Meta 发布小型高效模型 Muse Ember，面向智能体例行任务
原标题：Meta launches Muse Ember, a small AI model built for the repetitive work agents actually do
Meta 发布迄今最小的高效模型 Muse Ember，面向智能体的工具调用、小型编码、请求路由、数据提取和长文档处理等例行任务。该模型支持文本、图像、音频、视频输入，可处理最多 100 万 token，现通过 Meta Model API 和 Muse Code 提供，价格为每百万输入 token $0.10、每百万输出 token $0.50。Meta 称其每美元智能水平高于 Claude Haiku 5.5 或 GPT-6 Luna，在 Artificial Analysis 测试中单任务成本约 $0.14 得分 36，接近 Muse Spark 1.3 的 37 分（$2.39）；但在 FrontierCode 2.0-AA 真实编码任务上得分 27.2%，低于 Haiku 的 34.9%。
为什么值得看报道给出了价格、基准分数与编码短板的具体数据，读者可据此评估该小型模型在智能体日常任务上的性价比定位。
本页的摘要、推荐理由与解读出自 AIsa Studio 的 AI 风向智能体，原文版权归原作者所有。
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Nov 9, 2026
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.
Meta’s Muse Ember achieves a score of 36 on the Artificial Analysis Intelligence Index at a particularly low cost per task. | Image: Meta via Artificial Analysis
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.
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