GLM-5.3 Artificial Analysis Benchmarks

Proprietary model Released August 2026 GLM-5.3 (max) Intelligence, Performance & Price Analysis Model summary Intelligence Speed Cost Verbosity Comparison Summary GLM-5.3 (max) is amongst the leading models in intelligence and reasonably priced when comparing to other models of similar price. The model supports text input, outputs text, and has a 1M tokens context window. GLM-5.3 (max) scores 60 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 35). When evaluating the Intelligence Index, it generated 170M tokens, which is very verbose in comparison to the median of 72M. Pricing for GLM-5.3 (max) is $1.40 per 1M input tokens (moderately priced, median: $1.75) and $4.40 per 1M output tokens (moderately priced, median: $10.00). In total, it cost $1238.50 to evaluate GLM-5.3 (max) on the Intelligence Index. Technical specifications This page shows the reasoning version of this model. A non-reasoning variant may also exist. Supports: text Supports: text 181 models in this class 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: <$0.15 per 1M tokens $0.15–$1 per 1M tokens >$1 per 1M tokens Highlights Intelligence Speed Cost per Task Intelligence Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index 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 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. Open Weights 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 Agentic real-world work tasks, (Elo-500)/2000 Agentic tool use Agentic coding & terminal use Coding Reasoning & knowledge Scientific reasoning Physics reasoning Knowledge 1 - hallucination rate Long context reasoning Agentic knowledge work, Elo Agentic SaaS workflows Legal agentic work, criterion pass rate Agentic business operations Quantitative analysis on spreadsheets & documents Instruction following Long-horizon agentic tasks Kubernetes incident root-cause analysis Visual reasoning Intelligence Evaluation Relevance While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases. Artificial Analysis Intelligence Index 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 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 Cost per Intelligence Index Task 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 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 Output Tokens per Intelligence Index Task 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 Cost per Intelligence Index Task 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 to Run Artificial Analysis Intelligence Index 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 Cache Hit 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 for RAG Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data. Context Window Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model). Frequently Asked Questions Common questions about GLM-5.3 (max) When was GLM-5.3 (max) released? GLM-5.3 (max) was released on August 18, 2026. Who created GLM-5.3 (max)? GLM-5.3 (max) was created by Z AI. How intelligent is GLM-5.3 (max)? GLM-5.3 (max) scores 60 on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 35). How much does GLM-5.3 (max) cost? GLM-5.3 (max) costs $1.40 per 1M input tokens (better than average, median: $1.75) and $4.40 per 1M output tokens (better than average, median: $10.00), based on Z AI's API. What is GLM-5.3 (max) API pricing? GLM-5.3 (max) costs $1.40 per 1M input tokens and $4.40 per 1M output tokens (based on Z AI's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.90 per 1M tokens. Pricing may vary by provider. Compare provider pricing How verbose is GLM-5.3 (max)? When evaluated on the Intelligence Index, GLM-5.3 (max) generated 170M output tokens, which is at the higher end compared to other reasoning models in a similar price tier (median: 72M). Is GLM-5.3 (max) a reasoning model? Yes, GLM-5.3 (max) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer. What input modalities does GLM-5.3 (max) support? GLM-5.3 (max) supports text input. What output modalities does GLM-5.3 (max) support? GLM-5.3 (max) supports text output. Can GLM-5.3 (max) process images? No, GLM-5.3 (max) does not support image input. It can only process text. Is GLM-5.3 (max) multimodal? No, GLM-5.3 (max) is not multimodal. It only supports text input. What is the context window of GLM-5.3 (max)? GLM-5.3 (max) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request. Is GLM-5.3 (max) open source? No, GLM-5.3 (max) is proprietary. The model weights are not publicly available. How many parameters does GLM-5.3 (max) have? GLM-5.3 (max) has 753 billion parameters. How does GLM-5.3 (max) perform on benchmarks? GLM-5.3 (max) achieves a score of 60 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding. Is GLM-5.3 (max) available via API? Yes, GLM-5.3 (max) is available via API through 1 provider. Compare API providers Where can I use GLM-5.3 (max)? GLM-5.3 (max) is available through 1 API provider. Compare providers