{"slug": "meta-muse-ember-scores-36-on-the-artificial-analysis-the-cheapest-to-reach-so", "title": "Meta Muse Ember scores 36 on the Artificial Analysis, the cheapest to reach so", "summary": "Meta's Muse Ember (max) scored 36 on the Artificial Analysis Intelligence Index at $0.14 per task, making it the cheapest model to reach that score, according to Artificial Analysis. The result matches Claude Haiku 5.5 (max), which costs nearly 3x more at $0.41 for the same score, while Muse Ember (xhigh) scores 34 at $0.12. Muse Ember (max) also scored 68.1% on AutomationBench-AA and 46 on the Artificial Analysis Coding Agent Index in Muse Code, ahead of Claude Haiku 5.5 in Claude Code (37) and GPT-6 Luna in Codex (31), though its AA-Omniscience accuracy of 21.1% was the lowest among similarly intelligent models because it declines to answer 64% of questions, holding its hallucination rate to 17.4%.", "body_md": "November 9, 2026\n\n# **Meta's newest model Muse Ember scores 36 on the Artificial Analysis Intelligence Index, matching Claude Haiku 5.5 (max) at a third of the Cost per Task**\n\n[See model page](https://artificialanalysis.ai/models/muse-ember)\n\nMuse Ember is Meta's newest efficient-tier model line, with the same input and output token prices as Claude Haiku 5.5 and GPT-6 Luna\n\n## **Key Takeaways:**\n\n➤ **On the Intelligence Index vs. Cost per Task Pareto frontier at both max and xhigh effort.** Muse Ember (max) is the cheapest model scoring 36 or higher, at $0.14 per task, and Muse Ember (xhigh) scores 34 for $0.12. Claude Haiku 5.5 (max) costs almost 3x more at $0.41 for the same score as Muse Ember (max). Muse Ember (max) is also about 4x cheaper than DeepSeek V4.1 Flash 1017 (max, 33, $0.60) and GLM-5.3-Flash (28, $0.58)\n\n➤ **8 points behind Muse Wonder for about 1/50th of the Cost per Task.** Muse Wonder (max) scores 44 at $6.95 per Intelligence Index task\n\n➤ **Stands out on AutomationBench-AA.** Muse Ember (max) scores 68.1% on AutomationBench-AA, level with Muse Wonder (max), and beats Claude Haiku 5.5 (max) on GDP.pdf (22.8% vs 20.8%), but trails it on AA-Briefcase (1521 vs 1574 Elo)\n\n➤ **Mixed in agentic coding in our Intelligence Index evaluations.** It beats Claude Haiku 5.5 (max) on Terminal-Bench 4.0 (40.9% vs 32.8%) but trails it on FrontierCode 2.0 (27.2% vs 34.9%)\n\n➤ **Ahead of similarly priced models when running in its own coding agent.** In Muse Code, Muse Ember (max) scores 46 on the Artificial Analysis Coding Agent Index, ahead of Claude Haiku 5.5 in Claude Code (max, 37) and GPT-6 Luna in Codex (max, 31)\n\n➤ **Knowledge is the main weakness, but it rarely hallucinates.** Its AA-Omniscience accuracy of 21.1% is the lowest of the similarly intelligent models we compared, against 34.7% for Claude Haiku 5.5 (max) and 50.5% for Gemini 3.8 Flash (high). This is mostly because it declines to answer 64% of questions, which keeps its hallucination rate at 17.4%, half of Claude Haiku 5.5’s (max)\n\n## **Other details:** \n\n➤ **Pricing:** $0.10 input / $0.004 cached input / $0.50 output per 1M tokens\n➤ **Reasoning effort:** results shown at max effort, with xhigh also on the Intelligence vs. Cost chart\n➤ **Availability:** Meta's first-party API and Muse Code\n\n**Muse Ember costs less per task than similarly priced models**\n\nMuse Ember (max) has the same input and output token prices as Claude Haiku 5.5 (max) and GPT-6 Luna (max), but its cached input, most of the cost in our agentic evaluations, is 60% cheaper. Muse Ember (max) also uses roughly a third as many output tokens as Claude Haiku 5.5 (max)\n\n**Muse Ember's standout agentic result is AutomationBench-AA, where it scores 68.1%, level with Muse Wonder and GPT-6 Astra**\n\nAgentic coding is mixed. Muse Ember beats Claude Haiku 5.5 on Terminal-Bench 4.0 (40.9% vs 32.8%) but trails it on FrontierCode 2.0 (27.2% vs 34.9%)\n\n**Muse Ember is ahead of similarly priced models as a coding agent**\n\nIn Muse Code, it scores 46 on the Artificial Analysis Coding Agent Index, ahead of Claude Haiku 5.5 in Claude Code (37) and GPT-6 Luna in Codex (31). Its lead comes from SWE-Atlas-QnA-AA and Terminal-Bench 4.0, while it trails Claude Haiku 5.5 on FrontierCode, as it does in our Intelligence Index\n\n**Knowledge is Muse Ember's main weakness, but it rarely hallucinates**\n\nIts AA-Omniscience accuracy of 21.1% is the lowest of the similarly intelligent models we compared, mostly because it declines to answer 64% of questions. That keeps its hallucination rate at 17.4%, half of Claude Haiku 5.5's\n\nFull Artificial Analysis Intelligence Index results for Muse Ember (max)\n\n#### Read the latest\n\n### Introducing the Artificial Analysis Decision Index v2\n\nIntroducing the Artificial Analysis Decision Index v2: a new proprietary evaluation, updated benchmarks, and a headline metric that rewards model calibration\n\nNovember 8, 2026\n\n### VIALS-AA: different paths to scientific image reasoning\n\nIndependent VIALS-AA results, interactive agent traces, and an analysis of how models inspect scientific images.\n\nNovember 6, 2026\n\n### Thinking Machines Inkling 2 Small: the highest scoring open weights model outside of China pending weights release\n\nThinking Machines has released Inkling 2 Small, scoring 31 on the Artificial Analysis Intelligence Index - their plan to release its weights would make it the highest-scoring open weights model developed outside China based on current rankings\n\nNovember 5, 2026", "url": "https://wpnews.pro/news/meta-muse-ember-scores-36-on-the-artificial-analysis-the-cheapest-to-reach-so", "canonical_source": "https://artificialanalysis.ai/articles/meta-s-muse-ember-is-the-cheapest-model-scoring-36-on-the-intelligence-index", "published_at": "2026-11-09 19:06:33+00:00", "updated_at": "2026-11-09 19:23:55.231212+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["Meta", "Muse Ember", "Artificial Analysis", "Claude Haiku 5.5", "GPT-6 Luna", "Muse Wonder", "Muse Code", "DeepSeek V4.1 Flash"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/meta-muse-ember-scores-36-on-the-artificial-analysis-the-cheapest-to-reach-so", "markdown": "https://wpnews.pro/news/meta-muse-ember-scores-36-on-the-artificial-analysis-the-cheapest-to-reach-so.md", "text": "https://wpnews.pro/news/meta-muse-ember-scores-36-on-the-artificial-analysis-the-cheapest-to-reach-so.txt", "jsonld": "https://wpnews.pro/news/meta-muse-ember-scores-36-on-the-artificial-analysis-the-cheapest-to-reach-so.jsonld"}}