Anthropic's lightweight, high-speed small model in the Claude family, focused on low-latency, low-cost inference.
Meta released Muse Ember, its smallest and most efficient model, positioned against Claude Haiku and GPT-6 Luna for agent workloads such as tool calls, coding subtasks, routing, extraction and long-document parsing. Short-prompt pricing matches rivals at $0.10 per million input and $0.50 output tokens, but long-context tiers rise more steeply, reaching $0.30 input and $1.50 output beyond 200k tokens.
Meta released Muse Ember, its smallest and most efficient model to date, targeting high-frequency agent workloads such as tool calling, coding subtasks and long-document parsing. On the Artificial Analysis intelligence index it scores about 36 at top reasoning effort, matching Claude Haiku 5.5 and beating GPT-6 Luna, while costing roughly one-third per task at about $0.136, placing it on a favorable efficiency frontier. It is available now via Meta Model API and Muse Code, with more efficient models reportedly in training.
Meta released Muse Ember, its smallest model, matching Claude Haiku 5.5 at 36 on the Artificial Analysis Intelligence Index at roughly $0.136 per task versus $0.411 for Haiku. Positioned as the execution layer in a two-model agent split alongside the planning-focused Muse Wonder, it natively handles text, images, audio and video with a 1M-token context and up to 512K output tokens. The trade-off is weaker real-repo mergeability on FrontierCode 2.0-AA, and several results are Meta internal runs.
Meta has launched Muse Ember, its smallest and most efficient model to date, purpose-built for complex reasoning and high-frequency agentic workloads. It matches Claude Haiku 5.5 on the Artificial Analysis Index (36) at one-third the per-task cost, while leading on agentic and multimodal benchmarks such as AutomationBench-AA and OSWorld 2.1. The model is available immediately via Meta Model API, Google Cloud and Oracle Cloud, priced at $0.10/$0.50 per million input/output tokens.
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.