Best AI for coding.
Every assistant writes code. They differ in how much of it you can actually ship — and in how you have to talk to them.
Ask which AI is best at coding and you will get five confident, contradictory answers. The honest version is duller: the leading assistants are all genuinely good at writing code now, and on a self-contained function you would struggle to tell their output apart. The differences that matter show up elsewhere — how much of your codebase the model can hold at once, whether it lives in your editor or in a browser tab, and how it handles an existing project with its own conventions rather than a blank file. Here is what each tool is actually good at, and how to get code out of them you would merge.
ChatGPT
ChatGPT is the versatile all-rounder and, for a lot of developers, the default second opinion. It is strong at explaining unfamiliar code, translating between languages, writing one-off scripts, producing the regex you would rather not think about, and walking through a concept until it clicks. Because it is a general assistant rather than a coding-specific tool, it fits the messy half of programming: sketching an approach before you commit, summarising documentation you have not read, writing the shell command you need twice a year. If you want one thread for coding questions and everything else, this is it.
Claude
Claude's reputation among developers rests on long-context reasoning. You can hand it several large files, or a whole module, and ask for a careful refactor rather than a snippet. It tends to follow the conventions already in the code you give it rather than rewriting everything in its own style, and it takes well to being told to explain its reasoning before it changes anything. That suits work that is hard to do in small pieces — untangling a legacy file, migrating an API surface, reading a diff for consequences you missed. There is also a terminal-based agent, so the same model can work against a repository directly.
GitHub Copilot
Copilot's advantage is location. It lives in your editor and completes the line or block you are already typing, which makes it the least disruptive of these tools: you never leave the file, and you accept or ignore a suggestion in a keystroke. It shines on boilerplate and on code with an obvious next step — test scaffolding, repetitive mappers, the fourth handler that looks like the first three — and it reads the surrounding file, so suggestions inherit your naming. It is a weaker fit for architectural questions, where a chat interface you can argue with serves you better. It has a chat mode too, but inline completion is what it does best.
Cursor
Cursor is an editor built around AI rather than an editor with AI bolted on. Its distinguishing feature is multi-file work: point it at several files, describe the change in plain English, and review the result as a diff across the project instead of shuttling snippets between a browser tab and your code. It indexes the repository, so a question like where is this validated? is answered from your actual project. Because it is a VS Code fork, most extensions and keybindings carry over, keeping the switching cost low. If your friction is being the courier between a chat window and your files, this is the category that removes it.
Gemini
Gemini's pitch is scale and integration: a large context window, so you can hand over a lot of material at once, plus tight connections to the rest of Google's stack — Workspace, Android tooling and Google Cloud. If your work already lives there, or your problem involves more code, logs and documentation than you want to chunk up by hand, it is a natural pick. It is also wired into Google's own IDE and cloud tooling, so it need not be one more browser tab.
How to pick
Match the tool to the moment rather than crowning a winner:
- Learning a concept or a new framework — a chat assistant with room for follow-up questions: ChatGPT or Claude.
- Autocomplete while you type — Copilot, or Cursor's inline completion.
- Refactoring across many files — Cursor, or a long-context chat model with the relevant files pasted in.
- Debugging a stack trace — any of the chat models, given the full trace and the code it points at.
- Reviewing a diff before you open the PR — paste the diff into a long-context model and ask what could break.
These overlap heavily, and the honest answer is that most developers settle on two: something inline in the editor for speed, and a chat model for thinking. A week on your own code will tell you more than any comparison table.
Getting usable code out of them
Which tool you pick matters less than how you ask. The biggest single improvement is to stop describing the problem and start showing it:
- Paste the real code and the real error. The actual stack trace and the actual function beat a paraphrase every time.
- State what you expected versus what happened. "I expected an empty list, I got
null" is a bug report; "it doesn't work" is not. - Give the language, framework and version. APIs move between major versions, and a model with nothing to anchor on picks one for you.
- Ask for the reasoning and the edge cases. "Explain why this works, then list the inputs that would break it" surfaces problems before you run anything.
- Request tests. Asking for a few test cases forces the model to commit to how the code should behave, and hands you something to run.
Ready-made versions of these live in our coding prompts pack, and the general principles are in how to write better AI prompts.
Where AI still gets it wrong
Every one of these tools will occasionally be confidently, fluently wrong, and the failure modes are consistent enough to watch for. Invented library methods that sound exactly like something the library ought to have. Function signatures with arguments in the wrong order, or borrowed from an older major version. Code that is correct on the happy path but subtly broken on empty input, unicode, timezones or concurrency. There are non-code risks too: generated output can resemble licensed source, and anything security-sensitive — auth, crypto, input handling, anything that builds a query — deserves more scepticism than a formatting helper. None of this makes the tools unusable; it makes review non-negotiable. Read what you accept, run it, test it, and remember that once you merge it, it is your code with your name on it. The same applies to factual claims — see how to fact-check AI answers.
After that, the fastest gains come from prompts you can reuse and an editor you can drive without thinking. Start with the prompt packs for Claude and ChatGPT, then tighten up the editor itself with the Cursor and VS Code shortcut cheat sheets.