Z AI

Proprietary model

Released August 2026

GLM-5.3 (max) Intelligence, Performance & Price Analysis

API Provider Benchmarks

Model summary

Intelligence

60

Artificial Analysis Intelligence Index

4 out of 4 units for Intelligence.

Speed

84.7

Output tokens per second

3 out of 4 units for Speed.

Cost

In

$1.40

Out

$4.40

Cache Discount

$0.68

Cost per Intelligence Index task

3 out of 4 units for Cost.

Verbosity

170M

Output tokens from Intelligence Index

4 out of 4 units for Verbosity.

GLM-5.3 (max) is amongst the leading models in intelligence and reasonably priced when comparing to other models of similar price. It's also faster than average, however very verbose. 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.

At 85 tokens per second, GLM-5.3 (max) is faster than average (74).

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

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

Intelligence

Speed

Cost per Task

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

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

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.

Benchmarks

Intelligence Evaluations

See more

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgent

New

Quantitative analysis on spreadsheets & documents

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

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.

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

Most attractive quadrant

Pareto line

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

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

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

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)

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

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

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 (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Time per Intelligence Index Task

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better

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

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

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 (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Frequently Asked Questions

Common questions about GLM-5.3 (max)

GLM-5.3 (max) was released on August 18, 2026.

GLM-5.3 (max) was created by Z AI.

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).

GLM-5.3 (max) generates output at 84.7 tokens per second (based on Z AI's API), which is above average compared to other reasoning models in a similar price tier (median: 74.5 t/s).

GLM-5.3 (max) has a time to first token (TTFT) of 1.88s (based on Z AI's API), which is better than average compared to other reasoning models in a similar price tier (median: 2.81s).

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.

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

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).

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.

GLM-5.3 (max) supports text input.

GLM-5.3 (max) supports text output.

No, GLM-5.3 (max) does not support image input. It can only process text.

No, GLM-5.3 (max) is not multimodal. It only supports text input.

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.

No, GLM-5.3 (max) is proprietary. The model weights are not publicly available.

GLM-5.3 (max) has 753 billion parameters.

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.

Yes, GLM-5.3 (max) is available via API through 2 providers. Compare API providers

GLM-5.3 (max) is available through 2 API providers. Compare providers