AWS Bedrock exposes Command R through model ID command-r. 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
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Code Examples
pip install boto3AWS_ACCESS_KEY_IDcommand-rUse 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="command-r",
messages=[{
"role": "user",
"content": [{"text": "Hello"}]
}]
)
print(response["output"]["message"]["content"][0]["text"])Pricing on AWS Bedrock
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.50 |
| Output tokens | $1.50 |
Capabilities
About Command R
Command R is a 35-billion parameter large language model developed by Cohere, tailored for enterprise-scale AI applications. It is optimized for long-context tasks such as retrieval-augmented generation (RAG) and tool use, emphasizing high performance coupled with strong accuracy. This model excels in reasoning, summarization, and question answering across ten key languages. Its use spans various enterprise applications, automating complex tasks that involve interacting with systems like CRMs, APIs, and databases. The model can process up to 128,000 tokens and integrates seamlessly with Cohere's Embed and Rerank models, enhancing accuracy and efficiency in large-scale tasks.