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Meta 发布轻量模型 Muse Ember 输入价低至每百万 token 10 美分Simon Willison 博客T2·基座大模型

Meta 发布 Muse Ember 小模型 主打智能体高频任务

原标题：Introducing Our Smallest, Most Efficient Model: Muse Ember
72AI 研判分

Meta 推出旗下最小最高效模型 Muse Ember，定位与 Claude Haiku、GPT-6 Luna 同档，专为工具调用、编码子任务、路由、信息抽取与长文档解析等智能体高频重复工作设计。短提示词定价为每百万输入 0.10 美元、输出 0.50 美元，与两个竞品持平；但长上下文阶梯涨价更陡，超过 200k 后输入涨至 0.30 美元、输出 1.50 美元。

关联知识实体

模型Muse Ember模型Claude Haiku 5.5公司Meta AI模型GPT-6 Luna术语分层定价

报道正文

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 8/8/40.

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:

ModelPrompt sizeInput ($/M)Cached inputOutput
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.

Stacked bar chart from Artificial Analysis comparing a cost in dollars across six models, each bar split into four unlabeled stacked segments, sorted from lowest to highest total. Claude Haiku 5.5 (low): 0.04* total, bottom segment 0.02, three thin unlabeled segments above. GPT-6 Luna (non-reasoning): $0.07 total, bottom segment 0.06, thin unlabeled segments above. GPT-6 Luna (xhigh): 0.10 total, segments from bottom $0.07, $0.02, $0.02, plus a thin unlabeled top segment. Muse Ember (xhigh): $0.12 total, segments from bottom $0.06, $0.02, $0.02, plus a thin unlabeled top segment. Muse Ember (max): $0.14 total, segments from bottom $0.07, $0.03, $0.03, $0.02. Claude Haiku 5.5 (xhigh): 0.25* total, segments from bottom 0.14, $0.04, $0.05, $0.02. Anthropic bars are terracotta, OpenAI bars are dark gray, Meta bars are blue, each with the company logo beneath.

And even more useful than that is their Intelligence Index vs. Cost per Task chart, which shows the following for those three models:

Scatter plot from Artificial Analysis titled 'Intelligence Index vs. Cost per Task', plotting Artificial Analysis Intelligence Index (y-axis, 5 to 40) against cost per task in USD on a log scale (x-axis, $0.04 to $0.3), with a green 'most attractive quadrant' in the upper left and a dotted Pareto line. Claude Haiku 5.5 (low)* scores about 19 at roughly $0.044. GPT-6 Luna (non-reasoning) scores about 9.5 at roughly $0.07. GPT-6 Luna (xhigh) scores about 23.5 at roughly $0.10. Muse Ember (xhigh) scores about 34 at roughly $0.12 and Muse Ember (max) about 36 at roughly $0.14, both on the Pareto line. Claude Haiku 5.5 (xhigh)* scores about 34.5 at roughly $0.25, well to the right of the Pareto line. Three unlabeled gray dots also sit on the Pareto line.

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:

Bicycle frame is OK, basket has fish, pelican has a weird blue hat on.

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