Meta launches its smallest model Muse Ember, matching Claude's performance at about one-third the cost
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Meta has released Muse Ember, its smallest and most efficient model to date, with the model ID muse-ember-1.0, now available through Muse Code and the Meta Model API. The model supports text, image, audio, video, and PDF inputs, outputs text, has a context window of about 1 million tokens, a maximum output of about 512,000 tokens, and charges $0.10 per million input tokens and $0.50 per million output tokens. Artificial Analysis testing shows that at its highest reasoning tier, a single task costs about $0.14, compared with about $0.41 for a similar task on Claude Haiku 5.5; Meta's published tests claim it leads Claude Haiku 5.5 and GPT-6 Luna on some agentic, tool-calling, and multimodal tasks, but scores lower than Claude Haiku 5.5 on FrontierCode 2.0.
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AI score 73
The original text provides pricing, context window, and third-party cost comparisons, and also points out discrepancies among different evaluation results, making it easier to judge the applicable boundaries of this small model.
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Meta将Muse Ember定位为旗下规模最小、运行效率最高的模型，主要面向AI智能体需要反复处理的高频任务，包括调用工具、执行编程子任务、请求路由、提取信息以及解析长文档。该模型能够接收文本、图片、音频、视频及PDF等多种输入形式，并以文本形式输出结果。
Muse Ember拥有约100万Token的上下文窗口，最高可输出约51.2万Token，可在一次请求中处理较大规模的代码库或文档集合。该模型现已通过Muse Code及Meta Model API上线，模型编号为“muse-ember-1.0”。
价格方面，Muse Ember每百万输入Token收费0.10美元，缓存输入收费0.004美元，每百万输出Token收费0.50美元。Artificial Analysis的测试显示，其最高推理档位单次任务成本约为0.14美元，Claude Haiku 5.5完成同类任务的成本约为0.41美元。
不过，不同评测项目的结果存在差异。Meta公布的测试显示，Muse Ember在部分智能体、工具调用和多模态任务中领先Claude Haiku 5.5及OpenAI的GPT-6 Luna；在考察代码修改能否被项目维护者采用的FrontierCode 2.0测试中，Muse Ember得分则低于Claude Haiku 5.5。