Muse Code vs Claude Code: Meta’s New Coding Agent Next to Anthropic’s

Muse Code and Claude Code are the same kind of tool. Both are coding agents: you point them at a project, describe what you want, and they read files, run commands and propose changes. The honest difference is less about what they can do and more about who makes them, what they cost, how new they are, and which model does the thinking underneath.

Muse Code is Meta’s. It arrived in beta on 5 August 2026, alongside Muse Spark 1.2, according to Meta’s developer announcement, and came out of beta on 31 August with monthly plans and new features. Claude Code is Anthropic’s, and it runs in the terminal, in an IDE and on the desktop, as described in the Claude Code documentation. If you are new to the name Muse and wondering how the model, the app and the coding tool relate, what Muse Spark is and how it fits with Meta’s other products sorts that out first.

The short answer

If you already pay for Claude Pro or higher, Claude Code is the simpler choice, because it comes with the plan you have. If you want a cheaper entry price or pay-as-you-go billing, are curious about Meta’s newest model, and are comfortable with a tool that left beta only weeks ago, Muse Code is worth an afternoon on a practice project. Neither is the right answer for someone who cannot yet read the code an agent writes. That skill comes first, whichever tool you pick.

What Muse Code is

Muse Code is a terminal coding agent. You install it with one command, sign in through the browser at dev.meta.ai, then run it inside a project folder. The install line Meta publishes is below.

curl -fsSL https://dev.meta.ai/install.sh | bash

Once you have authenticated, you move into your project folder and start it with a single word.

muse

A few design choices stand out in Meta’s description. The agent can fan a job out to several subagents that work in parallel, each in its own git worktree, while a parent agent keeps an eye on them. There is a built-in /plan command that drafts a plan grounded in your codebase and waits for your approval before anything runs. Every subagent, tool call, steer and cancel is logged and can be replayed, and if a session drops you can pick it back up.

muse resume

TechCrunch’s launch report described it at launch as a beta aimed at large code bases, positioned against OpenAI’s Codex and Anthropic’s Claude Code, with Meta leaning on cost as the reason to switch. The same report says your working copy "is never touched".

What Claude Code is

Claude Code is Anthropic’s coding agent. It works in the terminal, inside an IDE and in a desktop app, and the official overview is the place to read what it does today, because agent features change often. This article sticks to what can be checked. If you want a feature-by-feature list, read the documentation rather than a comparison post, including this one.

For the models and general-purpose agents behind both companies, not just the coding tools, Muse Spark vs Claude covers the wider picture.

Price, as of September 2026

Both now sell monthly plans, but what the plan covers differs. Claude Code is included with the Claude Pro plan at $20 a month, or $17 a month billed annually, according to Anthropic’s pricing page. Max plans start at $100 a month for more usage, per the same pricing page.

Since leaving beta, Muse Code has three monthly plans on Meta’s product page: Everyday Usage at $5, High Usage at $15 with three times the usage, and Power Usage at $50 with ten times the usage. The smallest plan allows 10 to 50 prompts every five hours. Meta says the plans work out cheaper than paying per token. You can still pay API rates instead, or use a cheaper contributor tier. The standard rate is $1.25 per million input tokens and $4.25 per million output tokens, according to Meta’s announcement, and Artificial Analysis reports the same prices for Muse Spark 1.3. The contributor tier is rate-limited in a rolling five-hour window, is only offered in select countries, and your prompts may be used for training. Zero data retention is available on request through Meta’s sales team.

That training condition is the part to read twice. If your code belongs to an employer or a client, the contributor tier is probably not yours to choose. For the rest of Meta’s plans, including the consumer Muse app, Meta Muse pricing sets out each tier.

On cost per token, the models are far apart. Anthropic lists Claude Opus 5 at $5 per million input tokens and $25 per million output on the Claude pricing page, against Muse Spark’s $1.25 and $4.25. Plan limits are hard to compare directly, though, because each company measures usage its own way.

The models underneath

An agent is only as good as the model making its decisions. Muse Code runs Muse Spark, now at version 1.3. When Muse Spark first launched in April, Meta was unusually direct about its weak spots, acknowledging gaps in long-horizon agentic systems and coding workflows in its own launch post.

Version 1.3, released on 2 September, is Meta’s answer to that. Meta’s research post claims about 20% fewer tool calls and about 25% fewer tokens than 1.2, better handling of long multi-step tasks, and better resistance to prompt injection. Those are Meta’s own claims about its own model, so treat them as a starting point, not a verdict.

The independent numbers come from Artificial Analysis. On its Intelligence Index for Muse Spark 1.3, Claude Opus 5 at its max setting scores 63, while the public Muse Spark 1.3 at xhigh scores 61, the same as Claude Opus 5 at its high setting. On cost to run that index, Muse Spark 1.3 comes in at $0.55 per task against $1.23 for Claude Opus 5 at high.

So the fair summary is that Anthropic’s top model scores higher and Meta’s is cheaper per task. Benchmarks measure test sets, not your code base. A small gap on an index says little about whether an agent will understand your particular mess of legacy files, and what AI is actually bad at is a useful reminder of where both still fall down.

Where they really differ

• —Maturity. Muse Code launched in beta in August 2026 and left beta on 31 August. Claude Code is part of Anthropic’s paid Claude plans, with its own documentation site.
• —Billing. Claude Code comes inside a monthly Claude plan. Muse Code has its own monthly plans from $5, plus metered API usage or a rate-limited contributor tier that trades your prompts for a lower price.
• —Model. Muse Code uses Muse Spark 1.3. Claude Code comes from Anthropic, whose Claude Opus 5 scores higher on the Artificial Analysis index and costs more per task.
• —Workflow design. Meta highlights parallel subagents in separate worktrees, an approval-gated /plan, and a replayable log of every action. For Claude Code’s current workflow features, check its documentation directly.
• —Data. Meta’s contributor tier may use your prompts for training. Read the data terms of whichever tool you use before pointing it at code that is not yours.

Trying Muse Code safely

A new agent that can run commands deserves a cautious first week. None of this is a criticism of Muse Code in particular; it is how you should meet any coding agent.

1. 01Use a throwaway repository. Clone a small public project or start a fresh one. Do not start on your employer’s code, and do not start on anything with secrets in it. A project from AI project ideas that actually teach you something makes a good sandbox.
2. 02Run /plan before anything else. Ask for a small change, read the plan it drafts, and only approve it once you understand every step. If the plan touches files you did not expect, say so and ask it to narrow the scope.
3. 03Read the diff, line by line. Subagents work in separate git worktrees, so your working copy stays as it was while you review. Treat the change like a pull request from a stranger: run the tests, look for deleted checks, and question anything you cannot explain.
4. 04Check the replay when something looks odd. The event log shows which subagent did what, which is faster than guessing.
5. 05Decide on billing deliberately. The contributor tier may use your prompts for training, so read the terms before you pick it over a monthly plan or standard pay-as-you-go.

The quality of what you get back depends heavily on the request. The habits in AI prompts for expert-level web development apply to agents too: give the real constraint, name the version, and say how you will check the result.

If you are learning to code

A coding agent can write more code in a minute than a beginner can review in an hour. That is the risk. If you cannot tell whether a change is correct, the agent is not saving you time; it is moving the mistakes somewhere harder to find.

Used well, though, an agent is a patient explainer. Ask it to walk through its plan, then ask why it chose one approach over another. When its answer sounds confident but you cannot confirm it, the steps in how to check an AI answer when you are not the expert work just as well for code as for prose. And a clear request saves a lot of back-and-forth, which is the whole argument of how to write a prompt that works on the first try.

Pick Claude Code if

• —You already pay for Claude Pro or higher, so the agent is included in a plan you have.
• —You want one subscription that also covers Claude’s chat and Cowork.
• —You want Anthropic’s models, which score higher on the Artificial Analysis index, and accept that they cost more per task.

Pick Muse Code if

• —You want the lowest entry price, from $5 a month, or prefer to pay per use.
• —Meta’s approach appeals to you: parallel subagents in worktrees, an approval step before changes, and a full replayable log.
• —You are comfortable with a tool that is only weeks out of beta and will try it on practice code before anything that matters.

Where Coursium fits

Neither tool is a way to learn to code, and Coursium is not either. Coursium teaches people to use AI tools well at work, on iPhone, which is closer to the habits in the sections above than to writing software.

The practical next step is small. Pick one tool, one throwaway repository and one change you could make by hand. Let the agent plan it, read every line it writes, and compare the result with what you would have done. That hour will tell you more than any benchmark.