GoKawiilMuse launches Ember, a low-cost model aimed at agent workloads
Muse has released Ember, described as its smallest and most efficient model, built for tool calls, coding subtasks, routing, extraction and long document parsing. According to benchmarking data cited by the company, Ember matches Claude Haiku 5.5's top score at roughly a third of the cost and outperforms GPT-6 Luna at every reasoning setting tested.
GoKawiil's interpretation of the reporting above, not reported fact.
The comparison, based on Artificial Analysis Intelligence Index results against cost per task, suggests Ember could offer developers a cheaper option for high-volume agent tasks without sacrificing much capability. If the cost-to-performance figures hold up under independent testing, it could pressure rivals like Anthropic and OpenAI to adjust pricing for their smaller, task-oriented models.
- Muse Ember is positioned as the company's smallest, most efficient model for repetitive agent tasks.
- It reportedly matches Claude Haiku 5.5's best benchmark score at about one-third the cost.
- It beats GPT-6 Luna across all tested reasoning settings on the Artificial Analysis Intelligence Index.
Source: research.meta.ai — Meta Ai Research, 2026-11-09
Published there as: “Our Smallest, Most Efficient Model: Muse Ember”
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