GLM-5.3 (Max) vs. Muse Glimmer (High)

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Comparison between GLM-5.3 (Max) and Muse Glimmer (High) across intelligence, price, speed, context window and more.

For details relating to our methodology, see our Methodology page.

	Z AI	Meta
Intelligence
Intelligence Index	32	6
AA-Briefcase v1.1	1503	478
AutomationBench-AA	62%	7%
Terminal-Bench 4.0	42%	1%
FrontierCode 2.0	22%	1%
AA-Omniscience v1.1	6	−32
GDP.pdf	11%	10%
HLE-Diamond	18%	9%
CritPt v2 (Hard Subset)	34%	7%
Terminal-Bench-Science 0.1	10%	0%
Cost
Price per 1M Tokens	$0.902	$0.208
Input Price per 1M Tokens	$1.40	$0.30
Output Price per 1M Tokens	$4.40	$1.20
Cache Hit Price per 1M Tokens	$0.26	$0.04
Cost per Task	$5.01	$0.16
Cost to Run Intelligence Index	$3,496	$182
Token Use
Output Tokens per Task	119k	26k
Reasoning Tokens per Task	85k	14k
Output Tokens to Run Intelligence Index	135M	39M
Performance
Output Speed	86 tokens/s	123 tokens/s
Time to First Token	1.55s	1.20s
Time to First Answer Token	24.90s	17.50s
End-to-End Response Time	30.73s	21.58s
Time per Task	1077.16s	224.05s
Technical specifications
Context Window	1000k tokens~1,500 A4 pages of size 12 Arial font	131k tokens~197 A4 pages of size 12 Arial font
Release Date	August 2026	August 2026
Knowledge Cutoff		January 2026
Total parameters	753B	30B
Active parameters	40B
Reasoning	Yes	Yes
Input modality	Supports: text	Supports: text and image
Output modality	Supports: text	Supports: text
Open Source (Weights)	Yes	Yes
License	GLM-5.3 License	Apache 2.0
License Supports Commercial Use Without Restrictions	Yes	Yes

Updated

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

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

No publicly available API

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. 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

No publicly available API

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

AA-Briefcase v1.1

AutomationBench-AA

Cost per Task (USD, Log Scale)

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.

Capability Indexes

Measures the performance of models on specific capabilities and industries

Artificial Analysis Finance & Accounting Index

Incorporates 5 evaluations: AA-Briefcase v1.1, AA-Omniscience v1.1, HLE-Diamond, AutomationBench-AA, GDP.pdf · Higher is better

No publicly available API

Benchmarks

Evaluations

Evaluations measured independently by Artificial Analysis · AA-PersonalityBench scores are percentile ranks from 1 to 100

No publicly available API

AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience v1.1 AccuracyUpdated

Knowledge

AA-Omniscience v1.1 Non-Hallucination RateUpdated

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 2.0New

Agentic software engineering in open-source repositories

HLE-DiamondNew

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

CritPt v2 (Hard Subset)

Physics reasoning

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

GDPval-AA v2.1

Agentic real-world work tasks, Elo

AA-3DNew

Agentic interactive 3D experiences, Elo

Code2Video-AANew

Product and brand videos designed in code, Elo

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AAUpdated

Medical long context reasoning

MMMU-Pro

Visual reasoning

AA-PersonalityBench: Expressiveness ScoreNew

Rich, varied, well-crafted language

AA-PersonalityBench: Organization ScoreNew

Well-structured, scannable, answer-first

AA-PersonalityBench: Transparency ScoreNew

Truthful, acknowledges uncertainty, non-sycophantic

AA-PersonalityBench: Slop ScoreNew

Use of filler and generic language

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

AA-Briefcase v1.1

AA-Briefcase Elo

AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

No publicly available API

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience v1.1

AA-Omniscience Net Score

AA-Omniscience v1.1 Net Score (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.

No publicly available API

AA-PersonalityBenchNew

Model AvsModel B

AA-PersonalityBench: Expressiveness Index, GLM-5.3 (max) vs Muse Glimmer (high)

Chance each model’s answer is judged to show more of each trait from their Elo gap · 50% is an even match

GLM-5.3 (max)

Muse Glimmer (high)

Lexically Varied

99%

Eloquent

98%

Natural Sounding

98%

Literate

92%

Metaphorical

68%

32%

Openness Index

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Intelligence Index Comparisons

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

AA-Briefcase v1.1

AutomationBench-AA

Cost per Task (USD, Log Scale)

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

No publicly available API

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 Intelligence Index task · Segmented by token type · Lower is better

No publicly available API

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

No publicly available API

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)

No publicly available API

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: tokens limit · Higher is better

No publicly available API

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).

Speed

Measured by Output Speed (tokens per second)

Output Speed

Output tokens per second · Higher is better

No publicly available API

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

Time per Intelligence Index Task

Weighted average decode time (minutes) per Intelligence Index task · Excludes TTFT and overhead time · Lower is better

No publicly available API

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

Latency

Measured by Time (seconds) to First Token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

No publicly available API

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

End-to-End Response Time

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

No publicly available API

Seconds to receive a 500 token response. Key components:

Input time: Time to receive the first response token
Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API or the median across providers where a first-party API is not available.

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference (billions)

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

GLM-5.3 (Max) is more intelligent. GLM-5.3 (Max) scores 32, compared with Muse Glimmer (High) at 6 on the Artificial Analysis Intelligence Index.

Muse Glimmer (High) is faster. Muse Glimmer (High) generates 122.7 tokens per second, compared with GLM-5.3 (Max) at 85.7 tokens per second.

Muse Glimmer (High) is cheaper. Muse Glimmer (High) costs $0.21 per 1M tokens, compared with GLM-5.3 (Max) at $0.90 per 1M tokens (7:2:1 cache hit/input/output ratio).

Muse Glimmer (High) has lower latency. Muse Glimmer (High) has a time to first token of 1.20s, compared with GLM-5.3 (Max) at 1.55s.

GLM-5.3 (Max) has a larger context window. GLM-5.3 (Max) supports 1.0M tokens, compared with Muse Glimmer (High) at 130k tokens.

GLM-5.3 (Max) vs. Muse Glimmer (High)

Intelligence

Speed

Cost per Task

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Capability Indexes

Artificial Analysis Finance & Accounting Index

Benchmarks

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase v1.1

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience v1.1

AA-Omniscience Net Score

AA-Omniscience Net Score

AA-PersonalityBenchNew

AA-PersonalityBench: Expressiveness Index, GLM-5.3 (max) vs Muse Glimmer (high)

Openness Index

Artificial Analysis Openness Index: Score

Intelligence Index Comparisons

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Token Use

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Context Window

Context Window

Context Window for RAG

Context Window

Speed

Output Speed

Output Speed

Model Performance Representation

Time per Intelligence Index Task

Time per Intelligence Index Task

Latency

Latency: Time To First Answer Token

Time to First Answer Token

End-to-End Response Time

End-to-End Response Time

End-to-End Response Time

Model Performance Representation

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Frequently Asked Questions

Which is more intelligent, GLM-5.3 (Max) or Muse Glimmer (High)?

Which is faster, GLM-5.3 (Max) or Muse Glimmer (High)?

Which is cheaper, GLM-5.3 (Max) or Muse Glimmer (High)?

Which has lower latency, GLM-5.3 (Max) or Muse Glimmer (High)?

Which has a larger context window, GLM-5.3 (Max) or Muse Glimmer (High)?