Meta's Muse Spark Open brings a frontier MoE to open weights

Meta AI has released Muse Spark Open, an open-weight multimodal model built on a mixture-of-experts design. The model totals 816 billion parameters but activates only 42 billion for any given token, a structure meant to deliver frontier-scale capability without the full compute cost of a dense model of the same size.

The headline capability is reach. Muse Spark Open supports a context window of up to one million tokens and handles text and vision inputs, positioning it for agentic coding workflows where a model needs to keep an entire codebase, long logs, or multi-step task histories in view at once.

Why it matters

Open releases at this scale remain rare. Most models that compete at the frontier stay behind APIs, so weights that developers can inspect, fine-tune, and self-host change what independent teams can build.

• 816B total parameters, 42B active per token
• Multimodal: text and vision inputs
• Up to 1,000,000-token context
• Oriented toward agentic coding

The sparse-activation approach is the practical hook here: it lets a very large parameter count run with the inference footprint closer to a mid-size dense model. Licensing is listed as a custom "other" license rather than a standard permissive one, so teams planning production use should read Meta's terms closely. Full details are available on the model's Hugging Face page.