GPT-6 Luna

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'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.

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.