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About

Muse Code is Meta’s terminal coding agent, powered by Muse Spark 1.2, for handling complex software engineering tasks across large repositories. The agent can plan changes, write code, validate results, and coordinate multiple persistent subagents during development sessions. Muse Code uses async background agents that stay active throughout a session to reduce repeated information gathering and help complete multi-step tasks with less steering. Its runtime uses a local event log that records model calls, tool runs, approvals, and edits so sessions can be replayed and resumed after failures. Muse Code includes bundled skills such as /plan for approval-gated planning, /grill for stress-testing plans, and /goal for working toward completion. Built for AI developers and software teams, Muse Code helps automate coding workflows, long-running engineering tasks, debugging, and repository-level development.

About

Muse Spark Open is an open-weight, natively multimodal language model for developers and organizations building AI applications, coding systems, and agentic workflows. The released model has 816 billion total parameters, 42 billion active parameters, and a 1-million-token context window. Teams can download the model weights, run inference through supported engines such as vLLM, SGLang, TensorRT-LLM, Hugging Face Transformers, Apple MLX, llama.cpp, or Unsloth, and customize the model using PyTorch. It supports long-context reasoning, software engineering, coding, tool use, and multimodal inputs. Deployment options include cloud inference through listed API providers or self-hosting on compatible GPU systems; Meta documents a desktop setup using two Apple M5 Ultra Mac Studio computers. The model is released under the Muse Model License with limited commercial terms.

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Software engineers, AI developers, coding agent users, platform teams, DevOps teams, ML engineers, research teams, enterprise development teams, and organizations that need terminal coding agents, repository automation, code generation, debugging, validation, persistent subagents, replay-safe execution, long-running coding workflows, and end-to-end software development support

Audience

AI developers, machine learning engineers, software engineers, AI researchers, enterprise development teams, and organizations seeking customizable open-weight multimodal models for local inference, autonomous agents, complex coding, and long-context AI applications

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Not Supported

API

Offers API Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

$1.25 per 1M tokens (input)
Standard pricing: $1.25 per million input tokens and $4.25 per million output tokens

Discounted "contributor" tier costing $0.10 per million input tokens and $0.20 per million output tokens for users who agree to share feedback to improve the AI.
Free Version Supported
Free Trial Not Supported

Pricing

$0
Model weights are available at no charge under the Muse Model License, which has limited commercial-use terms. Hosted inference providers set their own usage prices.
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5

Pros & Cons from Real Users

Pros

  • Muse Code looks exciting because Meta is finally going after coding agents directly, not just releasing another general AI model. A terminal-based agent that can write code, inspect software, run tests, and validate changes is exactly the kind of workflow developers actually care about. The pricing angle is a big deal. Meta is pitching Muse Code as one of the more affordable coding agents, with a lower-cost tier that is reportedly less than one-tenth the cost of its general Muse Spark model. That matters if you use coding agents all day instead of just for the occasional refactor. I also like that it is powered by Muse Spark 1.2, which sounds more focused on software engineering than earlier Muse Spark releases. If Meta can make the agent reliable inside real repos, Muse Code could become a serious daily tool.

Cons

  • It is still in beta, so I would not trust it blindly yet. Coding agents need to prove themselves on messy codebases, failing tests, weird dependencies, security-sensitive changes, and long multi-step tasks. I would also want to see more independent developer feedback. Meta’s pricing and positioning are interesting, but the real test is whether Muse Code can consistently make good changes without wasting time or creating cleanup work.

Pros & Cons from Real Users

Pros

  • Meta finally opened up a genuinely frontier-level Muse model. The 1M-token context window and 42B active parameters make it strong enough for large repos, long agent sessions, multimodal work, and complex coding without feeling like a stripped-down open model. The local deployment story is also a huge deal. Being able to run something this capable on two M5 Ultra Mac Studios is impressive, especially for teams that want more control over data, infrastructure, and customization. I also like how much of the stack is open around it. Muse Spark Open works with MLX, vLLM, SGLang, TensorRT-LLM, Transformers, llama.cpp, and Unsloth, and Meta is making the weights available through Hugging Face, SageMaker, Google Cloud Model Garden, and Microsoft Foundry.

Cons

  • Even though it is technically desktop-capable, needing two M5 Ultra Mac Studios for the full local experience is still a serious setup, so most developers will probably use hosted inference instead.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Meta
Founded: 2004
United States
meta.ai

Company Information

Meta
Founded: 2004
United States
research.meta.ai/blog/muse-spark-open

Alternatives

Claude Code

Claude Code

Anthropic

Alternatives

Hermes Agent

Hermes Agent

Nous Research
GPT-6.1 Sol

GPT-6.1 Sol

OpenAI
Inkling

Inkling

Thinking Machines Lab

Categories

Categories

Integrations

Baseten Not Supported
Fireworks AI Not Supported
Groq Not Supported
Hugging Face Not Supported
Meta AI Supported
Modal Not Supported
Muse Glimmer Supported
Muse Spark Supported
Muse Spark 1.1 Supported
Muse Spark 1.2 Supported
Muse Spark 1.3 Supported
Muse Wonder Supported
Muse Wonder Full Supported
Nebius Not Supported
OpenRouter Not Supported
Together AI Not Supported
vLLM Not Supported

Integrations

Baseten Supported
Fireworks AI Supported
Groq Supported
Hugging Face Supported
Meta AI Not Supported
Modal Supported
Muse Glimmer Not Supported
Muse Spark Not Supported
Muse Spark 1.1 Not Supported
Muse Spark 1.2 Not Supported
Muse Spark 1.3 Not Supported
Muse Wonder Not Supported
Muse Wonder Full Not Supported
Nebius Supported
OpenRouter Supported
Together AI Supported
vLLM Supported
Claim Muse Code and update features and information
Claim Muse Code and update features and information
Claim Muse Spark Open and update features and information
Claim Muse Spark Open and update features and information