Meta releases its smallest and most economical model, Muse Ember, targeting repetitive agentic tasks

Meta has released Muse Ember, its smallest and most efficient model, aimed at high-frequency agentic tasks such as tool calling, coding subtasks, routing, extraction, and long-document parsing. The company says that under equivalent inference settings it is more cost-effective than Claude Haiku 5.5 and GPT-6 Luna, scoring 36 on the Artificial Analysis Intelligence Index at about $0.14 per task. The model supports a 1M token context and up to 512K output tokens, natively understands text, images, audio, and video, and launches today as muse-ember-1.0 on Muse Code and the Meta Model API, priced at $0.10 per million input tokens and $0.50 per million output tokens.