{"slug": "muse-ember-max-intelligence-performance-and-price-analysis", "title": "Muse Ember (Max) Intelligence, Performance and Price Analysis", "summary": "Meta's Muse Ember (Max), released November 9, 2026, scores 36 on the Artificial Analysis Intelligence Index, well above the median of 24 for models in its price tier, according to Artificial Analysis. The reasoning model accepts text and image input, outputs text with a 1M-token context window, and is priced at $0.10 per 1M input tokens and $0.50 per 1M output tokens, versus medians of $0.23 and $0.83. Evaluating Muse Ember (Max) on the Intelligence Index costs $0.14 per task on average, and the model generated 115M tokens during the evaluation against a median of 97.1M.", "body_md": "# Muse Ember (Max) Intelligence, Performance & Price Analysis\n\n### Model summary\n\n#### Performance\n\nMuse Ember (Max) is amongst the leading models in intelligence and well priced when comparing to other models of similar price. The model supports text and image input, outputs text, and has a 1M tokens context window.\n\nMuse Ember (Max) scores 36 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 24). When evaluating the Intelligence Index, it generated 115M tokens, which is somewhat verbose in comparison to the median of 97.1M.\n\nPricing for Muse Ember (Max) is $0.10 per 1M input tokens (competitively priced, median: $0.23) and $0.50 per 1M output tokens (competitively priced, median: $0.83). On average, it costs $0.14 per task to evaluate Muse Ember (Max) on the Intelligence Index.\n\n| Reasoning | Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist. | \n|---|---|\n| Input modality | Supports: text and image | \n| Output modality | Supports: text | \n| Context window | 1M ~1573 A4 pages of size 12 Arial font | \n\nMetrics are compared against models of the same class:\n\n- Non-reasoning models → compared only with other non-reasoning models\n- Reasoning models → compared across both reasoning and non-reasoning\n- Open weights models → compared only with other open weights models of the same size class:\n- Tiny: ≤4B parameters\n- Small: 4B–40B parameters\n- Medium: 40B–150B parameters\n- Large: >150B parameters\n- Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:\n- <$0.15 per 1M tokens\n- $0.15–$1 per 1M tokens\n- >$1 per 1M tokens\n\n### Time per Task\n\n## Intelligence\n\n### [Artificial Analysis Intelligence Index](https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index)\n\n### Artificial Analysis Intelligence Index by Open Weights / Proprietary\n\n### Intelligence Index vs. Cost per Task\n\nMeasures the performance of models on specific capabilities and industries\n\n### [Artificial Analysis Finance & Accounting Index](https://artificialanalysis.ai/indexes/finance-and-accounting)\n\n### Evaluations\n\nAgentic knowledge work, Elo\n\nKnowledge\n\n1 - hallucination rate\n\nAgentic SaaS workflows\n\nAgentic scientific research workflows in a terminal\n\nAgentic software engineering in open-source repositories\n\nReasoning & knowledge\n\nProfessional document reasoning, All-pass\n\nAgentic coding & terminal use\n\nPhysics reasoning\n\nQuantitative analysis on spreadsheets & documents\n\nLegal agentic work, Hallucination-Gated All-Pass Rate\n\nAgentic business operations\n\nAgentic real-world work tasks, Elo\n\nAgentic interactive 3D experiences, Elo\n\nProduct and brand videos designed in code, Elo\n\nKubernetes incident root-cause analysis\n\nMedical long context reasoning\n\nVisual reasoning\n\nRich, varied, well-crafted language\n\nWell-structured, scannable, answer-first\n\nTruthful, acknowledges uncertainty, non-sycophantic\n\nUse of filler and generic language\n\n### AA-Briefcase v1.1\n\n### AA-Briefcase Elo\n\n### AA-Omniscience v1.1\n\n### AA-Omniscience Net Score\n\n## Token Use\n\n### Output Tokens per Intelligence Index Task\n\n## Cost\n\n### Cost per Intelligence Index Task\n\n### Cost to Run Artificial Analysis Intelligence Index\n\n### Pricing: Cache Hit, Input, and Output\n\n## Context Window\n\n### Context Window\n\n## Frequently Asked Questions\n\nCommon questions about Muse Ember (Max)\n\nMuse Ember (Max) was released on November 9, 2026.\n\nMuse Ember (Max) was created by Meta.\n\nMuse Ember (Max) scores 36 on the Artificial Analysis Intelligence Index, placing it well above average among other models in a similar price tier (median: 24).\n\nMuse Ember (Max) costs $0.10 per 1M input tokens (very competitive, median: $0.23) and $0.50 per 1M output tokens (very competitive, median: $0.83), based on the median across providers serving the model.\n\nMuse Ember (Max) costs $0.10 per 1M input tokens and $0.50 per 1M output tokens (based on the median across providers serving the model). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.0728 per 1M tokens. Pricing may vary by provider. [Compare provider pricing](https://artificialanalysis.ai/models/muse-ember/providers)\n\nWhen evaluated on the Intelligence Index, Muse Ember (Max) generated 115M output tokens, which is somewhat higher than average compared to other models in a similar price tier (median: 97.1M).\n\nYes, Muse Ember (Max) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.\n\nMuse Ember (Max) supports text and image input.\n\nMuse Ember (Max) supports text output.\n\nYes, Muse Ember (Max) supports image input and can analyze, describe, and answer questions about images.\n\nYes, Muse Ember (Max) is multimodal. It can process text and image input and generate text output.\n\nMuse Ember (Max) has a context window of 1M tokens. This determines how much text and conversation history the model can process in a single request.\n\nNo, Muse Ember (Max) is proprietary. The model weights are not publicly available.\n\nMuse Ember (Max) is a proprietary model and Meta has not disclosed the model size or parameter count.\n\nMuse Ember (Max) achieves a score of 36 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across knowledge work, coding, science, and general knowledge.\n\nYes, Muse Ember (Max) is available via API through 1 provider. [Compare API providers](https://artificialanalysis.ai/models/muse-ember/providers)\n\nMuse Ember (Max) is available through 1 API provider. [Compare providers](https://artificialanalysis.ai/models/muse-ember/providers)", "url": "https://wpnews.pro/news/muse-ember-max-intelligence-performance-and-price-analysis", "canonical_source": "https://artificialanalysis.ai/models/muse-ember", "published_at": "2026-11-09 20:08:19+00:00", "updated_at": "2026-11-09 20:22:35.760769+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research"], "entities": ["Meta", "Muse Ember (Max)", "Artificial Analysis", "Artificial Analysis Intelligence Index"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/muse-ember-max-intelligence-performance-and-price-analysis", "markdown": "https://wpnews.pro/news/muse-ember-max-intelligence-performance-and-price-analysis.md", "text": "https://wpnews.pro/news/muse-ember-max-intelligence-performance-and-price-analysis.txt", "jsonld": "https://wpnews.pro/news/muse-ember-max-intelligence-performance-and-price-analysis.jsonld"}}