Using Llama 4 Scout 17B-16E Instruct on AWS Bedrock
Implementation guide · Llama 4 · AI at Meta
AWS Bedrock exposes Llama 4 Scout 17B-16E Instruct through model ID llama-4-scout-17b-16e-instruct. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-07-09. Next refresh: weekly.
Quick Start
- 1
- 2Use the AWS Bedrock SDK or REST API to call
llama-4-scout-17b-16e-instruct— see the documentation for request format. - 3
Code Examples
pip install boto3AWS_ACCESS_KEY_IDllama-4-scout-17b-16e-instructUse 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="llama-4-scout-17b-16e-instruct",
messages=[{
"role": "user",
"content": [{"text": "Hello"}]
}]
)
print(response["output"]["message"]["content"][0]["text"])Pricing on AWS Bedrock
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.17 |
| Output tokens | $0.22 |
Capabilities
About Llama 4 Scout 17B-16E Instruct
Meta's Llama 4 Scout is a 17-billion parameter mixture-of-experts model with 16 expert routing. Optimized for efficient inference on edge and cloud environments with strong multi-turn conversation capabilities. Available on Cloudflare Workers AI.