Cost 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.

Meta Superintelligence Lab released Muse Ember, its lightest and most cost-efficient model yet, built for the repetitive work agents do all day: tool calls, coding subtasks, routing, extraction, and long document parsing. At 10 cents per million input tokens, Meta positions Ember as cheaper and smarter than Anthropic's Haiku and OpenAI's Luna on Artificial Analysis's composite benchmark, while staying price-competitive with open-source rivals DeepSeek V4.1 Flash and GLM 5.3 Flash. Ember is designed to pair with a larger model such as Muse Wonder inside an agentic harness.

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.

Artificial Analysis compared how many Intelligence Index tasks a fixed $100 budget buys: roughly 40 tasks with Opus 5.5 at high reasoning effort versus about 166 for DeepSeek V4.1 Flash and 2,500 for GPT-6 Luna, exposing a wide spread in per-task inference cost across frontier models. In parallel, Apple cut engineers' daily AI usage budgets from $300 to $200, with plans to drop to $100 and $25 for some teams, signalling that enterprises now treat cost-per-task as a core deployment metric. The figures are observational and come without disclosed methodology; no model release or capability upgrade is involved.