# Inference using Anthropic Messages API The Anthropic Messages API provides native access to Claude models using the Anthropic request and response format. You can use the Messages API on both the `bedrock-runtime` and `bedrock-mantle` endpoints. For new applications, we recommend the `bedrock-runtime` endpoint. | **Endpoint** | **Base URL** | **Use case** | | --- | --- | --- | | bedrock-runtime | https://bedrock-runtime.{region}.amazonaws.com/anthropic | Recommended for new applications. Uses AWS SDK integrations, IAM authentication, and invocation logging. | | bedrock-mantle | https://bedrock-mantle.{region}.api.aws/anthropic/v1/messages | Supports Amazon Bedrock API key authentication and [Workspaces (Anthropic-compatible)](workspaces.md). | ## Supported Regions The Messages API is available on the `bedrock-runtime` endpoint in all Regions where Claude models are available. See [Regional availability by models](models-region-compatibility.md) for details. On the `bedrock-mantle` endpoint, it is available in all Regions that support `bedrock-mantle` (see [Supported Regions and Endpoints](inference-responses-api.md#bedrock-mantle-supported)). ## Prerequisites Before using the Messages API, make sure you have the following: + **Model access** – Request access to Claude models in the Amazon Bedrock console. See [Request access to models](model-access.md). + **Authentication** – Depends on the endpoint: + `bedrock-runtime` – Use AWS SigV4 credentials through the AWS SDK (boto3, etc.), a Amazon Bedrock API key (see [API keys](api-keys.md)), or a short-term Bedrock bearer token generated from AWS credentials with the [aws-bedrock-token-generator](https://pypi.org/project/aws-bedrock-token-generator/) library. + `bedrock-mantle` – Use a Amazon Bedrock API key (see [API keys](api-keys.md)) or AWS SigV4 credentials. + **Anthropic version header** – All requests require an API version: + `bedrock-runtime`: Set `"anthropic_version": "bedrock-2023-05-31"` in the request body when calling `InvokeModel`. The Anthropic SDK adds the required header automatically when using `base_url=https://bedrock-runtime.{region}.amazonaws.com/anthropic`. + `bedrock-mantle`: Set `anthropic-version: 2023-06-01` as an HTTP header. ## Basic request To send a basic message to a Claude model, choose the tab for your preferred method, and then follow the steps: ------ #### [ bedrock-runtime (Python, Anthropic SDK) ] Use the Anthropic SDK against the `bedrock-runtime` endpoint's `/anthropic` route with a short-term Bedrock bearer token generated by [aws-bedrock-token-generator](https://pypi.org/project/aws-bedrock-token-generator/): ``` from anthropic import Anthropic from aws_bedrock_token_generator import provide_token token = provide_token(region="us-east-1") client = Anthropic( base_url="https://bedrock-runtime.us-east-1.amazonaws.com/anthropic", api_key=token, ) response = client.messages.create( model="global.anthropic.claude-sonnet-5", max_tokens=1024, messages=[ {"role": "user", "content": "Explain quantum computing in one sentence."} ] ) print(response.content[0].text) ``` ------ #### [ bedrock-runtime (Python, boto3) ] ``` import boto3 import json client = boto3.client("bedrock-runtime", region_name="us-east-1") response = client.invoke_model( modelId="anthropic.claude-sonnet-5", body=json.dumps({ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Explain quantum computing in one sentence."} ] }) ) result = json.loads(response["body"].read()) print(result["content"][0]["text"]) ``` ------ #### [ bedrock-runtime (AWS CLI) ] Use the AWS CLI to invoke the model: ``` aws bedrock-runtime invoke-model \ --model-id anthropic.claude-sonnet-5 \ --body '{ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Explain quantum computing in one sentence."} ] }' \ --cli-binary-format raw-in-base64-out \ output.json ``` ------ #### [ bedrock-runtime (curl) ] You can call `bedrock-runtime` directly with curl by signing the request with AWS SigV4. The example below uses curl's built-in `--aws-sigv4` flag (available in curl 7.75.0 and later): ``` curl -X POST \ "https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-sonnet-5/invoke" \ -H "Content-Type: application/json" \ --aws-sigv4 "aws:amz:us-east-1:bedrock" \ --user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY" \ -d '{ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Explain quantum computing in one sentence."} ] }' ``` **Note** If you are using temporary credentials from AWS STS (for example, an assumed role), also pass the session token by adding `-H "X-Amz-Security-Token: $AWS_SESSION_TOKEN"` to the request. For most use cases we recommend the AWS SDKs or CLI, which handle request signing and credential refresh automatically. ------ #### [ bedrock-runtime (curl, API key) ] You can also call `bedrock-runtime` with a Amazon Bedrock API key using the Anthropic-native `/anthropic/v1/messages` route, just like `bedrock-mantle`: ``` curl -X POST https://bedrock-runtime.us-east-1.amazonaws.com/anthropic/v1/messages \ -H "x-api-key: $AWS_BEARER_TOKEN_BEDROCK" \ -H "anthropic-version: 2023-06-01" \ -H "Content-Type: application/json" \ -d '{ "model": "us.anthropic.claude-sonnet-5", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Explain quantum computing in one sentence."} ] }' ``` ------ #### [ bedrock-mantle (curl) ] ``` curl -X POST https://bedrock-mantle.us-east-1.api.aws/anthropic/v1/messages \ -H "x-api-key: $BEDROCK_API_KEY" \ -H "anthropic-version: 2023-06-01" \ -H "Content-Type: application/json" \ -d '{ "model": "anthropic.claude-sonnet-5", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Explain quantum computing in one sentence."} ] }' ``` ------ ## Stream responses To receive response tokens incrementally as they are generated, choose the tab for your preferred method, and then follow the steps: ------ #### [ bedrock-runtime (Python, Anthropic SDK) ] ``` from anthropic import Anthropic from aws_bedrock_token_generator import provide_token token = provide_token(region="us-east-1") client = Anthropic( base_url="https://bedrock-runtime.us-east-1.amazonaws.com/anthropic", api_key=token, ) with client.messages.stream( model="global.anthropic.claude-sonnet-5", max_tokens=1024, messages=[ {"role": "user", "content": "Write a short poem about cloud computing."} ], ) as stream: for text in stream.text_stream: print(text, end="", flush=True) ``` ------ #### [ bedrock-runtime (Python, boto3) ] ``` import boto3 import json client = boto3.client("bedrock-runtime", region_name="us-east-1") response = client.invoke_model_with_response_stream( modelId="anthropic.claude-sonnet-5", body=json.dumps({ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Write a short poem about cloud computing."} ] }) ) for event in response["body"]: chunk = json.loads(event["chunk"]["bytes"]) if chunk["type"] == "content_block_delta": print(chunk["delta"]["text"], end="") ``` ------ #### [ bedrock-runtime (curl, API key) ] ``` curl -X POST https://bedrock-runtime.us-east-1.amazonaws.com/anthropic/v1/messages \ -H "x-api-key: $AWS_BEARER_TOKEN_BEDROCK" \ -H "anthropic-version: 2023-06-01" \ -H "Content-Type: application/json" \ -d '{ "model": "us.anthropic.claude-sonnet-5", "max_tokens": 1024, "stream": true, "messages": [ {"role": "user", "content": "Write a short poem about cloud computing."} ] }' ``` ------ #### [ bedrock-mantle (curl) ] ``` curl -X POST https://bedrock-mantle.us-east-1.api.aws/anthropic/v1/messages \ -H "x-api-key: $BEDROCK_API_KEY" \ -H "anthropic-version: 2023-06-01" \ -H "Content-Type: application/json" \ -d '{ "model": "anthropic.claude-sonnet-5", "max_tokens": 1024, "stream": true, "messages": [ {"role": "user", "content": "Write a short poem about cloud computing."} ] }' ``` ------ ## Supported features In addition to the [Extended thinking](claude-messages-extended-thinking.md), [Adaptive thinking](claude-messages-adaptive-thinking.md), [Structured outputs](claude-messages-structured-outputs.md), [Compaction](claude-messages-compaction.md), [Fallback credit for refused requests (beta)](claude-messages-fallback-credit.md), and [Mid-conversation system messages](claude-messages-mid-conversation-system.md) sections above, the Messages API supports the following features with Claude models: + **System prompts** – Set model behavior with a `system` parameter. + **Multi-turn conversations** – Pass alternating `user` and `assistant` messages. + **Tool use** – Define tools the model can call. See [Use a tool to complete an Amazon Bedrock model response](tool-use.md). + **Vision** – Send images in the `content` array alongside text. + **Prompt caching** – Cache frequently used context to reduce latency and cost. For the full Messages API request and response field reference, see [Request and Response](model-parameters-anthropic-claude-messages-request-response.md). ## Count tokens To count the number of input tokens that a request would consume before sending it for inference, use the Anthropic `count_tokens` path on the `bedrock-mantle` endpoint. This is the only token-counting path supported for Claude models that are not available on `bedrock-runtime` with a Region-specific endpoint, including Claude models that launch with cross-Region inference (CRIS) only. For details and an example, see [Count tokens using the bedrock-mantle endpoint](count-tokens.md#count-tokens-mantle).