Meta enters the AI coding market with Muse Code, a terminal agent offering autonomous background tasks and aggressive pricing tied to code data sharing.
Meta has released Muse Code, its first AI coding agent, pricing a data-sharing tier at $0.30 per million tokens. The move targets developers who have flocked to tools like Cursor and GitHub Copilot, and it signals Meta's intent to compete on cost as much as capability The Next Web.
The agent runs in the terminal on macOS and Linux, built on Meta's Muse Spark 1.2 model. Unlike editor plugins that sit inside VS Code or JetBrains, Muse Code operates as a standalone command-line tool that can plan, write, and validate code across large repositories without constant prompting Her Zindagi.
Its defining feature is persistent background agents. These processes stay active throughout a session, gathering context and handling secondary tasks while the developer works elsewhere. The system also keeps an append-only log of every model action, tool call, and file change, allowing it to resume after crashes without losing progress.
Pricing is the sharpest lever. At $0.30 per million tokens for developers who allow their code to train the model, Meta undercuts the economics that recently forced GitHub to freeze new Copilot sign-ups as agentic usage blew through compute budgets. The trade is explicit: cheap access in exchange for real-world coding data that improves Muse Spark The Next Web.
The market Meta is entering is both brutal and lucrative. Cursor, the current pace-setter, is reportedly raising at a $50 billion valuation after reaching billions in revenue in just a few years. GitHub Copilot, OpenAI's coding tools, and Anthropic's Claude Code are all racing to make their agents more autonomous. Each additional degree of autonomy burns exponentially more compute, which is why Meta's subsidized pricing matters.
Meta's strategy mirrors its playbook in open-source AI: release capable models for free or cheap, harvest usage data, and improve the next generation. The difference here is the direct commercial pitch to professional developers. If the data-sharing tier attracts enough high-quality repositories, Muse Spark could close the gap with models trained on far larger proprietary datasets.
The risk for developers is lock-in to a pricing model that could change once Meta has the data it needs. The risk for Meta is that enterprise customers — the real revenue base — may reject a tool that ingests their code by default. GitHub and Anthropic have spent years building trust around data privacy; Meta has not.
Whether Muse Code becomes a standard part of the stack or a niche experiment depends on two things: whether the background agents actually reduce total development time, and whether the $0.30 price holds once the beta ends.
The market reaction
Early feedback from the beta suggests the terminal-first approach appeals to engineers who already live in the command line, but the lack of Windows support at launch limits adoption in many corporate environments. Meta has not committed to a Windows timeline.
What this means for you
If you lead an engineering team evaluating AI coding tools, Muse Code deserves a proof-of-concept slot — especially if your stack runs on macOS or Linux and your token spend on Copilot or Cursor has become unpredictable. The data-sharing tier makes the math easy to test. Just read the data-use terms before pointing it at proprietary code.
FAQ
What is Muse Code?
A terminal-based AI coding agent from Meta that runs persistent background tasks, powered by the Muse Spark 1.2 model, currently in beta for macOS and Linux.
How much does Muse Code cost?
The data-sharing tier is $0.30 per million tokens. Meta has not published pricing for a non-data-sharing tier.
Does Muse Code work on Windows?
Not at launch. Meta has not announced a Windows release date.
How does it differ from GitHub Copilot or Cursor?
Muse Code runs as a standalone command-line agent rather than an editor plugin, emphasizes autonomous background agents, and offers a significantly lower price tied to code data sharing.
