Credit: Meta Meta has released Muse Ember, its smallest and cheapest AI model so far, the company announced on Monday. Meta built it for the small jobs AI agents repeat all day. These include calling tools, coding subtasks, routing requests, pulling out data and parsing long documents. Ember costs $0.10 per million input tokens and $0.50 per million output tokens.<\/p>\n
Its two closest rivals, Anthropic\u2019s Claude Haiku 5.5 and OpenAI\u2019s GPT-6 Luna, have the same list prices. Meta says Ember gets more done for each dollar than either of them, at every reasoning setting. Chief executive Mark Zuckerberg announced the launch on X. Releasing Muse Ember today \u2014 our smallest model that delivers higher intelligence per dollar than others in its class.<\/p>\n
Built for token-heavy work that agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing. Available today on the Meta Model API and in Muse Code. \u2013 Mark Zuckerberg (@finkd) November 9, 2026 A third of the cost per task The cost claim rests on the Intelligence Index from Artificial Analysis, an independent benchmarking firm. Ember scores 36 on its highest setting, the same as Haiku 5.5 on its highest setting. Luna scores 28.<\/p>\n
Running the index cost about $0.14 per task with Ember, according to Meta\u2019s figures. It cost $0.41 for Haiku and $0.15 for Luna. That puts Ember level with Haiku at about a third of the cost. \u201cOur teeny model muse ember is now on the efficient frontier, matching haiku at 3x cheaper cost per task, and smarter than luna,\u201d Meta\u2019s AI chief Alexandr Wang wrote on X. Ember also leads both rivals on Meta\u2019s other agent and coding tests.<\/p>\n
On AutomationBench, which tests full business workflows, it scored 37.8%, against 20.2% for Luna and 13.9% for Haiku. It does not win everywhere. On FrontierCode 2.0, which checks whether maintainers would merge a model\u2019s code changes, Haiku scored 34.9% and Ember 27.2%. Several of the other results, including Meta\u2019s Personal Superintelligence Index, come from Meta\u2019s own internal tests.<\/p>\n
Built to work under a bigger model Ember can take in text, images, audio and video. It can read up to a million tokens at once, enough for a full codebase. Meta pitches it as a partner for Muse Wonder, its frontier model. \u201cFor the hardest problems, pair it with Muse Wonder: Muse Wonder plans, Muse Ember handles the volume,\u201d Meta wrote. Ember is also the first model Meta trained using the larger Muse Wonder Full.<\/p>\n
Meta is keeping Ember\u2019s model weights private, as with its previous Muse releases, Frederic Lardinois reported for The New Stack. In October, Meta released a weaker open model while keeping its frontier models closed. Ember\u2019s input price also undercuts Chinese rivals, according to The New Stack. They include DeepSeek V4.1 Flash, Z.ai\u2019s GLM-5.3 Flash and MiniMax M3.1 Flash.<\/p>\n
All three score lower on the Artificial Analysis index. Meta tested Ember against its own safety framework, covering chemical and biological weapons, cybersecurity and loss of control. It stayed below the framework\u2019s high-risk thresholds in every category, the company said. Ember is available now through the Meta Model API and Muse Code, Meta\u2019s coding tool.<\/p>\n
It joins a Muse line-up that already powers Meta\u2019s Muse agent. Meta said it has even more efficient models in training. Get the TNW newsletter \u2715 Close Get the TNW newsletter Get the most important tech news in your inbox each week.<\/p>\n","protected":false},"excerpt":{"rendered":"
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