翻訳待ち：Muse Glimmer (High) Intelligence, Performance and Price Analysis

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Artificial Analysis Meta • Open weights model • Released August 2026 Muse Glimmer (high) Intelligence, Performance & Price Analysis API Provider Benchmarks Model summary Intelligence 35 Artificial Analysis Intelligence Index 4 out of 4 units for Intelligence. Speed N/A Output tokens per second Unknown out of 4 units for Speed. Input Price $0.00 USD per 1M tokens 1 out of 4 units for Input Price. Output Price $0.00 USD per 1M tokens 1 out of 4 units for Output Price. Verbosity 48M Output tokens from Intelligence Index 3 out of 4 units for Verbosity. Muse 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. Muse 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. Pricing 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). ReasoningYes This page shows the reasoning version of this model. A non-reasoning variant may also exist. Input modality Supports: text and image Output modality Supports: text Knowledge cutoffJan 4, 2026 Context window256k ~384 A4 pages of size 12 Arial font Total parameters30B LicenseApache 2.0 Model weightsHugging Face Metrics are compared against models of the same class: Non-reasoning models → compared only with other non-reasoning models Reasoning models → compared across both reasoning and non-reasoning Open weights models → compared only with other open weights models of the same size class: Tiny: ≤4B parameters Small: 4B–40B parameters Medium: 40B–150B parameters Large: >150B parameters Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio: $1 per 1M tokens Highlights Intelligence Artificial Analysis Intelligence Index · Higher is better Speed Output tokens per second · Higher is better Cost per Task Weighted average cost (USD) per Intelligence Index task · Lower is better Intelligence Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR Reasoning models are indicated by a lightbulb icon Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. Artificial Analysis Intelligence Index by Open Weights / Proprietary Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR Reasoning models are indicated by a lightbulb icon Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license). Benchmarks Intelligence Evaluations Intelligence evaluations measured independently by Artificial Analysis · Higher is better See more GDPval-AA v2 Agentic real-world work tasks, (Elo-500)/2000 𝜏³-BankingUpdated Agentic tool use Terminal-Bench v2.1 Agentic coding & terminal use SciCode Coding Humanity's Last ExamUpdated Reasoning & knowledge GPQA Diamond Scientific reasoning CritPt Physics reasoning AA-Omniscience AccuracyUpdated Knowledge AA-Omniscience Non-Hallucination RateUpdated 1 - hallucination rate AA-LCRUpdated Long context reasoning AA-Briefcase Agentic knowledge work, Elo AutomationBench-AA Agentic SaaS workflows Harvey LAB-AA Legal agentic work, criterion pass rate EnterpriseOps-Gym-AANew Agentic business operations IFBench Instruction following APEX-Agents-AA Long-horizon agentic tasks ITBench-AA Kubernetes incident root-cause analysis MMMU-Pro Visual reasoning Reasoning models are indicated by a lightbulb icon While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases. Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. AA-Omniscience AA-Omniscience Index AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. Reasoning models are indicated by a lightbulb icon AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. Openness Index Artificial Analysis Openness Index: Score Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open) Reasoning models are indicated by a lightbulb icon Intelligence Index Comparisons Intelligence Index vs. Cost per Intelligence Index Task Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task Most attractive quadrant Pareto line Reasoning models are indicated by a lightbulb icon Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight. Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. Token Use Output Tokens per Intelligence Index Task Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index Reasoning models are indicated by a lightbulb icon The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats). Cost Cost per Intelligence Index Task Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better Reasoning models are indicated by a lightbulb icon Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight. Cost to Run Artificial Analysis Intelligence Index Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index Reasoning models are indicated by a lightbulb icon The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats). Pricing: Cache Hit, Input, and Output Price (USD per M Tokens) Reasoning models are indicated by a lightbulb icon Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail. Context Window Context Window Context window: tokens limit · Higher is better Reasoning models are indicated by a lightbulb icon Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data. Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model). Model Size (Open Weights Models Only) Model Size: Total and Active Parameters Comparison between total model parameters and parameters active during inference Reasoning models are indicated by a lightbulb icon The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses. The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total. Frequently Asked Questions Common questions about Muse Glimmer (high) Muse Glimmer (high) was released on August 10, 2026. Muse Glimmer (high) was created by Meta. Muse 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). When 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). Yes, 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. Muse Glimmer (high) supports text and image input. Muse Glimmer (high) supports text output. Yes, Muse Glimmer (high) supports image input and can analyze, describe, and answer questions about images. Yes, Muse Glimmer (high) is multimodal. It can process text and image input and generate text output. Muse 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. Yes, Muse Glimmer (high) is open weights. The model weights are publicly available and can be downloaded for self-hosting. Muse Glimmer (high) has 30 billion parameters. Muse Glimmer (high) is released under the Apache 2.0 license. This license allows commercial use. View license Muse Glimmer (high) achieves a score of 35 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding. Muse Glimmer (high) has a knowledge cutoff of January 2026. The model's training data includes information up to this date. Muse Glimmer (high) is an open weights model that can be self-hosted. View providers Muse Glimmer (high) is an open weights model that can be downloaded and self-hosted. Compare providers