Copilot vs ChatGPT for Coding
Copilot and ChatGPT don't do the same thing when coding. We explain when to use each, their limits, and how teams use them.
What are the differences between these two AIs for coding?
In real-world code development, the question is no longer whether to use AI or not. The key issue is which tool fits best in each part of the workflow. GitHub Copilot and ChatGPT (especially when used with Codex in the IDE) can generate and transform code, but their value shows when you understand how they work and what types of problems they solve best.
Copilot lives inside the editor and boosts your speed when you're already coding. ChatGPT/Codex tends to perform better when you need to analyze, plan, debug carefully, or delegate tasks with more autonomy and follow-up, including workflows that combine IDE and cloud.
Two different approaches to writing code with AI
The useful comparison isn't which one is better in the abstract. It's what approach each one represents.
Copilot is designed to accompany you while you type: inline suggestions, chat in the IDE, and agent and coding agent capabilities depending on plans and supported environments.
ChatGPT is a generalist conversational environment, but when you bring it consistently to programming, the typical approach is to connect it to real work via Codex in the IDE, which can read, edit, and execute code, and also delegate tasks to Codex Cloud from the editor.
What is GitHub Copilot and how does it work inside the editor
Copilot offers inline suggestions and Copilot Chat in compatible editors and IDEs, and also includes a coding agent that can create changes and open PRs for review within the GitHub workflow.
In its individual plans, GitHub details that chat, agent mode, code review, coding agent, and Copilot CLI consume premium requests, with limits depending on the plan, and the option to purchase additional requests.
What is ChatGPT and how is it used for coding
With the Codex IDE extension, you can use Codex side by side in the editor or delegate tasks to Codex Cloud. OpenAI defines it as a coding agent capable of reading, editing, and executing code.
In operational security, the Codex documentation describes a secure-by-default approach: sandboxed permissions, no network access by default in certain modes, and configuration for approval policies based on risk level. Additionally, Codex includes a web search tool that, for local tasks, can serve results from a cache to reduce exposure to prompt injection from arbitrary live content.
Copilot vs ChatGPT for code
This table is designed to answer what matters in a team: what reduces the most time without increasing risk (bugs, tech debt, security, PR friction).
| Criteria | GitHub Copilot | ChatGPT / Codex |
|---|---|---|
| Workflow integration | Strong inside the IDE: inline suggestions and chat; plus coding agent and PR workflow in GitHub depending on plan. | Codex IDE extension works attached to the repo from VS Code and compatible editors. Allows delegating tasks to Codex Cloud from the editor. |
| Most common type of help | Speeding up writing and mechanical changes while coding. Support for agent mode, code review, and coding agent depending on plan. | Analysis, design, guided debugging, and task delegation with local/cloud continuity. |
| Project context | Relies on the context available in the editor and files. Its performance depends heavily on what it sees and how the repo is structured. | Generic chat doesn't see your repo unless you provide it. With Codex in the IDE, it does work on the workspace, editing and executing. |
| Typical risks | Can generate code that looks valid but isn't, or doesn't reflect your intent. GitHub recommends reviewing and testing. | Can hallucinate or fill gaps with confidence. Best to anchor everything to execution and tests. |
| Control signals | GitHub documents references when a suggestion matches public code, depending on configuration. | Codex documents sandbox, approvals, and network options. Useful for adding guardrails when delegating actions. |
Where each one fails
Copilot fails in a very specific way: it amplifies what already exists around it. If the repo has bad patterns or vulnerabilities, it can replicate them because it takes context from nearby files and normalizes it into suggestions. On top of that, there's the public code similarity issue: GitHub documents how to view references when a suggestion matches publicly available code. That's a useful mechanism, but it doesn't replace an internal PR and review policy.
ChatGPT/Codex tends to fail differently: not so much with an isolated insecure line, but with convincing narrative. When a model isn't sure, it can complete with a response that sounds good, and in debugging or architecture that costs dearly if you don't anchor it to reproduction, logs, tests, and execution.
How do teams that already code with AI combine them?
The pattern that usually works best in an intermediate team is to split roles.
Copilot is used as a flow engine: completions and micro-refactors in the editor, plus chat/agent mode and code review depending on plan and needs.
ChatGPT/Codex is used as a judgment and delegation engine: clarifying intent, preparing a refactor plan, generating tests first, debugging with hypotheses and verification, and delegating tasks to the agent when it makes sense.
- When Copilot usually makes more sense: when the task is well-defined and the cost of being wrong is low, and you want speed inside the editor.
- When ChatGPT/Codex usually makes more sense: when you need to think before writing or coordinate broader changes, and you want execution guardrails and permissions to delegate with less risk.
Frequently asked questions
Is Copilot better than ChatGPT for coding?
There's no overall winner. Copilot tends to win when you're already inside the editor and looking for speed in writing and mechanical changes. ChatGPT/Codex tends to win when you need analysis, design, guided debugging, or task delegation with context continuity.
Can I use ChatGPT like Copilot inside the IDE?
Yes, if you use it through the Codex IDE extension. OpenAI positions it precisely as a companion in the editor and as a way to delegate tasks to Codex Cloud from the IDE.
Which one makes more mistakes when generating code?
Both can make mistakes, but they fail differently. GitHub acknowledges as a limitation that Copilot Chat can generate code that looks valid but isn't, and recommends reviewing and testing. OpenAI explains that models can hallucinate and respond with confidence when they're not sure, so you also need verification.
What should I look at if I want to use this in a company?
IDE/PR integration, usage limits and plans, review policy, and risk controls: public code match references in Copilot, and sandbox/approvals/network in Codex when delegating tasks.
Which AI is better for coding if you don't know how to code?
If you don't know how to code, the most important thing isn't that the AI writes code, but that you can understand what you're accepting. In that scenario, ChatGPT/Codex usually fits better as a tutor: asking for explanations, breaking down tasks, building a plan, and validating with examples. Copilot starts to shine when you're already comfortable in an editor and can review, test, and correct what it suggests.