Meta releases Muse Ember, a new lightweight model for agent execution
Quick Look
- Meta's Superintelligence Lab launched Muse Ember, its lightest and most cost-efficient AI model, priced at 10 cents per million input tokens.
- The model targets complex reasoning and repetitive agent tasks like tool calls, coding, routing, extraction, and document parsing, and is designed to pair with larger models such as Muse Wonder in agentic workflows.
AI-generated summary
Why It Matters
Meta's Superintelligence Lab released Muse Ember, described as its lightest and most cost-efficient AI model yet, designed for complex reasoning execution and repetitive agent tasks such as tool calls, coding subtasks, routing, extraction, and long document parsing.
Meta Superintelligence Lab released a new AI model on Monday called Muse Ember, the lab’s lightest and most cost-efficient AI model yet. In its release, Meta said Ember was “designed for complex reasoning execution and the work agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing.”
Meta particularly emphasized Ember’s cost-per-task, using Artificial Analysis’s composite benchmark score to show Ember as cheaper and smarter than competitors like Anthropic’s Haiku and OpenAI’s Luna.
At 10 cents per million input tokens, Ember is significantly cheaper than frontier competitors like Haiku and Luna. Not only that, it’s cost-competitive with open-source competitors like DeepSeek V4.1 Flash and GLM 5.3 Flash.
Given the emphasis on low cost and accuracy at scale, Ember is designed to pair with a larger model like Meta’s Muse Wonder in an agentic harness. In the case of a customer-support agent, for instance, you might see a heavier model establishing rules for how to handle a particular issue, then sending a lighter-weight model to apply the rules across tens of thousands of cases. As Meta puts it in the announcement, “Muse Wonder plans, Muse Ember handles the volume.”
With volume-heavy use cases in mind, Meta also trained Ember to work well across different types of media, including text, images, and audio. The result makes the model surprisingly flexible in what kind of inputs it can handle. As Meta puts it in the announcement, “agents can read screenshots, charts, and scanned documents, or work from voice memos, meeting recordings, and video without a separate model.”
Open Questions
- What is the exact release date beyond 'Monday'?
- What are Ember's specific benchmark scores compared to Haiku and Luna?
- When will Muse Ember be available to developers or enterprise customers?
- What is the pricing for output tokens?
