Meta has unveiled Muse Ember, its smallest model yet, designed to handle repetitive coding and reasoning tasks performed by AI agents while keeping operating costs low.
The compact model is built for everyday agent workloads, including tool calls, coding subtasks, information extraction, request routing, and parsing lengthy documents. Meta says Muse Ember delivers more intelligence per dollar than Anthropic’s Claude Haiku 5.5 and OpenAI’s GPT-6 Luna across reasoning settings.
According to Artificial Analysis, Muse Ember scores 36 on its Intelligence Index, matching Claude Haiku 5.5 while costing approximately $0.14 per task. This is nearly three times cheaper than Haiku’s estimated $0.41 cost at the same score.
The model also compares favorably with Meta’s larger Muse Spark 1.3. Artificial Analysis estimates that Muse Ember costs $0.14 per Intelligence Index task, compared with $2.39 for Spark, which scores 37 on the index.
Meta designed Muse Ember to handle the numerous smaller operations that agents perform during extended workflows rather than focusing exclusively on complex reasoning problems.
It can execute business workflows from start to finish by retrieving records, making sequential tool calls, and updating information across applications. The model also comes with a 1-million-token context window and can generate up to 512,000 output tokens, allowing it to process entire codebases or large document collections in a single call.
In evaluations, Muse Ember scored 37.8 percent on AutomationBench-AA, which measures end-to-end business workflows. This compared with 20.2 percent for GPT-6 Luna and 13.9 percent for Claude Haiku 5.5.
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