Claude Fable 5

Anthropic — Claude Fable 5

Model Details

Claude Fable 5 is Anthropic's next-generation model for complex knowledge work and coding, capable of sustained autonomous operation across multi-day tasks. It plans across stages, delegates to sub-agents, and self-verifies its work.

Endpoints and APIs supported

The following tables show which endpoints and APIs are supported for Claude Fable 5. 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-runtime endpoint

Bedrock Features

Features supported using bedrock-mantle endpoint

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

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

Content Restrictions

Claude Fable 5 includes blocking classifiers for dual-use content in cybersecurity and biology. When a classifier blocks a request, the API returns a standard HTTP 200 response with stop_reason: "refusal" and a stop_details object containing the restriction category. Refusal rates on this model are materially higher than on previous Claude models.

Customers should handle stop_reason: "refusal" as a primary response path. Prompt-stage refusals (blocked before inference begins) are not billed. Mid-stream refusals (blocked after partial output) are billed for tokens generated before the block.

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/".

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

Data Retention

To use this model, you must opt in to AWS review by setting your data retention mode to aws_review via the Data Retention API. For more information, see Amazon Bedrock abuse detection.

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.

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.

Invoke & Converse

pip install boto3

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

AWS_BEARER_TOKEN_BEDROCK="<provide your Bedrock API key>"

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

import json
import boto3

client = boto3.client('bedrock-runtime', region_name='us-east-1')
response = client.invoke_model(
    modelId='anthropic.claude-fable-5',
    body=json.dumps({
        'messages': [{'role': 'user', 'content': 'Can you explain the features of Amazon Bedrock?'}],
        'max_tokens': 1024
    })
)
print(json.loads(response['body'].read()))

Messages

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

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

AWS_BEARER_TOKEN_BEDROCK="<provide your Bedrock API key>"

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

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-fable-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello, Claude"}],
)

print(response)