Using GLM-4 9B on AWS Bedrock

Implementation guide · GLM-4 · Zhipu AI

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AWS Bedrock exposes GLM-4 9B through model ID glm-4-9b. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.

Last refreshed 2026-06-15. Next refresh: weekly.

Quick Start

1
Create an account at AWS Bedrock and generate an API key.
2
Use the AWS Bedrock SDK or REST API to call glm-4-9b — see the documentation for request format.
3
You'll be billed $0.10/1M input, $0.10/1M output tokens. See full pricing.

API Portal Documentation Pricing

Code Examples

Install

pip install boto3

API key

AWS_ACCESS_KEY_ID

Model ID

glm-4-9b

Use Amazon Bedrock model IDs, e.g. "anthropic.claude-3-opus-20240229-v1:0" for on-demand, or cross-region inference profile IDs like "us.anthropic.claude-opus-4-7-20251101-v1:0". These differ from the public model slug.

import boto3

# Reads AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION from env
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
    modelId="glm-4-9b",
    messages=[{
        "role": "user",
        "content": [{"text": "Hello"}]
    }]
)
print(response["output"]["message"]["content"][0]["text"])