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.
Artificial Analysis | November 9, 2026. Each point is one reasoning-effort setting. Muse Ember cost at list pricing.
Muse Ember evaluation results
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.
“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.”
“It's incredible seeing Meta release a lightweight model for developers who want a fast, low-cost model they can rely on, and for teams cutting costs by running it as the subagent in their harness. Muse Ember is purpose-built for, and excels at, subagent work like coding and document analysis.”
“Muse Ember showed an encouraging ability to tackle complex engineering tasks in our early testing. We see strong potential for it to become a valuable tool for engineering teams.”
“What stood out in our testing was speed. Muse Ember ran up to 30% faster at the tail than the comparable model we tested, and it handled broad, multi-repo investigation tasks well. It’s a strong fit for fast, high-throughput agentic workloads.”
“Muse Ember completed every coding quest in our testing with the lowest latency of any model we tested: a p95 of 2.2 seconds, about 4x to 13x faster than leading open-weight models, and with 36–48% fewer output tokens. We’re excited about what Meta Superintelligence Labs is building next and about bringing it to our customers.”
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
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
Personal Superintelligence Index
Personal Superintelligence Index results at max reasoning, reproduced from the doc's current chart.. Score
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
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
FrontierCode 2.0-AA
FrontierCode 2.0-AA results at max reasoning, reproduced from the doc's current chart.. Score
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
OSWorld 2.1
OSWorld 2.1 results at max reasoning, reproduced from the doc's current chart.. Score
Meta internal evaluation. Partial-credit reward.
PerceptionBench
PerceptionBench results at max reasoning, reproduced from the doc's current chart.. Score
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
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. Muse Ember, Muse Spark and Muse Wonder are now available on Google Cloud and Oracle Cloud.