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Meta 发布轻量模型 Muse Ember,主打智能体执行与低成本

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

Meta Superintelligence Lab 周一发布轻量模型 Muse Ember,定位智能体的复杂推理执行和重复任务,如工具调用、编码子任务、路由、抽取和长文档解析。输入定价为每百万 token 10 美分,Meta 引用 Artificial Analysis 综合基准称其成本低于 Anthropic 的 Haiku 和 OpenAI 的 Luna,并与 DeepSeek V4.1 Flash、GLM 5.3 Flash 相当。模型支持文本、图像、音频输入,设计上与 Muse Wonder 搭配使用, Wonder 负责规划, Ember 处理大批量任务。这是 Muse 品牌下的第四个模型,此前有 Spark、Glimmer 和 Wonder。

为什么值得看

原文给出每百万 token 定价和与 Haiku、Luna、DeepSeek V4.1 Flash 的成本对比,可用于评估智能体执行层的选型。

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原文

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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事件综述

Meta 发布最小模型 Muse Ember,主打智能体任务与低成本

2026 年 11 月 9 日,Meta(Meta Superintelligence Lab)发布旗下最小、最高效的模型 Muse Ember,定位为 Muse 品牌下第四个模型(此前有 Spark、Glimmer 和 Wonder)。该模型面向智能体全天重复的 token 密集任务,包括工具调用、编码子任务、路由、信息抽取和长文档解析,可跨应用执行工作流、按顺序调用工具、运行测试并做针对性代码修复。模型具备 1M token 上下文窗口,原生支持文本、图像、音频和视频输入,现已通过 Meta Model API 和 Muse Code 提供。定价方面:200K 以内的提示为每百万输入 token 0.10 美元、输出 0.50 美元,超过 200K 输入后涨至 0.30/1.50 美元。Meta 引用 Artificial Analysis 基准称其单位成本智能更高,每美元智能水平高于 Anthropic 的 Claude Haiku 5.5 和 OpenAI 的 GPT-6 Luna,与 DeepSeek V4.1 Flash、GLM 5.3 Flash 相当;Artificial Analysis 显示 Ember 与 Haiku 5.5 同为 36 分的 Intelligence Index,但估算每任务成本约 0.14 美元对 0.41 美元,接近 Muse Spark 1.3 的 37 分(2.39 美元),且位于效率帕累托前沿。后续报道称,在 FrontierCode 2.0-AA 真实编码任务上 Ember 得分 27.2%,低于 Haiku 的 34.9%。关于竞争对象表述不完全一致:Meta 官方称成本低于 Haiku 和 Luna,而 The Decoder 引用 Meta 的说法为每美元智能水平高于两者。该模型设计上与 Muse Wonder 搭配使用,由 Wonder 负责规划、Ember 处理大批量任务。

其他来源的报道 5