GitHub Copilot and Claude Code are compared constantly and are barely competitors. One lives in the editor and completes the line you are writing; the other is a terminal process you hand a task and leave alone. What that difference means when you want several agents working at once, and which parts of each survive the move.
the short version
GitHub Copilot and Claude Code are compared constantly and they are barely competitors. Copilot's centre of gravity is the editor, where it completes the line you are writing at the place your cursor already is; Claude Code's centre of gravity is a shell, where you hand it a task and then stop typing for several minutes. Both have grown toward each other, so the line is genuinely blurry at the edges, but the centres have not moved, and the centre is what decides whether you can run four of the thing at once.
The axis people should be comparing on is not model quality, not price and not context window. It is supervision granularity: how often the tool needs you. A tool you correct every few seconds cannot be parallelised, because the supervision is the entire cost. A tool that works unattended for minutes can be. Everything below follows from that one sentence.
why the usual comparison is the wrong axis
The comparison gets made because both products put a large language model near your code and both come out of roughly the same budget. That is enough similarity for a search query, and the query is asked constantly. It still produces bad answers in both directions: people who want faster typing get told to learn a terminal agent, and people who want unattended work get told to buy completions.
Being fair means admitting the products have moved. Copilot is not only completions: it has chat, it has agentic modes, and as documented it has a coding agent on GitHub that can take an issue and open a pull request without you watching. Claude Code is not only a terminal: it has editor integrations, and plenty of people drive it from a panel inside the editor they already had open. Anyone claiming the boundary is clean is describing the products from two years ago.
But a centre of gravity is not a feature list. It is where the product is best, where its defaults point, and what it feels like when you stop configuring it and just use it. Copilot is at its best when you are typing and it finishes the thought. Claude Code is at its best when you have stopped typing entirely. Both products can be pushed away from their centre, and both are noticeably less comfortable there.
We make the same argument on our comparison page, where the editors are deliberately kept out of the feature table that scores parallel-agent runners. Scoring Copilot on whether it manages git worktrees would be building a comparison to make it lose at something it never entered.
supervision granularity, the axis that matters
Here is the whole argument in one paragraph. Every AI coding tool has an interval: the typical time between the tool doing something and you having to look at it. For inline completion that interval is about one second, because you accept or reject a suggestion as fast as you read it. For a delegated task in a terminal agent, the interval is minutes, because the agent reads files, writes a patch, runs the tests, reads the failures and tries again while you are not watching.
Now try to run two of each. Two inline completion sessions means two streams of suggestions arriving every second or two, each needing your judgement in the moment it appears, and there is no version of a human that services both. The tool is not broken; it is built around your attention being continuously present, and you only have one.
Two delegated agents means two processes, each of which will not need you for several minutes. You start the first, you start the second, you review whichever finishes. The arithmetic that makes multi-agent work interesting is entirely a consequence of the interval being long enough that a second agent fits inside the first agent's gaps.
where the interval actually comes from
It is not a property of the model. The same model can be wired into a completion loop or a delegation loop. The interval comes from what the tool may do between checkpoints: how many files it can read without asking, whether it can run commands, whether it can iterate against test output alone, and whether the interface has anywhere to put work that is in progress. An interface built around your cursor has nowhere to put a fifteen-minute job, because the cursor is where you are.
This is why the agentic modes on editor-based tools change the picture without erasing it. They lengthen the interval, genuinely. What they do not change is that the container for the work is a window you are looking at, and those are a resource you have one of.
what GitHub Copilot is genuinely better at
This section is not a courtesy. If your problem is the one Copilot solves, no amount of agent orchestration is a substitute, and buying the wrong one costs you a month before you notice.
inline completion, in the editor you already have
The core loop is still the best argument for it. You type, it finishes, you keep typing. No prompt to write, no task to scope, no context to assemble. For the large fraction of programming that is knowing exactly what you want and typing it out, a good completion halves the keystrokes and never breaks your train of thought. A terminal agent cannot do this and does not try to.
breadth of editor and language support
Copilot is available across a wide range of editors, as documented on its own page, so for most developers it installs into the environment they already know rather than asking them to adopt a new one. That is a larger advantage than it sounds: the adoption cost of a tool is mostly the cost of changing where you work, and Copilot has almost none.
it is often already paid for
At a great many companies the Copilot licence already exists, procurement has already happened, and the security review is already filed. We are not going to quote a figure for either product, but the shape of the difference is worth naming plainly: Copilot has free and paid tiers and business and enterprise plans with org-level controls, and it is included with some GitHub plans. Claude Code is bought against a model subscription or API usage. Which is cheaper depends entirely on how much you use, and anyone who gives you a single answer to that has not asked how you work.
review integration where the code already lives
Copilot is wired into GitHub itself: pull request work and code review suggestions happen where your review process already is, as documented. That matters more than feature lists suggest, because the last mile of any AI-written change is a human reading a diff, and landing the work where your team already reads diffs skips a whole integration problem.
what Claude Code is genuinely better at
long unattended runs
The thing you buy is the ability to describe a task, walk away, and come back to a branch: not a suggestion, but a piece of finished work with tests run against it. Whether the work is good is a separate question and often the answer is no, but the shape of the transaction is unlike anything a completion engine offers.
an arbitrary shell
An agent with a real terminal can run your build, your linter, your migrations, the odd internal script nobody ever wrapped in an extension, and the six-year-old Makefile that is the only thing that knows how to package the thing. Extension-based tools reach the world through the extension API. A shell has no such boundary, which is both the advantage and the reason to be careful about what you let it run.
no editor dependency
It runs over SSH on a box with no display. It runs in a container in CI. It runs in a worktree you created thirty seconds ago in a directory you will delete this afternoon. Nothing about it assumes a window, which is exactly what makes the next section possible.
scriptability
Because it is a process with arguments and standard streams, it composes with everything else that is a process. You can start it from a shell script, from a Makefile, from a loop over a list of packages. That is a category of use that a graphical tool cannot offer at all, and it is where a lot of the genuinely surprising multi-agent workflows come from.
you can run N of it
The last one is the one this post exists for, and it is a consequence of the previous four rather than a separate feature. A thing with no display dependency, an interval measured in minutes and a command-line interface is a thing you can start four of.
what running several of each actually looks like
several Claude Code processes
You give each agent its own checkout and its own branch, then start each one in its own directory. In git that is a worktree, which is cheap because all the worktrees share one object database. The commands are ordinary and there is nothing clever in them:
git worktree add ../wt-schema -b agent/schema
git worktree add ../wt-api -b agent/api
git worktree add ../wt-ui -b agent/ui
# three shells, one per directory, each running its own agent
# later, once each branch is reviewed and merged
git worktree remove ../wt-schemaThat is the whole mechanism. Each agent has its own working directory, so two of them cannot silently overwrite each other's edits, and real conflicts surface at merge time where a human can see them. Everything harder about multi-agent work is a workflow problem rather than a tooling one. The long version is in what AI agent orchestration means and the step-by-step version in how to set up a multi-agent workspace.
several editor-based agents
The honest account is that there are equivalents and they are not nothing. You can open several editor windows on several worktrees and run an agentic session in each, and people do. Copilot's coding agent on GitHub, as documented, works against issues and produces pull requests, and several issues can be in flight at the same time without any of them occupying a window on your machine at all. That is a genuinely parallel model, and it has an advantage ours does not: the work is not running on your laptop, so closing the lid does not end it.
What is different is where the work is visible and what the unit of supervision is. With several processes, the unit is a process: you can see whether it is running, you can attach to it, you can kill it, and you can ask a script how many are alive. With several editor windows, the unit is a window, and windows are not addressable by anything except you looking at them. With a hosted coding agent, the unit is a pull request, which is excellent for review and gives you very little while the work is in progress.
None of those three is wrong. They are three different answers to where does in-progress agent work live, and the reason the terminal answer scales to a fleet is that a process is the only one of the three that a computer can enumerate.
the constraint that binds either way
Whichever shape you pick, the ceiling is not your CPU count and it is not the tool. It is how many diffs you can read carefully before you start approving them by pattern. Agents parallelise. Review does not. Every agent you add produces another diff for the same one person, and past a small number the extra agent is making the output worse rather than faster.
cost, and the thing neither tool shows you
Describing the pricing of either product in figures would be a disservice, because both change their plans more often than a post gets updated. The shapes, as read off the vendors' own pages: Copilot has a free tier with caps, paid individual plans, and business and enterprise tiers, and comes included with some GitHub plans. Claude Code is used either against a subscription or against API usage, and which of those you are on changes what running out means. Go and read the current numbers before you commit anything to a spreadsheet.
The interesting thing is not the sticker price, it is that neither tool gives you a combined figure across concurrent sessions. One session in a terminal shows you no running total for the afternoon, and four sessions show you four times no running total. Editor-based tools mostly meter against a monthly request allowance, which is legible per seat and tells you nothing about which of today's four tasks was the expensive one.
And the naive fix does not work. If you try to total it yourself by summing token counts out of transcripts, you get a number that is wrong by a multiple, because for a long coding session the overwhelming majority of tokens are cache reads rather than fresh input, and cache reads and cache writes are priced differently from both. We measured this and wrote up the arithmetic in what Claude Code actually costs. The multi-agent point is narrower than the pricing point: parallelism multiplies burn rate, and burn rate is the one variable a person watching four terminals cannot estimate from looking at them.
which should you use
A real answer, in the order most people should read it.
If you mostly know what you want to write and want to write it faster, use Copilot. The completion loop is the thing, it installs into the editor you already have, and at many companies it is already paid for so the decision costs you nothing but an evening. No amount of delegation replaces the feeling of a good completion landing exactly where your cursor was.
If your work arrives as scoped tasks you could hand to a competent stranger, use Claude Code. Migrate this module, add tests to these six files, make this failing suite pass. The value is entirely in walking away, so if you would not walk away, you are paying for something you are not using.
Most people who do both kinds of work should use both, at different parts of the day. That is not a fence-sitting answer, it is the actual pattern: agents on the mechanical work in the background, completions in the editor for the piece you are thinking hardest about. They do not fight, and if your budget is tight, the free tier of one of them is the obvious place to start.
If you already run three or more agents and the problem is keeping track of them, you have a different problem from the one this comparison is about. That is the gap the tools on our comparison page exist to fill, and several of them are free, which is worth knowing before you spend anything.
where we come in, and where we do not
Skribbl is a macOS app that puts real terminals and coding agents on an infinite canvas. You can spawn several agents at once, each in its own git worktree, draw a line between two of them to let one command the other, erase it to revoke that, and watch a meter in the top bar showing combined spend while everything is still running.
What it is not, stated plainly, because this post would be dishonest without it. It is not an editor. It has no inline completion, no inline edit, no language server, no go-to-definition and no extension ecosystem. It ships no model and no subscription, so whatever you are paying a model provider you keep paying. It does not compete with Copilot on anything Copilot is good at, and if the comparison you came here for is which one writes better code, we are not in that comparison at any price.
It sits a layer above: the place where you keep track of four agents you already decided to run. If you are running one agent at a time and that is working, keep doing that. This category is a solution to a bottleneck, and it is worth exactly as much as the bottleneck it removes.