Meta发布轻量模型Muse Ember，对标Haiku与Luna
Introducing Our Smallest, Most Efficient Model: Muse Ember
Meta正式入局轻量级推理市场，直接对标Anthropic和OpenAI的入门模型，定价策略与性能权衡对Agent开发者极具参考价值。
Introducing Our Smallest, Most Efficient Model: Muse Ember
介绍我们最小、最高效的模型：Muse Ember
Meta AI just launched their entry in the Claude Haiku/GPT Luna category: Muse Ember, which they describe as:
Meta AI 刚刚推出了他们在 Claude Haiku/GPT Luna 类别中的参赛作品：Muse Ember，他们将其描述为：
[...] 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.
[...] 这是我们最小且最高效的模型，专为复杂的推理执行以及智能体全天重复的工作而设计：工具调用、编码子任务、路由、提取和长文档解析。
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/$40.
对于较短的提示词，其定价与 GPT-6 Luna 和 Claude Haiku 5.5 相同——两者的输入均为每百万 token 0.10 美元，输出为每百万 token 0.50 美元。今天发布的另一款大型模型 Claude Fable 5.2 的价格是前者的 80 倍，即 8 美元/40 美元。
The pricing is a little less straightforward as prompts get longer, though. Luna, Ember, and Haiku all support up to a million input tokens, but they also all have a threshold after which the price goes up. Taking that threshold into account Ember is priced between the two others:
不过，随着提示词变长，定价就变得不那么直接了。Luna、Ember 和 Haiku 都支持高达一百万个输入 token，但它们都有一个阈值，超过该阈值后价格会上涨。考虑到这一阈值，Ember 的定价介于另外两者之间：
| Model | Prompt size | Input ($/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 | $2.50 |
| 模型 | 提示词大小 | 输入 ($/M) | 缓存输入 | 输出 |
|---|---|---|---|---|
| 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.
但在当今时代，比较每 token 的价格其实意义不大，因为推理 token 的存在意味着不同的模型解决同一问题可能会使用数量截然不同的 token。
For that, one measure that's useful is the Artificial Analysis.
为此，一个有用的衡量指标是 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:
比这更有用的是他们的“智力指数 vs. 每项任务成本”图表，该图表显示了以下三款模型的情况：
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:
Muse Ember 模型正好位于那条帕累托前沿线上——即你只能通过牺牲另一个维度来提升一个维度的那条线。Meta 在他们的公告中特别指出了这一点：
It also sits on the efficiency frontier of the full Artificial Analysis leaderboard: no model scores higher for less per task.
它也位于整个 Artificial Analysis 排行榜的效率前沿上：没有模型能在每项任务成本更低的情况下获得更高的评分。
(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!)
(绿色区域中间帕累托线上的那个灰色点是 GPT-6.1 Terra (low) ——显然我应该更多地关注 Terra 系列！)
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.
Muse Ember 有六个推理级别——minimal（最低）、low（低）、medium（中）、high（高）、xhigh（极高）和 max（最大）——因此我渲染了六只骑自行车的鹈鹕。“high”推理级别未能返回完整的 SVG，而其他 SVG 令人惊讶之处在于，推理级别并没有清晰地映射到响应的视觉质量上。
Here's the "max" one, but the others are worth a look too:
这是“max”级别的，但其他的也值得一看：
The Ember pelicans are comparable in quality to those I got from Muse Wonder, which is 40x the price of Muse Ember.
Ember 鹈鹕的质量与我通过 Muse Wonder 获得的相当，而 Muse Wonder 的价格是 Muse Ember 的 40 倍。
Tags: ai, generative-ai, llms, meta, llm-pricing, llm-release, artificial-analysis, muse
标签：ai, generative-ai, llms, meta, llm-pricing, llm-release, artificial-analysis, muse
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