GLM 5.3

Z.AI — GLM 5.3

Model Details

GLM 5.3 is Z.AI's flagship open weight text generation model for complex software engineering and agentic tasks. It uses a Mixture-of-Experts architecture with 744B total parameters and approximately 40B active per token, and is built on the same base model as GLM 5.2 with improvements driven by scaled post-training. The model provides a 1M-token context window with up to 128K output tokens, and supports selectable reasoning effort levels so you can trade latency and token consumption against task performance. Typical uses include agentic coding and autonomous software engineering, terminal and CLI task execution, repository-scale code generation and refactoring, long-horizon multi-step engineering tasks spanning many tool calls, infrastructure diagnosis, and long-context analysis of large codebases. It supports function calling, structured JSON output, and response streaming including streamed reasoning content. For more information about model development and performance, see the model card.

Capabilities and Features

Bedrock Features

Features supported using bedrock-runtime endpoint

Explicit prompt caching using bedrock-runtime endpoint

For more information, see Prompt caching for faster model inference.

Pricing

For pricing information, see the Amazon Bedrock Pricing page.

Programmatic Access

Use the following model IDs and endpoint URLs to access this model programmatically. GLM 5.3 is available through cross-Region inference only, so you must use the geo or global inference ID rather than the base model ID. For more information about the available APIs and endpoints, see APIs supported and Endpoints supported.

For example, if region is us-east-1 (N. Virginia), then the bedrock-runtime endpoint URL will be "https://bedrock-runtime.us-east-1.amazonaws.com".

Service Tiers

Amazon Bedrock offers multiple service tiers to match your workload requirements. Standard provides pay-per-token access with no commitment (set "service_tier": "default" or omit the field). Priority delivers the fastest response times for a price premium (set "service_tier": "priority"). Flex provides lower-cost access for flexible, non-time-sensitive workloads (set "service_tier": "flex"). Reserved provides dedicated throughput with a term commitment for predictable workloads; it is set at the account level rather than per request (contact your AWS account team to enable). For more information, see service tiers.

Regional Availability

Regional availability at a glance

Amazon Bedrock offers three inference options: In-Region keeps requests within a single Region for strict compliance, Geo Cross-Region routes across Regions within a geography (such as US, EU, and APAC) while respecting data residency, and Global Cross-Region routes anywhere worldwide when there are no residency constraints. Refer to the Regional availability by models page for more details.

GLM 5.3 supports Geo Cross-Region inference from the US Regions and Global Cross-Region inference from the Regions listed in the following table. In-Region inference is not supported.

Quotas and Limits

Your AWS account has default quotas to maintain the performance of the service and to ensure appropriate usage of Amazon Bedrock. The default quotas assigned to an account might be updated depending on regional factors, payment history, fraudulent usage, and/or approval of a quota increase request. For more information, see Quotas for Amazon Bedrock documentation and see the limits for the model.

Sample Code

Step 1 - AWS Account: If you have an AWS account already, skip this step. If you are new to AWS, sign up for an AWS account.

Step 2 - API key: Go to the Amazon Bedrock console and generate a long-term API key.

Step 3 - Get the SDK: To use this getting started guide, you must have Python already installed. Then install the relevant software depending on the APIs you are using.

Chat Completions API

pip install boto3 openai

Invoke/Converse API

pip install boto3

Step 4 - Set environment variables: Configure your environment to use the API key for authentication.

Chat Completions API

OPENAI_API_KEY="<provide your Bedrock API key>"
OPENAI_BASE_URL="https://bedrock-runtime.<your-region>.amazonaws.com/openai/v1"

Invoke/Converse API

AWS_BEARER_TOKEN_BEDROCK="<provide your Bedrock API key>"

Step 5 - Run your first inference request: Save the file as bedrock-first-request.py

Chat Completions API

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="us.zai.glm-5.3",
    messages=[{"role": "user", "content": "Can you explain the features of Amazon Bedrock?"}]
    )
print(response)

Invoke API

import json
import boto3

client = boto3.client('bedrock-runtime', region_name='us-east-1')
response = client.invoke_model(
    modelId='us.zai.glm-5.3',
    body=json.dumps({
            'messages': [{ 'role': 'user', 'content': 'Can you explain the features of Amazon Bedrock?'}],
            'reasoning_effort': 'max',
            'max_tokens': 1024
    })
)
print(json.loads(response['body'].read()))

Converse API

import boto3

client = boto3.client('bedrock-runtime', region_name='us-east-1')
response = client.converse(
    modelId='us.zai.glm-5.3',
    messages=[
        {
            'role': 'user',
            'content': [{'text': 'Can you explain the features of Amazon Bedrock?'}]
        }
    ]
)
print(response)