Muse Spark: Scaling Towards Personal Superintelligence

Today, we’re excited to introduce Muse Spark, the first in the Muse family of models developed by Meta Superintelligence Labs. Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration. Muse Spark is the first step on our scaling ladder and the first product of a ground-up overhaul of our AI efforts. To support further scaling, we are making strategic investments across the entire stack — from research and model training to infrastructure, including the Hyperion data center. Muse Spark is available today at meta.ai and the Meta AI app. We’re opening a private API preview to select users. Muse Spark offers competitive performance in multimodal perception, reasoning, health, and agentic tasks. We’re also releasing Contemplating mode, which orchestrates multiple agents that reason in parallel. This allows Muse Spark to compete with the extreme reasoning modes of frontier models such as Gemini Deep Think and GPT Pro. Muse Spark is built from the ground up to integrate visual information across domains and tools. One major application of personal superintelligence is to help people learn about and improve their health. To build personal superintelligence, our model’s capabilities should scale predictably and efficiently along three axes: pretraining, reinforcement learning, and test-time reasoning. We found that Muse Spark demonstrates strong refusal behavior across high-risk domains, enabled by pretraining data filtering and safety-focused post-training.