Our Smallest, Most Efficient Model: Muse Ember

Meta released Muse Ember, its smallest and most efficient model, available today on the Meta Model API at $0.10 per million input tokens and $0.50 per million output tokens. Meta said Muse Ember matches Claude Haiku 5.5's best Artificial Analysis Intelligence Index score (36) at roughly a third of the cost per task and beats every GPT-6 Luna setting, with a 1M-token context window and up to 512K output tokens. On agent benchmarks at max reasoning, Muse Ember scored 37.8 on AutomationBench-AAE2E business workflows versus 13.9 for Claude Haiku 5.5 and 20.2 for GPT-6 Luna, and 61.6 on Agents' Last Exam versus 57.0 and 57.1 respectively.

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 | | | |---|---|---| | Series | Measurement | Value | |---|---|---| | Muse Ember | Cost Per Task USD | $0.116 | | Muse Ember | Score | 33.9 | | Muse Ember | Cost Per Task USD | $0.136 | | Muse Ember | Score | 35.8 | | Claude Haiku 5.5 | Cost Per Task USD | $0.092 | | Claude Haiku 5.5 | Score | 24.6 | | Claude Haiku 5.5 | Cost Per Task USD | $0.159 | | Claude Haiku 5.5 | Score | 29.6 | | Claude Haiku 5.5 | Cost Per Task USD | $0.248 | | Claude Haiku 5.5 | Score | 34.3 | | Claude Haiku 5.5 | Cost Per Task USD | $0.411 | | Claude Haiku 5.5 | Score | 36.2 | | GPT-6 Luna | Cost Per Task USD | $0.073 | | GPT-6 Luna | Score | 21.4 | | GPT-6 Luna | Cost Per Task USD | $0.104 | | GPT-6 Luna | Score | 23.3 | | GPT-6 Luna | Cost Per Task USD | $0.152 | | GPT-6 Luna | Score | 27.8 | 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 results | | Featured model. Muse Ember max | Claude Haiku 5.5 max | GPT-6 Luna max | |---|---|---|---|---| | Agents | AutomationBench-AAE2E business workflows | Best result. 37.8 | 13.9 | 20.2 | | | Agents' Last ExamEnd-to-end agent tasks | Best result. 61.6 | 57.0 | 57.1 | | | Personal Superintelligence IndexInternal composite index of personal and knowledge work | Best result. 46.8 | 36.1 | 37.0 | | Coding | Terminal-Bench 4.0Agentic terminal coding, best published harness | Best result. 40.9 | 39.2 | 16.4 | | | Coding Agent Index-AACoding in real agent harnesses | Best result. 46 | 37 | 31 | | | FrontierCode 2.0-AAReal engineering tasks judged on mergeability | 27.2 | Best result. 34.9 | 25.2 | | Multimodal | GDP.pdf-AAProfessional work over long documents | Best result. 22.8 | 20.8 | Best result. 22.8 | | | OSWorld 2.1Computer use across desktop apps | Best 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 perception | Best result. 80.6 | 78.6 | 62.8 | | General | Artificial Analysis Index | Best result. 36 | Best result. 36 | 28 | For more details about our evaluations, see our report https://research.meta.ai/static/muse-ember-evaluation-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. 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 | | | |---|---|---| | Series | Measurement | Value | |---|---|---| | Muse Ember max | Model | Muse Ember max | | Muse Ember max | Score | 37.8% | | GPT-6 Luna max | Model | GPT-6 Luna max | | GPT-6 Luna max | Score | 20.2% | | Claude Haiku 5.5 max | Model | Claude Haiku 5.5 max | | Claude Haiku 5.5 max | Score | 13.9% | 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 | | | |---|---|---| | Series | Measurement | Value | |---|---|---| | Muse Ember max | Model | Muse Ember max | | Muse Ember max | Score | 61.6% | | GPT-6 Luna max | Model | GPT-6 Luna max | | GPT-6 Luna max | Score | 57.1% | | Claude Haiku 5.5 max | Model | Claude Haiku 5.5 max | | Claude Haiku 5.5 max | Score | 57.0% | 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 | | | |---|---|---| | Series | Measurement | Value | |---|---|---| | Muse Ember max | Model | Muse Ember max | | Muse Ember max | Score | 46.8% | | GPT-6 Luna max | Model | GPT-6 Luna max | | GPT-6 Luna max | Score | 37.0% | | Claude Haiku 5.5 max | Model | Claude Haiku 5.5 max | | Claude Haiku 5.5 max | Score | 36.1% | 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 | | | |---|---|---| | Series | Measurement | Value | |---|---|---| | Muse Ember max | Model | Muse Ember max | | Muse Ember max | Score | 40.9% | | Claude Haiku 5.5 max | Model | Claude Haiku 5.5 max | | Claude Haiku 5.5 max | Score | 39.2% | | GPT-6 Luna max | Model | GPT-6 Luna max | | GPT-6 Luna max | Score | 16.4% | 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 | | | |---|---|---| | Series | Measurement | Value | |---|---|---| | Muse Ember max | Model | Muse Ember max | | Muse Ember max | Score | 46 | | Claude Haiku 5.5 max | Model | Claude Haiku 5.5 max | | Claude Haiku 5.5 max | Score | 37 | | GPT-6 Luna max | Model | GPT-6 Luna max | | GPT-6 Luna max | Score | 31 | 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 | | | |---|---|---| | Series | Measurement | Value | |---|---|---| | Claude Haiku 5.5 max | Model | Claude Haiku 5.5 max | | Claude Haiku 5.5 max | Score | 34.9% | | Muse Ember max | Model | Muse Ember max | | Muse Ember max | Score | 27.2% | | GPT-6 Luna max | Model | GPT-6 Luna max | | GPT-6 Luna max | Score | 25.2% | 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 | | | |---|---|---| | Series | Measurement | Value | |---|---|---| | Muse Ember max | Model | Muse Ember max | | Muse Ember max | Score | 22.8% | | GPT-6 Luna max | Model | GPT-6 Luna max | | GPT-6 Luna max | Score | 22.8% | | Claude Haiku 5.5 max | Model | Claude Haiku 5.5 max | | Claude Haiku 5.5 max | Score | 20.8% | 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 | | | |---|---|---| | Series | Measurement | Value | |---|---|---| | Muse Ember max | Model | Muse Ember max | | Muse Ember max | Score | 69.5% | | Claude Haiku 5.5 max | Model | Claude Haiku 5.5 max | | Claude Haiku 5.5 max | Score | 68.9% | | GPT-6 Luna max | Model | GPT-6 Luna max | | GPT-6 Luna max | Score | 50.5% | Meta internal evaluation. Partial-credit reward. PerceptionBench PerceptionBench results at max reasoning, reproduced from the doc's current chart.. Score | PerceptionBench: PerceptionBench | | | |---|---|---| | Series | Measurement | Value | |---|---|---| | Muse Ember max | Model | Muse Ember max | | Muse Ember max | Score | 80.6% | | Claude Haiku 5.5 max | Model | Claude Haiku 5.5 max | | Claude Haiku 5.5 max | Score | 78.6% | | GPT-6 Luna max | Model | GPT-6 Luna max | | GPT-6 Luna max | Score | 62.8% | 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 Analysis | Featured model. Muse Ember max | Featured model. Muse Spark 1.3 max | |---|---|---| | Intelligence Index | 36 | Best result. 37 | | Cost Per Intelligence Index Task | Best result. $0.14 | $2.39 | | Price Per 1M Input / Output Tokens | Best 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 https://dev.meta.ai