Meta 发布 Muse Ember：主打智能体高频任务的小型推理模型
Meta 这次没做大模型，做了个专给智能体干脏活的小模型，缓存输入只要 $0.004，便宜到几乎白送。用 Artificial Analysis 的图看，同样的任务成本它最省。跑 Agent、做批量文档抽取的团队可以重点看看定价表那一栏。
Meta AI 发布 Muse Ember，定位为旗下最小、最省的模型，专攻智能体全天重复执行的任务：工具调用、编码子任务、路由、信息抽取和长文档解析。短提示下价格与 GPT-6 Luna 和 Claude Haiku 5.5 持平，输入 $0.10/百万 token、输出 $0.50/百万 token；对比同日发布的 Claude Fable 5.2（$8/$40）便宜 80 倍。
长提示的定价结构三者不同：Ember 在 ≤200k token 时缓存输入 $0.004/百万，超过后输出涨到 $1.50；Haiku 5.5 阈值是 100k，超过后输出 $2.50；Luna 阈值是 272k。由于推理 token 消耗差异大，单纯比单价意义有限。按 Artificial Analysis 的 Intelligence Index vs. Cost per Task 图，Ember 正好落在帕累托前沿上，Meta 称其为整个榜单的效率前沿：没有模型能以更低成本拿到更高分。Simon Willison 实测了 Ember 的六档推理级别（minimal 到 max），发现推理档位与输出质量并非严格对应，且 max 档的 SVG 生成质量与比它贵 40 倍的 Muse Wonder 相当。
Introducing Our Smallest, Most Efficient Model: Muse Ember Meta AI just launched their entry in the Claude Haiku/GPT Luna category: Muse Ember, which they describe as: [...] our smallest and most efficient model designed for complex reasoning execution and the work agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing. For shorter prompts it's priced the same as GPT-6 Luna and Claude Haiku 5.5 - both are $0.10/million input tokens and $0.50/million output. Today's other big model release, Claude Fable 5.2, is 80x that price at $8/$40. The pricing is a little less straightforward as prompts get longer, though. Luna, Ember, and Haiku all support up to a million input tokens, but they also all have a threshold after which the price goes up. Taking that threshold into account Ember is priced between the two others: Model Prompt size Input ($/M) Cached input Output GPT-6 Luna ≤272k $0.10 $0.01 $0.50 >272k $0.20 $0.02 $0.75 Muse Ember 1.0 ≤200k $0.10 $0.004 $0.50 >200k $0.30 $0.012 $1.50 Claude Haiku 5.5 ≤100k $0.10 $0.01 $0.50 >100k $0.50 $0.05 $2.50 But comparing price-per-token doesn't really make sense these days, when reasoning tokens mean that different models might use dramatically different numbers of tokens to solve the same problem. For that, one measure that's useful is the Artificial Analysis. And even more useful than that is their Intelligence Index vs. Cost per Task chart, which shows the following for those three models: The Muse Ember models sit right on that Pareto line - the line at which you can only improve one dimension by giving up another. Meta call this out in their announcement: It also sits on the efficiency frontier of the full Artificial Analysis leaderboard: no model scores higher for less per task. (That grey dot on the Pareto line in the middle of the green area is GPT-6.1 Terra (low) - clearly I should be paying more attention to the Terra family!) Muse Ember has six reasoning levels - minimal, low, medium, high, xhigh, and max - so I rendered six pelicans riding bicycles . The "high" reasoning level failed to return a full SVG, and the other SVGs were surprising in that the reasoning level didn't cleanly map to the visual quality of the response. Here's the "max" one, but the others are worth a look too: The Ember pelicans are comparable in quality to those I got from Muse Wonder , which is 40x the price of Muse Ember. Tags: ai , generative-ai , llms , meta , llm-pricing , llm-release , artificial-analysis , muse
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, Meta, Claude Haiku, GPT-6 Luna.
Source attribution: curated from Simon Willison’s Weblog.
