Anthropic's lightweight, high-speed small model in the Claude family, focused on low-latency, low-cost inference.
Meta released Muse Ember, its smallest model to date, claiming intelligence on par with Claude Haiku 5.5 at roughly one-third the cost. It targets token-heavy agent workloads such as tool calls, coding subtasks, routing, extraction and long-document parsing, and is available now on the Meta Model API and in Muse Code. Pricing is $0.10 input and $0.50 output per 1M tokens up to 200K tokens, rising to $0.30 and $1.50 beyond that.
Meta has launched Muse Ember, its smallest and most efficient model yet, purpose-built for the routine work AI agents do all day: tool calling, small coding tasks, request routing, data extraction and long-document processing. Meta claims it delivers more intelligence per dollar at every reasoning level than Claude Haiku 5.5 or GPT-6 Luna, at roughly $0.14 per task, though Haiku still leads on real coding benchmarks. The multimodal model handles up to one million tokens and is available now via the Meta Model API and Muse Code at $0.10 per million input tokens and $0.50 per million output tokens.
Meta has released Muse Ember, an efficiency-tier model that scores 36 on the Artificial Analysis Intelligence Index, matching Claude Haiku 5.5 (max) while costing about one-third as much at $0.14 per task. It scores 68.1% on AutomationBench-AA, level with Meta's flagship Muse Wonder, though it trails the flagship by 8 index points at roughly 1/50th the cost per task.
Meta's Muse Ember (Max) tops the Code Arena WebDev small-model leaderboard at 1608 points, beating Claude Haiku 5.5 and GPT-6 Luna. At identical API pricing, its median cost per task is just $0.03, roughly half of Luna and a quarter of Haiku, placing it on the cost-performance Pareto frontier. Generations averaged about two minutes each, producing 20+ websites and animations in a single pass.
Meta has introduced Muse Ember, its smallest and most efficient model to date, targeting complex reasoning execution and everyday agent workflows with cross-app operation, sequential tool calling, test running and targeted code fixes, plus a 1M-token context and native multimodal understanding. It tops the Code Arena WebDev small-model ranking at 1608 points, beating Claude Haiku 5.5 and GPT-6 Luna, while sitting on the Pareto frontier at a median cost of just $0.03 per task.
Meta has launched Muse Ember, a new efficiency-tier model line that scores 36 on the Artificial Analysis Intelligence Index, matching Claude Haiku 5.5 at roughly one-third the cost per task ($0.14 vs $0.41). It reaches 68.1% on AutomationBench-AA, level with Meta's flagship Muse Wonder, though results are mixed on agentic coding and other benchmarks.
Muse introduces Muse Ember, its smallest and most efficient model, targeting the repetitive work agents do all day: tool calls, coding subtasks, routing, extraction and long-document parsing. Muse claims it delivers more intelligence per dollar than Claude Haiku 5.5 or GPT-6 Luna at any reasoning setting, matching Haiku's best score at roughly one-third the cost and sitting on the efficiency frontier of the Artificial Analysis leaderboard.