Z AI

Proprietary model

Released August 2026

GLM-5.3-Flash Intelligence, Performance & Price Analysis

Model summary

Intelligence

Speed

N/A

Unknown out of 4 units for Speed.

Cost

In

Out

Cache Discount

Cost per Intelligence Index task

Verbosity

150M

GLM-5.3-Flash is amongst the leading models in intelligence and well priced when comparing to other models of similar price. The model supports text and image input, outputs text, and has a 400k tokens context window.

GLM-5.3-Flash scores 57 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 18). When evaluating the Intelligence Index, it generated 150M tokens, which is very verbose in comparison to the median of 64M.

Pricing for GLM-5.3-Flash is $0.15 per 1M input tokens (competitively priced, median: $0.25) and $0.50 per 1M output tokens (competitively priced, median: $0.90). In total, it cost $138.02 to evaluate GLM-5.3-Flash on the Intelligence Index.

Reasoning	Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist.
Input modality	Supports: text and image
Output modality	Supports: text
Context window	400k ~600 A4 pages of size 12 Arial font

• 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 by Open Weights / Proprietary

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.

Benchmarks

Intelligence Evaluations

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.

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.

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).

Frequently Asked Questions

Common questions about GLM-5.3-Flash

GLM-5.3-Flash was released on August 26, 2026.

GLM-5.3-Flash was created by Z AI.

GLM-5.3-Flash scores 57 on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 18).

GLM-5.3-Flash costs $0.15 per 1M input tokens (very competitive, median: $0.25) and $0.50 per 1M output tokens (very competitive, median: $0.90), based on Z AI's API.

GLM-5.3-Flash costs $0.15 per 1M input tokens and $0.50 per 1M output tokens (based on Z AI's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.10 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, GLM-5.3-Flash generated 150M output tokens, which is at the higher end compared to other reasoning models in a similar price tier (median: 64M).

Yes, GLM-5.3-Flash 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-Flash supports text and image input.

GLM-5.3-Flash supports text output.

Yes, GLM-5.3-Flash supports image input and can analyze, describe, and answer questions about images.

Yes, GLM-5.3-Flash is multimodal. It can process text and image input and generate text output.

GLM-5.3-Flash has a context window of 400k tokens. This determines how much text and conversation history the model can process in a single request.

No, GLM-5.3-Flash is proprietary. The model weights are not publicly available.

GLM-5.3-Flash is a proprietary model and Z AI has not disclosed the model size or parameter count.

GLM-5.3-Flash achieves a score of 57 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Yes, GLM-5.3-Flash is available via API through 2 providers. Compare API providers

GLM-5.3-Flash is available through 2 API providers. Compare providers