Llama 3.2 11B Vision on AWS Bedrock

Name: Llama 3.2 11B Vision on AWS Bedrock
Brand: AI at Meta
SKU: llama-3.2-11b-vision-aws-bedrock
Price: 0.2 USD

Llama 3.2 · AI at Meta

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Last refreshed 2026-07-09. Next refresh: weekly.

Why use Llama 3.2 11B Vision on AWS Bedrock?

AWS Bedrock offers Llama 3.2 11B Vision with pay-as-you-go pricing at $0.20/1M 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.

Input / 1M

$0.20

Output / 1M

$0.27

Cache

Not sourced

Batch

Not sourced

Setup recipe

Python + curl

Install

pip install boto3

Auth

export AWS_ACCESS_KEY_ID=...

Call

import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
    modelId="llama-3.2-11b-vision",

Model ID

llama-3.2-11b-vision

Request 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-3.2-11b-vision",
    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.