Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 4 Scout 17B-16E Instruct on AWS Bedrock?
AWS Bedrock offers Llama 4 Scout 17B-16E Instruct with pay-as-you-go pricing at $0.17/1M input tokens. AWS Bedrock is Amazon's fully managed foundation-model service, providing unified API access to top models from Anthropic, Meta, Mistral, and other leading AI labs with built-in tools for RAG, fine-tuning, and AI agent development.
Compare Llama 4 Scout 17B-16E Instruct across 12 providers to find the best fit for your use caseInput / 1M
$0.17
Output / 1M
$0.22
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install boto3Auth
export AWS_ACCESS_KEY_ID=...Call
import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="llama-4-scout-17b-16e-instruct",Model ID
llama-4-scout-17b-16e-instructRequest example
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"])Gotchas
- 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.
- The endpoint template includes a region segment; set the same region in your SDK/client configuration.
- The examples expect AWS_ACCESS_KEY_ID; rename it only if your application config maps the new variable.
Compare Llama 4 Scout 17B-16E Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Cloudflare Workers AI | $0.27 | $0.85 |
| OpenRouter | $0.08 | $0.30 |
| Together AI | — | — |
| Fireworks AI | — | — |
| DeepInfra | $0.08 | $0.30 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.17 |
| Output tokens | $0.22 |
Capabilities
VisionMultimodalStructured Outputs
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.
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Model Specs
Released2025-04-05
Parameters109B (17B active)
Context10m
ArchitectureMixture of Experts
Knowledge cutoff2024-08