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Meta AI:Research Blog·· 12 小时前精选AI 评分73

Meta 发布最小最高效模型 Muse Ember,主打智能体高频任务

Introducing Our Smallest, Most Efficient Model: Muse Ember

AI 导读

Meta 发布 Muse Ember(muse-ember-1.0),定位最小最高效的复杂推理执行模型,面向工具调用、编码子任务、路由、抽取和长文档解析等智能体日常重复任务。

推荐理由

官方给出与 Claude Haiku 5.5 和 GPT-6 Luna 的逐项成本与基准对比,读者可据此评估智能体高频任务的性价比选型。

正文 · 原文

Today, we're introducing Muse Ember, 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.

Muse Ember delivers more intelligence per dollar than Claude Haiku 5.5 or GPT-6 Luna at any reasoning setting: it matches Haiku's best score at about a third of the cost and beats every Luna setting. It also sits on the efficiency frontier of the full Artificial Analysis leaderboard: no model scores higher for less per task.

Intelligence vs. Cost Per Task

Artificial Analysis Intelligence Index against cost per task at each reasoning effort, from the raw Artificial Analysis results. Muse Ember costs are estimates at proposed pricing.

  • xhigh
  • max
  • medium
  • high
  • xhigh
  • max
  • high
  • xhigh
  • max
  • Muse Ember
  • Claude Haiku 5.5
  • GPT-6 Luna
Intelligence vs. Cost Per Task: Intelligence vs. Cost Per Task
SeriesMeasurementValue
Muse EmberCost Per Task (USD)$0.116
Muse EmberScore33.9
Muse EmberCost Per Task (USD)$0.136
Muse EmberScore35.8
Claude Haiku 5.5Cost Per Task (USD)$0.092
Claude Haiku 5.5Score24.6
Claude Haiku 5.5Cost Per Task (USD)$0.159
Claude Haiku 5.5Score29.6
Claude Haiku 5.5Cost Per Task (USD)$0.248
Claude Haiku 5.5Score34.3
Claude Haiku 5.5Cost Per Task (USD)$0.411
Claude Haiku 5.5Score36.2
GPT-6 LunaCost Per Task (USD)$0.073
GPT-6 LunaScore21.4
GPT-6 LunaCost Per Task (USD)$0.104
GPT-6 LunaScore23.3
GPT-6 LunaCost Per Task (USD)$0.152
GPT-6 LunaScore27.8

Artificial Analysis | November 9, 2026. Each point is one reasoning-effort setting. Muse Ember cost at list pricing.

Muse Ember evaluation results

Muse Ember evaluation results: Evaluation results
Evaluation resultsFeatured model. Muse Ember (max)Claude Haiku 5.5 (max)GPT-6 Luna (max)
AgentsAutomationBench-AAE2E business workflowsBest result. 37.813.920.2
Agents' Last ExamEnd-to-end agent tasksBest result. 61.657.057.1
Personal Superintelligence IndexInternal composite index of personal and knowledge workBest result. 46.836.137.0
CodingTerminal-Bench 4.0Agentic terminal coding, best published harnessBest result. 40.939.216.4
Coding Agent Index-AACoding in real agent harnessesBest result. 463731
FrontierCode 2.0-AAReal engineering tasks judged on mergeability27.2Best result. 34.925.2
MultimodalGDP.pdf-AAProfessional work over long documentsBest result. 22.820.8Best result. 22.8
OSWorld 2.1Computer use across desktop appsBest result. 69.5(partial)33.8(binary)68.9(partial)27.8(binary)50.5(partial)17.0(binary)
PerceptionBench (w/ tools)Fine-grained visual perceptionBest result. 80.678.662.8
GeneralArtificial Analysis IndexBest result. 36Best result. 3628

For more details about our evaluations, see our report.

Muse Ember is available today on the Meta Model API, at $0.10 per million input tokens and $0.50 per million output tokens.

In early testing, our partners put Muse Ember to work on the kinds of tasks agents repeat all day, from reviewing contracts to writing code.

Box logo

“On the Box AI Complex Work Eval, Muse Ember delivers impressive accuracy, speed and token efficiency. It can score a contract against a company's negotiation playbook clause by clause and land on the right overall risk rating, or screen candidates against a detailed brief and flag the borderline ones who still qualify as alternates. Muse Ember is a fast, efficient foundation for agents doing real work on enterprise content.”

Author
Yashodha Bhavnani
Role
VP of AI Products
Company
Box

Agents

Agents spend most of their tokens on many small, structured steps, and Muse Ember is built for exactly that. It can carry a business workflow end to end, pulling records, calling tools in sequence, and updating systems across apps.

With a 1M-token context window and up to 512K output tokens, it can take in a full codebase or document set in a single call.

AutomationBench-AA

AutomationBench-AA results at max reasoning, reproduced from the doc's current chart.. Score

AutomationBench-AA: AutomationBench-AA
SeriesMeasurementValue
Muse Ember (max)ModelMuse Ember (max)
Muse Ember (max)Score37.8%
GPT-6 Luna (max)ModelGPT-6 Luna (max)
GPT-6 Luna (max)Score20.2%
Claude Haiku 5.5 (max)ModelClaude Haiku 5.5 (max)
Claude Haiku 5.5 (max)Score13.9%

Artificial Analysis | November 9, 2026. Strict pass@1.

Agents' Last Exam

Agents' Last Exam results at max reasoning, reproduced from the doc's current chart.. Score

Agents' Last Exam: Agents' Last Exam
SeriesMeasurementValue
Muse Ember (max)ModelMuse Ember (max)
Muse Ember (max)Score61.6%
GPT-6 Luna (max)ModelGPT-6 Luna (max)
GPT-6 Luna (max)Score57.1%
Claude Haiku 5.5 (max)ModelClaude Haiku 5.5 (max)
Claude Haiku 5.5 (max)Score57.0%

Meta internal evaluation. Linux subset.

Personal Superintelligence Index

Personal Superintelligence Index results at max reasoning, reproduced from the doc's current chart.. Score

Personal Superintelligence Index: Personal Superintelligence Index
SeriesMeasurementValue
Muse Ember (max)ModelMuse Ember (max)
Muse Ember (max)Score46.8%
GPT-6 Luna (max)ModelGPT-6 Luna (max)
GPT-6 Luna (max)Score37.0%
Claude Haiku 5.5 (max)ModelClaude Haiku 5.5 (max)
Claude Haiku 5.5 (max)Score36.1%

Internal composite index of personal and knowledge work.

Coding

Muse Ember is built for the coding subtasks an agent sends off hundreds of times a session: reading files, running tests, making targeted fixes.

Terminal-Bench 4.0

Terminal-Bench 4.0 results at max reasoning, reproduced from the doc's current chart.. Score

Terminal-Bench 4.0: Terminal-Bench 4.0
SeriesMeasurementValue
Muse Ember (max)ModelMuse Ember (max)
Muse Ember (max)Score40.9%
Claude Haiku 5.5 (max)ModelClaude Haiku 5.5 (max)
Claude Haiku 5.5 (max)Score39.2%
GPT-6 Luna (max)ModelGPT-6 Luna (max)
GPT-6 Luna (max)Score16.4%

Each model in its best published harness. See footnote 1.

Coding Agent Index-AA

Coding Agent Index-AA results at max reasoning, reproduced from the doc's current chart.. Score

Coding Agent Index-AA: Coding Agent Index-AA
SeriesMeasurementValue
Muse Ember (max)ModelMuse Ember (max)
Muse Ember (max)Score46
Claude Haiku 5.5 (max)ModelClaude Haiku 5.5 (max)
Claude Haiku 5.5 (max)Score37
GPT-6 Luna (max)ModelGPT-6 Luna (max)
GPT-6 Luna (max)Score31

Artificial Analysis | November 9, 2026

FrontierCode 2.0-AA

FrontierCode 2.0-AA results at max reasoning, reproduced from the doc's current chart.. Score

FrontierCode 2.0-AA: FrontierCode 2.0-AA
SeriesMeasurementValue
Claude Haiku 5.5 (max)ModelClaude Haiku 5.5 (max)
Claude Haiku 5.5 (max)Score34.9%
Muse Ember (max)ModelMuse Ember (max)
Muse Ember (max)Score27.2%
GPT-6 Luna (max)ModelGPT-6 Luna (max)
GPT-6 Luna (max)Score25.2%

Artificial Analysis | November 9, 2026

For the hardest problems, pair it with Muse Wonder: Muse Wonder plans, Muse Ember handles the volume.

Multimodal

Muse Ember natively understands text, images, audio, and video, so agents can read screenshots, charts, and scanned documents, or work from voice memos, meeting recordings, and video without a separate model. It can work through long real-world documents, operate a computer directly from screenshots, and pick out fine visual detail.

GDP.pdf-AA

GDP.pdf-AA results at max reasoning, reproduced from the doc's current chart.. Score

GDP.pdf-AA: GDP.pdf-AA
SeriesMeasurementValue
Muse Ember (max)ModelMuse Ember (max)
Muse Ember (max)Score22.8%
GPT-6 Luna (max)ModelGPT-6 Luna (max)
GPT-6 Luna (max)Score22.8%
Claude Haiku 5.5 (max)ModelClaude Haiku 5.5 (max)
Claude Haiku 5.5 (max)Score20.8%

Artificial Analysis | November 9, 2026

OSWorld 2.1

OSWorld 2.1 results at max reasoning, reproduced from the doc's current chart.. Score

OSWorld 2.1: OSWorld 2.1
SeriesMeasurementValue
Muse Ember (max)ModelMuse Ember (max)
Muse Ember (max)Score69.5%
Claude Haiku 5.5 (max)ModelClaude Haiku 5.5 (max)
Claude Haiku 5.5 (max)Score68.9%
GPT-6 Luna (max)ModelGPT-6 Luna (max)
GPT-6 Luna (max)Score50.5%

Meta internal evaluation. Partial-credit reward.

PerceptionBench

PerceptionBench results at max reasoning, reproduced from the doc's current chart.. Score

PerceptionBench: PerceptionBench
SeriesMeasurementValue
Muse Ember (max)ModelMuse Ember (max)
Muse Ember (max)Score80.6%
Claude Haiku 5.5 (max)ModelClaude Haiku 5.5 (max)
Claude Haiku 5.5 (max)Score78.6%
GPT-6 Luna (max)ModelGPT-6 Luna (max)
GPT-6 Luna (max)Score62.8%

Meta internal evaluation. With tools.

Safety

Before release, we evaluated Muse Ember under our Meta Superintelligence Scaling Framework, including chemical and biological, cybersecurity, and loss-of-control risks. Across the categories assessed, our evaluations found that Muse Ember remained below the framework’s high-risk thresholds. Though it’s a small model, it received the same core safety training and testing. We also evaluated how Muse Ember behaves when acting on users' behalf. That covers whether it respects user intent, handles sensitive information appropriately, resists instructions hidden in web pages, documents, or tool outputs that try to redirect it, and seeks human input before high-impact actions.

Availability

Muse Ember is available today on Muse Code and Meta Model API as muse-ember-1.0. It costs $0.10 per million input tokens, $0.004 per million cached input tokens and $0.50 per million output tokens, with a 1M-token context window and up to 512K output tokens. Muse Ember supports the Responses API.

When we introduced Muse Wonder and Muse Wonder Full, we said we would use them to develop specialized models we can bring to everyone. Muse Ember is the first trained using Muse Wonder Full. It rounds out our portfolio of models: Muse Wonder for the hardest problems, Muse Spark for coding and agentic workflows, and Muse Ember for repeatable coding and agentic tasks at scale at about $0.14 per Artificial Analysis task, a cost low enough to run in every product, for everyone.

Muse Ember and Muse Spark 1.3 comparison

Muse Ember and Muse Spark 1.3 comparison: Artificial Analysis comparison
Artificial AnalysisFeatured model. Muse Ember (max)Featured model. Muse Spark 1.3 (max)
Intelligence Index36Best result. 37
Cost Per Intelligence Index TaskBest result. $0.14$2.39
Price Per 1M Input / Output TokensBest result. $0.10(input)$0.50(output)$1.25(input)$4.25(output)

Our goal is personal superintelligence: models that help you pursue your goals, create what you imagine, deepen your relationships, and take action on what you value most. Bringing it to everyone takes more than frontier capability. It takes frontier capability at a price that scales. We have even more efficient models in training and look forward to sharing what's next.

Try Muse Ember today

来源:Meta AI:Research Blog · research.meta.ai