Meta launched Muse Ember on Nov. 9, positioning it as the company's smallest and most efficient AI model yet, designed to handle repetitive AI agent tasks at approximately $0.14 per completed assignment. Rather than competing with larger models on complex reasoning challenges, Ember focuses on everyday operations that keep AI agents running, including extracting data from documents, executing tool calls, handling coding subtasks and automating business workflows. The model is built for workloads where AI agents repeatedly perform smaller tasks as part of a larger assignment, with Meta describing the division of labor simply: "Muse Wonder plans, Muse Ember handles the volume."
Ember's base pricing sits at $0.10 per million input tokens and $0.50 per million output tokens, matching rates from Anthropic's Claude Haiku 5.5 and OpenAI's GPT-6 Luna. However, those rates apply at different prompt lengths—Haiku's higher rates kick in above 100,000 tokens, while Ember's don't rise until prompts exceed 200,000 tokens. Beyond that threshold, Ember's rates climb to $0.30 per million input tokens and $1.50 per million output tokens. The model supports a 1-million-token context window, up to 512,000 output tokens, and accepts text, image, audio and video inputs. On Artificial Analysis' Intelligence Index, Ember scored 36 at maximum reasoning effort, matching Claude Haiku 5.5's score while costing approximately $0.14 per task compared to Haiku's $0.41 and GPT-6 Luna's $0.15 per task at a lower score of 28.
According to Meta, Ember is designed for scenarios where "an enterprise customer support agent might use a more capable model to establish how a particular complaint should be handled, then rely on Ember to apply those instructions across thousands of customer cases." In a testimonial published by Meta, Cloudflare CTO Dane Knecht reported up to 30% faster tail performance—the slower end of response times—against an unnamed comparable model. The report notes the results weren't universally in Ember's favor, with Claude performing better on FrontierCode 2.0, which evaluates whether engineering changes are suitable for merging.
The release could give channel partners another way to bring AI automation to customers without making inference costs a major barrier, the report states. Managed service providers, systems integrators and software resellers building AI-powered services could use Ember for repetitive backend operations while reserving larger models for tasks requiring more complex reasoning. Potential applications span customer support for processing large volumes of requests and updating CRM records, document management for reviewing contracts and checking compliance requirements, software development for running tests and handling routine fixes, and managed IT services for automating repetitive troubleshooting. For channel businesses, the opportunity extends beyond selling access to another AI model—partners could build managed automation services around it, combining model selection, integration, security controls and ongoing monitoring.
The report emphasizes that partners should evaluate accuracy, latency and reliability against customer workloads before committing production systems to Ember. A useful pilot should compare Ember with the model already handling the same work, using representative customer tasks and agreed success criteria including cost per completed workflow, tool-call accuracy, response times, and operational safeguards. According to the report, these results can help partners decide which tasks to assign to Ember and which require a more capable model or a person, with document extraction and routine record updates potentially needing different acceptance criteria from contract reviews or changes to production systems. Muse Ember is available through Meta Model API and Muse Code as muse-ember-1.0, with Google Cloud and Oracle Cloud also listed as access options, though availability and supported inputs vary by provider—Google Cloud's Ember documentation currently lists the model in Preview with text and image inputs only. The decision will ultimately hinge on whether models built for volume can reliably close the loop on customer workflows, and whether channel partners can capture margin by orchestrating the right model for each step rather than defaulting to one-size-fits-all reasoning power.