Muse Ember: Our smallest, most efficient model

	Muse Ember: Our smallest, most efficient model(research.meta.ai)
	330 points by dvaplima 2 days ago \| 140 comments
	tl;dr: Meta introduced Muse Ember, a small, efficient multimodal model aimed at high-volume agent tasks like tool calls, coding subtasks, extraction, and document parsing, with a 1M-token context and 512K output. It reportedly beats Claude Haiku 5.5 and GPT-6 Luna on intelligence-per-dollar, matching Haiku's top score at roughly a third of the cost. Available now via Meta Model API at $0.10/M input and $0.50/M output tokens, it's the first model trained using Muse Wonder Full.
	HN Discussion: ~Pelican SVG benchmark shows reasoning levels don't correlate with quality here ↓Disappointment that the 'smallest' model isn't released as open weights for local use •LLM market will fragment into specialized models like early PC software did ↓Model appears benchmaxxed, not actually frontier-quality in real-world use ↓Refusal to use Meta's cloud due to distrust of the company