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Muse Code

Beta

Meta's terminal-based AI coding agent with persistent async background agents

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Meta's first coding agent and a credible beta entry with genuinely novel architecture — persistent background agents, worktree-isolated sub-agent fan-out, and a crash-safe event log. Powered by the co-trained Muse Spark 1.2, it lands second only to Claude Opus 5 on Meta's own published benchmarks. Vendor-run numbers, beta status, and closed-source binary distribution temper the promise. Best for developers evaluating coding agents who want to test persistent-context architecture and aggressive contributor-tier pricing.

AI-Native

Agent loop + persistent background agents hold full coding-session context — the model reads, acts, and continues across steps without context reset.

Is it right for you?

Good for

  • Persistent multi-step coding sessions — background agents retain repo context across tasks, avoiding redundant exploration and reducing steering
  • Long-horizon autonomous tasks — event-log crash recovery enables 24-hour runs; agent resumes precisely where it stopped with no lost work
  • Parallel feature work — worktree-isolated sub-agents build multiple features simultaneously without collisions (Meta demoed six game features in parallel)
  • Cost-sensitive experimentation — contributor tier at ~$0.10 input / $0.20 output per 1M tokens undercuts every comparable coding agent
  • Multimodal repo work — accepts video fly-throughs and images as coding context; no incumbent terminal agent ships this as a first-class flow

Not good for

  • Production workflows requiring independently verified benchmarks — all published results are vendor-run in Meta's framework with no third-party reproduction
  • Windows development — macOS and Linux only at beta launch; no Windows build exists
  • Open-source or self-hosted pipelines — closed-source binary installed via shell script; no public repository, API-dependent, no weight access
  • Teams with strict data governance — contributor tier sends prompts and completions to Meta's training pipeline; opting out costs 12.5x more on input

Our experience

We haven't tested Muse Code ourselves. This profile is based on public documentation, user reviews, and community feedback.

Pricing

Paid
Standard$1.25 in / $4.25 out per 1M tokens
  • No training on your data — prompts and completions are not used to train Meta models
  • 3,000 requests per minute / 4M tokens per minute per team
  • 1M token context window
  • Web search grounding: $2.50 per 1,000 queries
  • Cached input: $0.15 / 1M tokens
Contributor$0.10 in / $0.20 out per 1M tokens
  • Prompts and completions used to train future Meta models
  • 60 requests per minute / 2.1M tokens per minute per team
  • 12.5x cheaper on input, 21.25x cheaper on output vs standard tier
  • Cached input: $0.002 / 1M tokens
  • Aimed at prototyping and experimentation where training on your data is acceptable
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No verdict changes yet

The clock starts day one — changes land here as our verdict evolves.

Sources

Verification log

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How we evaluate