GLM5.3 Artificial Analysis Benchmarks

# GLM-5.3 (max) - Intelligence, Performance & Price Analysis Source: [https://artificialanalysis.ai/models/glm-5-3](https://artificialanalysis.ai/models/glm-5-3) ## 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\.1includes: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](https://artificialanalysis.ai/methodology/intelligence-benchmarking)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\.1includes: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](https://artificialanalysis.ai/methodology/intelligence-benchmarking)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 \(typically requires obtaining a paid license\)\. ### Intelligence Evaluations Intelligence evaluations measured independently by Artificial Analysis · Higher is better Quantitative analysis on spreadsheets & documents 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\.1includes: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](https://artificialanalysis.ai/methodology/intelligence-benchmarking)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\. ## 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 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\.1includes: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](https://artificialanalysis.ai/methodology/intelligence-benchmarking)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 \(varied by model\)\.