guarantee that private organisation code is not used to train models when Copilot Business or Enterprise is configured with the appropriate privacy settings. This is documented in GitHub's official pricing and enterprise documentation.
GitHub's official documentation describes two categories of usage. Code completions — the Tab autocomplete feature — are unlimited on all paid plans. Premium requests power Chat, Agent mode, code review and model selection. Each plan includes a monthly allocation:
Additional requests beyond the allocation cost $0.04 each, billed automatically. Agent mode sessions consume multiple premium requests per task, as multiple model calls are made during execution.
According to GitHub's official documentation as of April 2026:
Copilot Spaces, available on Business and Enterprise plans, allows teams to create persistent knowledge bases that Copilot can reference when answering questions. These can include internal documentation, architecture decision records, API references and codebase-specific context. This feature addresses the limitation of Copilot's context window by providing persistent, retrievable knowledge separate from the model's training data.