Meta's AI team has released the first model in the Muse series, Muse Spark, a native multimodal reasoning model that achieves significant breakthroughs in visual understanding and medical reasoning tasks, showing clear performance advantages over mainstream competitors.
Unlike common "vision module grafted onto language model" approaches, Muse Spark natively integrates text and visual information from the training stage, achieving true cross-modal joint reasoning. On the UI element location benchmark ScreenSpot Pro, Muse Spark scored 72.2 (reaching 84.1 with Python tools), far surpassing GPT-5.4 Xhigh's 39.0 and clearly leading Claude Opus 4.6 Max's 57.7.
Medical reasoning is another highlight. On the HealthBench Hard evaluation, Meta fine-tuned Muse Spark on a professional medical dataset built in collaboration with over 1,000 clinicians, with Muse Spark surpassing GPT-5.4 by over 2 percentage points. The CharXiv Reasoning scientific chart analysis benchmark also leads.
More noteworthy is the efficiency improvement: Meta revealed that Muse Spark achieves the same capability as Llama 4 Maverick with over an order of magnitude less compute, thanks to systematic improvements in pretraining architecture, optimization flow, and data management. Muse Spark will gradually integrate into Meta AI consumer services in the coming weeks, with API in private preview.