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GLM-5.3 (max) Intelligence, Performance & Price Analysis
Model summary
Speed
Cost
#53 / 181Comparison 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
| Reasoning | Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist. |
|---|---|
| Input modality | Supports: text |
| Output modality | Supports: text |
| Context window | 1M ~1500 A4 pages of size 12 Arial font |
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
Speed
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).
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