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Nov 10Tue
  1. Hacker News · 300+

    Meta launches Muse Ember: its smallest and most efficient model

    Meta launches Muse Ember, its smallest and most efficient model designed for complex reasoning tasks, outperforming Claude Haiku 5.5 and GPT-6 Luna in smartness-to-cost ratio, with a cost of 0.10 USD per million input tokens and 0.50 USD per million output tokens, supporting a 1M context window and 512K output tokens.

    Same story · 4 outlets
  3. MarkTechPost

    Meta launches Muse Ember: a high-capacity intelligent model with 1M context

    Meta releases Muse Ember, its smallest and most efficient model, designed for repetitive steps in agents. Muse Ember scores 36 on the AA Intelligence Index, matching Claude Haiku 5.5 (max effort), with a cost of about 0.14 USD per task.

    Same story · 4 outlets
  4. ITHome · AI

    Meta launches lightweight AI model Muse Ember: benchmark scores comparable to Claude Haiku 5.5, with a single-task cost of just 1/3

    Muse Ember is Meta's smallest and most efficient AI model, performing comparably to Claude Haiku 5.5 on the Artificial Analysis rankings, but with a single-task cost of only 1/3 and surpassing GPT-6 Luna.

    Same story · 4 outlets
  5. The Decoder

    Meta releases Muse Ember: An ultra-small, highly efficient model designed for repetitive tasks of AI agents.

    Meta releases Muse Ember, its smallest and most efficient AI model to date, designed for handling the repetitive tasks of AI agents, such as calling tools, processing small-scale coding tasks, routing requests, extracting data, and processing long documents.

    Same story · 2 outlets
  6. Artificial Analysis Articles66

    Meta's latest model Muse Ember scores 36 on the AI Index, comparable to Claude Haiku 5.5 but with lower costs

    Muse Ember scores 36, comparable to Claude Haiku 5.5 but with a task cost of only 0.14 USD, two-thirds lower than the latter; its input/output token prices are the same as Claude Haiku 5.5 and GPT-6 Luna, but caching input costs are lower. It scored 68.1% on AutomationBench-AA, comparable to Muse Wonder, but has the lowest knowledge accuracy (21.1%)

    Same story · 4 outlets
  7. TechCrunch · AI72

    Meta releases lightweight model Muse Ember, highlighting agent execution and low cost.

    Meta Superintelligence Lab releases lightweight model Muse Ember, priced at 10 cents per million tokens, focusing on high-frequency agent tasks such as tool calling, coding subtasks, routing, extraction, and long document parsing.

    Same story · 2 outlets

    Why it matters: Meta uses a pricing of 10 cents per million input tokens and the Artificial Analysis comprehensive benchmark to place the lightweight model in the agent execution context, comparing it with Haiku and Luna.

Oct 19Mon
Oct 16Fri
  1. Baseten Blog

    Meta releases Muse Spark Open: an open-source weight model designed for code agents

    Meta releases Muse Spark Open, an open-source weight model with 816B parameters designed for code agents, featuring 42.7B active parameters, a 1M token context window, and full-modal input (text, image, video, audio), performance comparable to cutting-edge lab models, using a compressed MoE architecture and hybrid global/local attention mechanisms to optimize inference efficiency, reducing model size to 540GB via NVFP4 checkpoints.

Oct 12Mon
Jul 21Tue
  1. Meta AI Blog

    Meta AI's SAM 3 and DINOv3 models drive the first wave of progress for the Genesis mission project

    Meta AI's open-sourced SAM 3 and DINOv3 models achieve 3D semantic segmentation and reconstruction of scientific images within 15 minutes using 300 A100 GPUs on the NERSC supercomputer, helping the U.S. Department of Energy's Genesis mission project overcome the analytical bottleneck caused by data explosion after ALS upgrade, and has realized a 20-fold increase in monthly expert annotation efficiency in grapevine drought cell-level research.

Jul 9Thu
  1. Meta AI Blog

    Meta AI has released the Muse Spark 1.1 multimodal reasoning model.

    Muse Spark 1.1 is the latest multimodal reasoning model released by Meta Superintelligence Labs, significantly improving in tool and computer use, coding, and multimodal understanding, supporting million-token context windows. It is open to developers for public preview via the Meta Model API, surpassing Muse Spark and matching leading alternatives in Meta's internal coding evaluations.

Jul 7Tue