{"slug": "muse-glimmer-high-intelligence-performance-and-price-analysis", "title": "Muse Glimmer (High) Intelligence, Performance and Price Analysis", "summary": "Meta released Muse Glimmer (high), a 30B-parameter open-weight reasoning model, on August 10, 2026, scoring 35 on the Artificial Analysis Intelligence Index, well above the median of 9 for comparable models. The model supports text and image input, outputs text, has a 256k token context window, and is priced at $0.00 per 1M input and output tokens, undercutting the medians of $0.04 and $0.15 respectively.", "body_md": "# Muse Glimmer (high) Intelligence, Performance & Price Analysis\n\n### Model summary\n\n#### Intelligence\n\n#### Speed\n\n#### Input Price\n\n#### Output Price\n\n#### Verbosity\n\nMuse Glimmer (high) is amongst the leading models in intelligence and well priced when comparing to other open weight models of similar size. The model supports text and image input, outputs text, and has a 256k tokens context window with knowledge up to January 2026.\n\nMuse Glimmer (high) scores 35 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 9). When evaluating the Intelligence Index, it generated 48M tokens, which is somewhat verbose in comparison to the median of 40M.\n\nPricing for Muse Glimmer (high) is $0.00 per 1M input tokens (competitively priced, median: $0.04) and $0.00 per 1M output tokens (competitively priced, median: $0.15).\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| Knowledge cutoff | Jan 4, 2026 |\n| Context window | 256k ~384 A4 pages of size 12 Arial font |\n| Total parameters | 30B |\n| License |\n|\n\n[Hugging Face](https://huggingface.co/meta-models/Muse-Glimmer-30B)Metrics 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\nHighlights\n\n### Speed\n\n## Intelligence\n\n### Artificial Analysis Intelligence Index\n\n### Artificial Analysis Intelligence Index by Open Weights / Proprietary\n\n### Intelligence Evaluations\n\nAgentic real-world work tasks, (Elo-500)/2000\n\n[𝜏³-Banking](/evaluations/tau3-banking)Updated\n\nAgentic tool use\n\nAgentic coding & terminal use\n\nCoding\n\n[Humanity's Last Exam](/evaluations/humanitys-last-exam)Updated\n\nReasoning & knowledge\n\nScientific reasoning\n\nPhysics reasoning\n\n[AA-Omniscience Accuracy](/evaluations/omniscience)Updated\n\nKnowledge\n\n1 - hallucination rate\n\n[AA-LCR](/evaluations/artificial-analysis-long-context-reasoning)Updated\n\nLong context reasoning\n\nAgentic knowledge work, Elo\n\nAgentic SaaS workflows\n\nLegal agentic work, criterion pass rate\n\nAgentic business operations\n\nInstruction following\n\nLong-horizon agentic tasks\n\nKubernetes incident root-cause analysis\n\nVisual reasoning\n\n### AA-Omniscience\n\n### AA-Omniscience Index\n\n## Openness Index\n\n### Artificial Analysis Openness Index: Score\n\n## Intelligence Index Comparisons\n\n### Intelligence Index vs. Cost per Intelligence Index Task\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## Model Size (Open Weights Models Only)\n\n### Model Size: Total and Active Parameters\n\n## Frequently Asked Questions\n\nCommon questions about Muse Glimmer (high)\n\nMuse Glimmer (high) was released on August 10, 2026.\n\nMuse Glimmer (high) was created by Meta.\n\nMuse Glimmer (high) scores 35 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 9).\n\nWhen evaluated on the Intelligence Index, Muse Glimmer (high) generated 48M output tokens, which is somewhat higher than average compared to other open weight models of similar size (median: 40M).\n\nYes, Muse Glimmer (high) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.\n\nMuse Glimmer (high) supports text and image input.\n\nMuse Glimmer (high) supports text output.\n\nYes, Muse Glimmer (high) supports image input and can analyze, describe, and answer questions about images.\n\nYes, Muse Glimmer (high) is multimodal. It can process text and image input and generate text output.\n\nMuse Glimmer (high) has a context window of 260k tokens. This determines how much text and conversation history the model can process in a single request.\n\nYes, Muse Glimmer (high) is open weights. The model weights are publicly available and can be downloaded for self-hosting.\n\nMuse Glimmer (high) has 30 billion parameters.\n\nMuse Glimmer (high) is released under the Apache 2.0 license. This license allows commercial use. [View license](https://www.apache.org/licenses/LICENSE-2.0)\n\nMuse Glimmer (high) achieves a score of 35 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.\n\nMuse Glimmer (high) has a knowledge cutoff of January 2026. The model's training data includes information up to this date.\n\nMuse Glimmer (high) is an open weights model that can be self-hosted. [View providers](/models/muse-glimmer/providers)\n\nMuse Glimmer (high) is an open weights model that can be downloaded and self-hosted. [Compare providers](/models/muse-glimmer/providers)", "url": "https://wpnews.pro/news/muse-glimmer-high-intelligence-performance-and-price-analysis", "canonical_source": "https://artificialanalysis.ai/models/muse-glimmer", "published_at": "2026-08-10 21:38:00+00:00", "updated_at": "2026-08-10 22:12:00.842083+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-products"], "entities": ["Meta", "Muse Glimmer (high)", "Artificial Analysis Intelligence Index"], "alternates": {"html": "https://wpnews.pro/news/muse-glimmer-high-intelligence-performance-and-price-analysis", "markdown": "https://wpnews.pro/news/muse-glimmer-high-intelligence-performance-and-price-analysis.md", "text": "https://wpnews.pro/news/muse-glimmer-high-intelligence-performance-and-price-analysis.txt", "jsonld": "https://wpnews.pro/news/muse-glimmer-high-intelligence-performance-and-price-analysis.jsonld"}}