Meta releases Muse Ember, a new lightweight model for agent execution

Meta released Muse Ember on Monday, November 9, 2026, describing it as its smallest and most efficient model and its lightest and most cost-efficient AI model yet. The model ID is muse-ember-1.0. It is available today on Muse Code and the Meta Model API, and Meta says it supports the Responses API. Meta AI reported that Ember is designed for complex reasoning execution and the work agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing.

The pricing is $0.10 per million input tokens, $0.004 per million cached input tokens, and $0.50 per million output tokens. It has a 1 million token context window and up to 512,000 output tokens. Meta AI said Ember delivers more intelligence per dollar than Claude Haiku 5.5 or GPT-6 Luna at any reasoning setting, matching Haiku's best score at about a third of the cost and beating every Luna setting. TechCrunch noted that at 10 cents per million input tokens, Ember is significantly cheaper than frontier competitors like Haiku and Luna, and cost-competitive with open-source competitors like DeepSeek V4.1 Flash and GLM 5.3 Flash.

Meta positioned Ember as part of a two-model pattern with Muse Wonder. Meta summarized the division of labor as “Muse Wonder plans, Muse Ember handles the volume.” TechCrunch gave the example of a customer-support agent where a heavier model establishes rules for handling an issue, then a lighter-weight model applies the rules across tens of thousands of cases. Meta AI said Ember is the first model trained using Muse Wonder Full and rounds out a portfolio that includes 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.

Meta AI said 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. Before release, Meta evaluated Ember under its Meta Superintelligence Scaling Framework, including chemical and biological, cybersecurity, and loss-of-control risks, and found it remained below the framework’s high-risk thresholds. Meta also evaluated how Ember behaves when acting on users’ behalf, covering user intent, sensitive information, hidden instructions, and seeking human input before high-impact actions.

Meta said it has even more efficient models in training and looks forward to sharing what is next.