Claude Opus 4.7

Anthropic — Claude Opus 4.7

Model Details

Claude Opus 4.7 is an Anthropic Opus model built for coding, enterprise workflows, and long-running agentic tasks.

Endpoints and APIs supported

The following tables show which endpoints and APIs are supported for Claude Opus 4.7. For more information, see APIs supported by Amazon Bedrock and Endpoints supported by Amazon Bedrock.

Endpoint support

APIs supported on bedrock-runtime endpoint

APIs supported on bedrock-mantle endpoint

Capabilities and Features

Bedrock Features

Features supported using bedrock-mantle endpoint

Features supported using bedrock-runtime endpoint

Implicit and Explicit Prompt Caching using bedrock-runtime and bedrock-mantle endpoints

For more information, see Prompt caching for faster model inference.

Computer use using bedrock-runtime and bedrock-mantle endpoints

For more information, see Computer use.

Pricing

This model is a third-party model offered and billed through AWS Marketplace. Charges appear on your AWS bill and in AWS Cost Explorer under the model provider (not under Amazon Bedrock). For pricing, see the Amazon Bedrock Pricing page.

Programmatic Access

Use the following model IDs and endpoint URLs to access this model programmatically. For more information about the available APIs and endpoints, see APIs supported and Endpoints supported.

For example, if region is us-east-1 (N. Virginia), then the bedrock-runtime endpoint URL will be "https://bedrock-runtime.us-east-1.amazonaws.com" and for bedrock-mantle will be "https://bedrock-mantle.us-east-1.api.aws/anthropic/v1/messages".

Service Tiers

Amazon Bedrock offers multiple service tiers to match your workload requirements. Standard provides pay-per-token access with no commitment (set "service_tier": "default" or omit the field). Priority delivers the fastest response times for a price premium (set "service_tier": "priority"). Flex provides lower-cost access for flexible, non-time-sensitive workloads (set "service_tier": "flex"). Reserved provides dedicated throughput with a term commitment for predictable workloads; it is set at the account level rather than per request (contact your AWS account team to enable). For more information, see service tiers.

Regional Availability

Regional availability at a glance

Amazon Bedrock offers three inference options: In-Region keeps requests within a single Region for strict compliance, Geo Cross-Region routes across Regions within a geography (such as US, EU, and APAC) while respecting data residency, and Global Cross-Region routes anywhere worldwide when there are no residency constraints. Refer to the Regional availability by models page for more details.

Availability differs by endpoint.

Availability using the bedrock-runtime endpoint

Availability using the bedrock-mantle endpoint

Geo inference details

Geo: US

Geo Inference ID: us.anthropic.claude-opus-4-7

Geo: EU

Geo Inference ID: eu.anthropic.claude-opus-4-7

Geo: JP

Geo Inference ID: jp.anthropic.claude-opus-4-7

Geo: AU

Geo Inference ID: au.anthropic.claude-opus-4-7

Global inference details

Quotas and Limits

Your AWS account has default quotas to maintain the performance of the service and to ensure appropriate usage of Amazon Bedrock. The default quotas assigned to an account might be updated depending on regional factors, payment history, fraudulent usage, and/or approval of a quota increase request. For more information, see Quotas for Amazon Bedrock documentation and see the limits for the model.

Default quotas for Claude Opus 4.7 are 20M input TPM and 4M output TPM on bedrock-mantle and 30M TPM on bedrock-runtime for each supported region. Claude Opus 4.7 does not have a requests-per-minute (RPM) quota; throttling is governed solely by the token-per-minute (TPM) quotas above.

Sampling parameters no longer supported

Starting with Claude Opus 4.7, temperature, top_p, and top_k parameters are no longer supported. The recommended migration path is to omit these parameters entirely from your requests and to use prompting to guide the model's behavior. If you previously relied on temperature = 0 for deterministic outputs, note that this setting did not guarantee identical responses across invocations.

Sample Code

Step 1 - AWS Account: If you have an AWS account already, skip this step. If you are new to AWS, sign up for an AWS account.

Step 2 - API key: Go to the Amazon Bedrock console and generate a long-term API key.

Step 3 - Get the SDK: To use this getting started guide, you must have Python already installed. Then install the relevant software depending on the APIs you are using.

Messages API

pip install -U anthropic aws-bedrock-token-generator

Invoke/Converse API

pip install boto3

Step 4 - Set environment variables: Configure your environment to use the API key for authentication.

Messages API

AWS_BEARER_TOKEN_BEDROCK="<provide your Bedrock API key>"

Invoke/Converse API

AWS_BEARER_TOKEN_BEDROCK="<provide your Bedrock API key>"

Step 5 - Run your first inference request: Save the file as bedrock-first-request.py

Messages API

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-opus-4-7",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Can you explain the features of Amazon Bedrock?"}],
)

print(response)

Invoke API

import json
import boto3

client = boto3.client('bedrock-runtime', region_name='us-east-1')
response = client.invoke_model(
    modelId='anthropic.claude-opus-4-7',
    body=json.dumps({
            'anthropic_version': 'bedrock-2023-05-31',
            'messages': [{ 'role': 'user', 'content': 'Can you explain the features of Amazon Bedrock?'}],
            'max_tokens': 1024
    })
)
print(json.loads(response['body'].read()))

Converse API

import boto3

client = boto3.client('bedrock-runtime', region_name='us-east-1')
response = client.converse(
    modelId='anthropic.claude-opus-4-7',
    messages=[
        {
            'role': 'user',
            'content': [{'text': 'Can you explain the features of Amazon Bedrock?'}]
        }
    ]
)
print(response)