Welcome Muse Spark Open: open-weight frontier multimodal mixture-of-experts model from Meta

Muse Spark Open is an open-weight model from Meta. The mixture of experts model brings text, images, audio, and video into a model with approximately 816 billion parameters, around 42 billion active per token, and a context window of up to one million tokens. It generates text, giving developers a common interface for asking questions about different kinds of input.

Following Muse Glimmer, Muse Spark Open introduces a mixture-of-experts architecture at a much larger scale, and the most capable US open-weights model to date.

Meta trained Muse Spark Open for agentic coding: reading a codebase, writing and debugging code, and calling tools. It also takes images, audio, and video as input, so the same model can read a screenshot, listen to a voice note, or watch a screen recording while it works on your code. This post shows you how to run it, how to fine-tune it, and what we built with it.

Overall Capabilities and Architecture

Meta describes Muse Spark Open as a coding-optimized model trained on real developer workflows such as multi-file refactors and long debugging sessions. Muse Spark Open is an agentic focused model that plans, acts, observes, and self-corrects over long tasks, including browsing the web with images.

Feature	Muse Spark Open
Model size	~816B total parameters; ~42B active per token
Expert routing	256 experts per MoE layer; 8 selected per token
Inputs	Text, images, audio, and video
Output	Text
Context window	Up to 1M tokens
Checkpoint formats	BF16 and NVFP4

The checkpoint repositories are Muse-Spark-Open-816B-A42B and Muse-Spark-Open-816B-A42B-NVFP4.

Mixture of Experts

Perception Encoder

Muse Glimmer 30B uses an encoder with the same architecture. The design follows the Perception Encoder work from Meta. Video input goes through the same encoder as images.

Audio Encoder

Long Context

Inference Support

Transformers

You can initialize the model and processor as follows.

Text-only Inference

Images and Text

Video Inference

processor.video_processor.fps = 4.0