Meta launched Muse Ember (muse-ember-1.0) today, November 9, 2026 — its lightest and most affordable AI model to date. Available immediately through Muse Code and the Meta Model API, the model is designed specifically to execute repetitive subtasks within AI agent workflows, priced at $0.10 per million input tokens and $0.50 per million output tokens, according to Meta.
What Is Muse Ember and What Can It Do?
Muse Ember ships with a 1-million-token context window — large enough to fit an entire codebase or document set in a single call — and supports outputs of up to 512,000 tokens. Unlike many specialized models, Ember handles text, screenshots, charts, scanned documents, voice memos, meeting recordings, and video natively, without routing to a separate model for each file type. Meta positions it as the 'volume executor' in its ecosystem: Muse Wonder plans, Muse Ember executes. It is also the first model trained with Muse Wonder Full, Meta's new training system. The model weights are not publicly released (per TechCrunch and The New Stack).
"A model purpose-built for subtask volume is exactly what SMBs need: continuous, affordable automation without sacrificing reasoning capability at every step."
Davarion Group & LabsWhat Changes for SMBs
- 01Lower automation costs: at $0.10/M input tokens, processing thousands of documents, emails, or records in agent pipelines becomes operationally affordable compared to larger-scale models.
- 02Multimodality at no extra cost: reading scanned invoices, voice notes, or screenshots in the same workflow removes the need for separate OCR or transcription integrations.
- 03Long context for full documents: the 1M-token window enables analyzing entire contracts, CRM histories, or accounting records in a single API call.
- 04Weights are not open: Ember cannot be self-hosted and requires Meta's API, introducing vendor dependency and data-privacy considerations to evaluate.
- 05Recommended action: test Ember for classification, data extraction, and subtask execution steps in n8n or other automation platforms where cost-per-call matters more than peak model power.
On performance, reporting from The New Stack and The Next Web indicates Muse Ember matches Claude Haiku 5.5 on most tasks, though Haiku still leads on at least one key coding benchmark. These comparisons are based on benchmarks Meta itself published, so it is worth validating on your specific use case before migrating production workflows. For classification, summarization, entity extraction, and simple orchestration steps, the pricing makes it a competitive option.
At Davarion Group & Labs, we help Houston businesses evaluate which model fits each step of their automation pipeline — integrating Muse Ember, Claude Haiku, GPT, or other models based on the cost-performance balance each process requires. If you want to find out whether Ember can reduce the cost of your existing agents, reach out for a no-commitment assessment.
