Muse Ember
Muse Ember is our smallest multimodal model built for high-volume agentic workflow execution, from coding subtasks and tool calls to document summarization and extraction.Meet Muse Ember
Introducing Muse Ember
Muse Ember is our smallest reasoning model built for the work agents repeat all day: tool calls, coding subtasks, routing, extraction and long documents.
BOX
“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.”
YASHODHA BHAVNANI
VP of AI Products
Box
CLINE
“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.”
SAOUD RIZWAN
Founder
Cline
ORACLE
“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.”
KONSTANTINOS PAPAMILTIADIS (KP)
SVP, Global Partnerships
Oracle
CLOUDFLARE
“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.”
DANE KNECHT
CTO
Cloudflare
you.com
“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.”
CHAK POTHINA
Inference Lead
You.com
Muse Ember benchmarks
Muse Ember (max)
Claude Haiku 5.5 (max)
GPT-6 Luna (max)
Agents
AutomationBench-AA
E2E business workflows
37.8
13.9
20.2
Agents' Last Exam
End-to-end agent tasks
61.6
57.0
57.1
Personal Superintelligence Index
Internal composite index of personal and knowledge work
46.8
36.1
37.0
Coding
Terminal-Bench 4.0
Agentic terminal coding, best published harness
40.9
39.2
16.4
Coding Agent Index-AA
Coding in real agent harnesses
46
37
31
FrontierCode 2.0-AA
Real engineering tasks judged on mergeability
27.2
34.9
25.2
Multimodal
GDP.pdf-AA
Professional work over long documents
22.8
20.8
22.8
OSWorld 2.1
Computer use across desktop apps
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
80.6
78.6
62.8
General
Artificial Analysis Index
36
36
28