Meta launches its smallest model Muse Ember, matching Claude's performance at about one-third the cost
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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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 positions Muse Ember as its smallest and most efficient model, aimed primarily at the high-frequency tasks that AI agents need to handle repeatedly, including tool calling, executing coding subtasks, request routing, information extraction, and parsing long documents. The model can accept multiple input forms such as text, images, audio, video, and PDFs, and outputs results as text.
Muse Ember has a context window of about 1 million tokens and can output up to about 512,000 tokens, allowing it to process larger codebases or document collections in a single request. The model is now available through Muse Code and the Meta Model API under the model ID "muse-ember-1.0".
On pricing, Muse Ember charges $0.10 per million input tokens, $0.004 for cached input, and $0.50 per million output tokens. Tests by Artificial Analysis show that at its highest reasoning tier, a single task costs about $0.14, while Claude Haiku 5.5 costs about $0.41 to complete a comparable task.
However, results differ across evaluation projects. Meta's published tests show that Muse Ember leads Claude Haiku 5.5 and OpenAI's GPT-6 Luna on some agentic, tool-calling, and multimodal tasks; on the FrontierCode 2.0 test, which examines whether code changes can be adopted by project maintainers, Muse Ember scores lower than Claude Haiku 5.5.