Muse-Spark-Open

Muse Spark Open

Authors: Meta Superintelligence Labs Model Release Date: October 2026 License: Muse Model License

Muse Spark Open is an 816B total/42B active parameter open weights model with a context window of 1M tokens. Built for coding and agentic work, Muse Spark Open is natively multimodal. This open weights version has additional safety post-training to make it more robust against misuse as a standalone model without system guardrails. We recommend deploying Muse Spark Open as part of a broader AI system, with additional guardrails suited to the specific use case.

The model can perform well across a wide variety of use cases including:

1. Multi-Step Reasoning - chains reasoning over long horizons, sustaining coherent plans across complex, extended workflows.
2. End to End Agentic Task Completion - multi-step planning, sequential tool invocation, failure recovery, and long-horizon task execution, working within scaffolds, write and debug code, and resolve multi-turn requests from start to finish.
3. Coding Agents - writing, debugging, and resolving real-world software engineering tasks.
4. Tool Use - tool invocation across extended multi-turn workflows, diagnosing error and retrying with tool call failures
5. Multimodal Reasoning - interpreting screenshots, charts, documents, images, audio and video recordings, alongside conversation for agentic and information-rich environments.
6. Synthetic Data Generation - generating high-quality training data for downstream model development.
7. LLM-as-a-judge Evaluation - serving as an evaluator for other models' outputs.

Muse Spark Open Model Overview

Model Access and Best Practices

Access via API The model is available via API through third party inference providers.

Open Weights on your own system The weights are available for download through Hugging Face. The model is available in BF16 and NVFP4 versions. To run on your own system, the model requires a GPU with at least 700GB of VRAM for NVFP4 and 2TB for BF16. It also requires an inference deployment framework - supported frameworks are SGLang, vLLM, Hugging Face, TensorRT-LLM, Unsloth and Llama.cpp. It is also optimized to run on two Mac Studios with M5 Ultra using MLX.

Please see the developer documentation for more information on how to deploy and use the model.

Inference Parameters You can customize inference parameters to achieve the best performance for your use case.

• Reasoning Effort: Reasoning effort controls how much the model thinks before it responds. Set it with the reasoning_effort request parameter, or with chat_template_kwargs={"reasoning_effort": ...}, to low, medium, high, or xhigh. The default is xhigh. Use high or xhigh for complex problem solving, coding, and agentic tasks, and low or medium for faster, shorter responses. Any other value returns an error.
• Sampling Parameters: The following settings are recommended for most deployments: temperature = 1.0, top_p = 1.0, top_k = disabled

Training data

Training data includes text, images, audio, and video. It includes data drawn from public sources, crawled from the Internet, acquired from third parties, derived from Meta’s products and services, and synthetically generated, curated and enriched by external vendor networks and Meta personnel. Data curation methodologies include cleaning, processing, modifying, deduplication, and filtering, among others.

Benchmarks

We compare Muse Spark Open to other leading open-weight models of similar size and architecture. For Muse Spark Open we use xhigh reasoning effort, temperature = 1.0, top_p=1.0, top_k=disabled across benchmarks.