Today, we're introducing Muse Ember, our smallest and most efficient model designed for complex reasoning execution and the work agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing. Muse Ember delivers more intelligence per dollar than Claude Haiku 5.5 or GPT-6 Luna at any reasoning setting: it matches Haiku's best score at about a third of the cost and beats every Luna setting. It also sits on the efficiency frontier of the full Artificial Analysis leaderboard: no model scores higher for less per task.
It is available today on the Meta Model API, at $0.10 per million input tokens and $0.50 per million output tokens. 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.
In early testing, partners put Muse Ember to work on tasks agents repeat daily, from reviewing contracts to writing code. On Automation Bench-AA at max reasoning, Muse Ember scored 37.8%, compared to 20.2% for GPT-6 Luna and 13.9% for Claude Haiku 5.5. On Agents' Last Exam, it scored 61.6%, ahead of 57.1% and 57.0%.
A Box VP of AI Products praised its accuracy, speed, and token efficiency for scoring contracts against negotiation playbooks and screening candidates. Muse Ember is built as a fast, efficient foundation for agents doing real work on enterprise content.
Source: Hacker News · Summarized by HeadlinesBriefing