Meta Superintelligence Lab released a new AI model on Monday called Muse Ember, the lab’s lightest and most cost-efficient AI model yet. In its release, Meta said Ember was “designed for complex reasoning execution and the work agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing.” Meta particularly emphasized Ember’s cost-per-task, using Artificial Analysis’s composite benchmark score to show Ember as cheaper and smarter than competitors like Anthropic’s Haiku and OpenAI’s Luna. At 10 cents per million input tokens, Ember is significantly cheaper than frontier competitors like Haiku and Luna. Not only that, it’s cost-competitive with open-source competitors like DeepSeek V4.1 Flash and GLM 5.3 Flash. Given the emphasis on low cost and accuracy at scale, Ember is designed to pair with a larger model like Meta’s Muse Wonder in an agentic harness. In the case of a customer-support agent, for instance, you might see a heavier model establishing rules for how to handle a particular issue, then sending a lighter-weight model to apply the rules across tens of thousands of cases. As Meta puts it in the announcement, “Muse Wonder plans, Muse Ember handles the volume.” With volume-heavy use cases in mind, Meta also trained Ember to work well across different types of media, including text, images, and audio. The result makes the model surprisingly flexible in what kind of inputs it can handle. As Meta puts it in the announcement, “agents can read screenshots, charts, and scanned documents, or work from voice memos, meeting recordings, and video without a separate model.” Ember is the fourth model released under Meta’s Muse brand, following the mid-weight Spark (released in April), Spark’s open-source twin Glimmer (released in August), and the Wonder frontier model (released in October). The flagship consumer AI product, the Muse Agent, was released in September to unexpected success, with new features like Spaces and video chat rolling out earlier this month. When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence. Russell Brandom has been covering the tech industry since 2012, with a focus on platform policy and emerging technologies. He previously worked at The Verge and Rest of World, and has written for Wired, The Awl and MIT’s Technology Review. He can be reached at [email protected] or on Signal at 412-401-5489.
Research3 分钟阅读
Meta 发布 Muse Ember,一种新的轻量级代理执行模型
Ember 每百万输入代币 10 美分,比 Haiku 和 Luna 等前沿竞争对手便宜得多。
TechCrunch — AI作者: Russell Brandom2026年11月9日 (1小时前)
提供 4 种语言版本AI 摘要:要点
- Ember 每百万输入代币 10 美分,比 Haiku 和 Luna 等前沿竞争对手便宜得多。

全文为英语
内容来自原始来源: TechCrunch — AI
查看原文查看原文
相关文章
Anthropic 提出了 Claude Fable 5.2 和 Mythos 5.2:1000 个 AI 智能体在 30 年内形式化了费马定理的证明......
Anthropic 推出了克劳德寓言 5.2 和神话 5.2:1000 个 AI 代理在 30 小时内正式证明了费马定理。新模型在编程、数学、科学研究和网络安全方面获得了重大改进。我们设法做到的: - 数学:...
Telegram — AI Machine Learning Big Data
牛津大学发布了一本 180 页的教科书,介绍如何深入神经网络并理解它们的思维方式。
📚 牛津大学发布了一本 180 页的教科书,讲述如何深入神经网络并理解它们的思维方式。来自牛津大学和其他科研机构的研究人员发表了一篇关于机械可解释性及其...的大规模综述。
Telegram — AI Machine Learning Big Data
Anthropic斥资34亿美元收购燃气发电机
⚡ Anthropic斥资34亿美元收购燃气发电机 据英国《金融时报》报道,该公司从OnePWR Solutions截获了总容量为1.7吉瓦的卡特彼勒设备订单。已知信息: • 交易价值 34 亿美元。 • 这是已知最大的单笔购买...
Telegram — GPT Time | Нейросети | ИИ
谷歌教导人工智能代理寻找新的量子算法,并立即在精益中证明其正确性和成本。
谷歌教导人工智能代理寻找新的量子算法,并立即在精益中证明其正确性和成本。 DeepMind 和 Quantum 团队都致力于此。这项工作不同于过去人工智能代理通过数值模拟测试量子电路的方法......
Telegram — blockchainRF (блокчейн / мозг / space / WEB 3.0)