Muse Ember 发布：主打 Agent 高频重复任务的低成本小模型
Alexandr Wang 团队发了 Muse Ember，专门给 Agent 跑重复任务用，号称比 Claude Haiku 和 Luna 便宜三分之一还能打。如果你的 Agent 每天跑大量分类抽取调用，这价格差距算下来很实在，做成本优化的工程团队可以看下。
Muse Ember 是一款面向 Agent 日常重复工作场景的小型高效模型，发布方称其在任意推理档位下，单位成本获得的智能水平高于 Claude Haiku、Luna 以及其他各家的 flash 类模型。
价格方面，其成本降至对比模型的约三分之一或更低。该模型的定位是用更低费用承接 Agent 全天高频调用的任务，而非追求单次推理的极限能力。
这类小型低价模型通常承担分类、抽取、格式化、工具调用等标准化工作，让大模型只在复杂步骤中启用。对大量部署 Agent 的团队来说，按调用次数计费的开销往往集中在这些简单步骤上，模型单价直接决定整体运行成本。
1/ announcing our smallest, most efficient little model is here: muse ember 🔥
built for work that agents repeat all day, it gets you more intelligence per dollar than Haiku, Luna, and all other flash models at any reasoning setting and does it at a third of the cost or less! https://t.co/ClmgPOTrsR
This report is categorized under AITOP's "Models" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to US-China foundation model benchmarks and commercialization comparison and AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Muse Ember, Claude Haiku, 推理模型, 低成本模型.
Source attribution: curated from Alexandr Wang (Meta MSL) (@alexandr_wang).
