AI

Introducing Our Smallest, Most Efficient Model: Muse Ember

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 10/11/2026, 07:42:24 UTC.

Briefings

Meta AI launched the Muse Ember model, the smallest and most efficient in the Claude Haiku/GPT Luna category, priced at $0.10 per million input tokens. It supports up to 200k input tokens with a 200k threshold where pricing increases, making it significantly cheaper than competitors like GPT-6 Luna and Claude Haiku 5.5. The model features six reasoning levels and was positioned on the Artificial Analysis efficiency frontier, offering high reasoning capability at low cost. However, the evidence does not specify the model's release date, the specific number of reasoning levels, or the exact quality of the generated SVGs, leaving those details unverified.

Key points

• Meta AI launched the Muse Ember model, the smallest and most efficient in the Claude Haiku/GPT Luna category, priced at $0.10 per million input tokens.
• Muse Ember supports up to 200k input tokens with a 200k threshold where pricing increases, making it significantly cheaper than competitors like GPT-6 Luna and Claude Haiku 5.5.

Context

Meta AI released the Muse Ember model on November 10, 2026, positioning it as the smallest and most efficient model in the Claude Haiku/GPT Luna category. The pricing structure is complex, with costs varying based on prompt size and reasoning levels, making direct price comparisons difficult without considering token usage efficiency.

Limitations

The provided evidence does not specify the exact release date of the Muse Ember model, the precise number of reasoning levels, or the specific quality of the generated SVGs. Additionally, the evidence does not confirm the model's availability in a specific city or region, nor does it provide details on the Artificial Analysis leaderboard performance beyond the stated efficiency frontier position.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 10/11/2026, 07:46:20 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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