Meta releases small model Muse Ember, targeting agents' everyday repetitive tasks
Meta has released its smallest and most efficient model, Muse Ember, targeting agents' everyday tasks, including tool calling, small-scale coding, request routing, data extraction, and long-document processing. The model supports text, images, audio, and video, can process up to one million tokens at a time, and is now available through the Meta Model API and Muse Code, priced at $0.10 per million input tokens and $0.50 per million output tokens. Meta says its intelligence per dollar at every reasoning tier is higher than that of Claude Haiku 5.5 and GPT-6 Luna; in Artificial Analysis tests, its cost per task is about $0.14, compared with $2.39 for the larger Muse Spark 1.3, with scores of 36 versus 37; however, on real coding tasks in FrontierCode 2.0-AA, Haiku leads with 34.9% over Muse Ember's 27.2%.
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The original text provides the cost per task and comparison results on FrontierCode 2.0-AA, which can be used to judge the cost-effectiveness boundary of small models in agents' everyday tasks.
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Meta has introduced Muse Ember, its smallest and most efficient AI model to date. It's designed for the routine tasks AI agents handle all day: calling tools, handling small coding tasks, routing requests, extracting data, and processing long documents. Meta says Muse Ember delivers more intelligence per dollar at every reasoning level than Claude Haiku 5.5 or GPT-6 Luna. It costs about $0.14 per task in the Artificial Analysis test , while the larger Muse Spark 1.3 costs $2.39 for a nearly identical score of 36 versus 37. Muse Ember leads in most of Meta's own tests, but Haiku outperforms it on real coding tasks in FrontierCode 2.0-AA, scoring 34.9 percent to Muse Ember's 27.2 percent.
The model handles text, images, audio, and video and can process up to one million tokens at once. It's available now through the Meta Model API and Muse Code at $0.10 per million input tokens and $0.50 per million output tokens.