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Meta releases Muse Ember, a new lightweight model for agent execution

Meta Superintelligence Lab on Monday released Muse Ember, a lightweight model designed for complex reasoning execution and repeated agent tasks such as tool calls, coding subtasks, routing, extraction, and long document parsing. Priced at 10 cents per million input tokens, Meta cited Artificial Analysis's composite benchmark to show it as cheaper than Anthropic's Haiku and OpenAI's Luna, and cost-competitive with DeepSeek V4.1 Flash and GLM 5.3 Flash. The model handles text, images, and audio, and is designed to pair with Muse Wonder in agentic workflows, with Wonder planning and Ember handling volume. It is the fourth model under the Muse brand, following Spark, Glimmer, and Wonder.

Why it matters

The article gives per-million-token pricing and cost comparisons against Haiku, Luna, and DeepSeek V4.1 Flash, useful for evaluating model choices at the agent execution layer.

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Original article

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.

**Image Credits:**Meta Superintelligence Labs

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.”

Ember is the fourth model released under Meta’s Muse brand, following the mid-weight Spark (released in April), Spark’s open source twin Glimmer (released in August), and the Wonder frontier model (released in October). The flagship consumer AI product, the Muse Agent, was released in September to unexpected success, with new features like Spaces and video chat rolling out earlier this month.

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Russell Brandom has been covering the tech industry since 2012, with a focus on platform policy and emerging technologies. He previously worked at The Verge and Rest of World, and has written for Wired, The Awl and MIT’s Technology Review. He can be reached at [email protected] or on Signal at 412-401-5489.

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