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
| 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
| 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
| 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
| 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
| 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
| 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
| 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
| 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
| 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
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