Claude Fable 5.1

Anthropic — Claude Fable 5.1

Model Details

Claude Fable 5.1 is Anthropic's frontier model for ambitious coding, long-horizon agents, and enterprise knowledge work. It handles software projects that span an entire codebase, runs multi-hour jobs across many tools inside agent harnesses, and brings best-in-class vision to dense filings, charts, and diagrams.

• Model launch date: September 1, 2026

• Model EOL date: N/A

• End User License Agreements and Terms of Use: View

• Model lifecycle: Active

• Context window: 1M tokens

• Max output tokens: 128K

• Sampling parameters: temperature must be 1.0 or unset; top_p must be 0.99 or unset; temperature and top_p cannot be specified together; top_k is not supported

• Reasoning: Supported (adaptive thinking is always on and cannot be disabled; effort level configurable — low, medium, high, xhigh, max; default: high)

• Knowledge cutoff: June 2026

• Marketplace product ID: prod-lt3y75wunrtv4

Input Modalities	Output Modalities
Audio	Embedding
Image	Image
Speech	Speech
Text	Text
Video	Video

Endpoints and APIs supported

The following tables show which endpoints and APIs are supported for Claude Fable 5.1. For more information, see APIs supported by Amazon Bedrock and Endpoints supported by Amazon Bedrock.

Endpoint support

Endpoint	Supported
bedrock-runtime
bedrock-mantle

APIs supported on bedrock-runtime endpoint

Messages	Responses	Chat Completions	Converse	Invoke

APIs supported on bedrock-mantle endpoint

Messages	Responses	Chat Completions	Converse	Invoke

Tip

Whenever possible, we recommend using the bedrock-runtime endpoint for new applications. See Endpoints supported by Amazon Bedrock for details.

Capabilities and Features

Bedrock Features

Features supported using bedrock-runtime endpoint

Supported	Not Supported
Response streaming Prompt caching Abuse detection Guardrails Prompt optimization Knowledge base Model evaluation Prompt management Flows Agents	Intelligent prompt routing

Bedrock Features

Features supported using bedrock-mantle endpoint

Supported	Not Supported
Response streaming Prompt caching Abuse detection Count tokens	Guardrails Prompt optimization Knowledge base Model evaluation Prompt management Flows Agents Intelligent prompt routing

Prompt caching using bedrock-runtime and bedrock-mantle endpoints

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

Prompt caching supported	Min tokens per cache checkpoint	Max cache checkpoints per request	Supported TTL	Fields that accept prompt cache checkpoint
Yes	512	4	5 minutes, 1 hour	system, messages, and tools

Content Restrictions

Claude Fable 5.1 includes blocking classifiers for dual-use content in cybersecurity and life sciences. 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

For pricing information, 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.

Endpoint	Model ID	In-Region endpoint URL	Geo inference ID	Global inference ID
bedrock-runtime	anthropic.claude-fable-5-1	N/A	us.anthropic.claude-fable-5-1	global.anthropic.claude-fable-5-1
bedrock-mantle	anthropic.claude-fable-5-1	https://bedrock-mantle.{region}.api.aws/anthropic/v1/messages	N/A	N/A

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.

Standard	Priority	Flex	Reserved

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

Region	In-Region	Geo	Global
us-east-1 (N. Virginia)
us-east-2 (Ohio)
us-west-1 (N. California)
us-west-2 (Oregon)
ca-central-1 (Canada)
ca-west-1 (Calgary)
eu-central-1 (Frankfurt)
eu-central-2 (Zurich)
eu-north-1 (Stockholm)
eu-south-1 (Milan)
eu-south-2 (Spain)
eu-west-1 (Ireland)
eu-west-2 (London)
eu-west-3 (Paris)
ap-east-2 (Taipei)
ap-northeast-1 (Tokyo)
ap-northeast-2 (Seoul)
ap-northeast-3 (Osaka)
ap-south-1 (Mumbai)
ap-south-2 (Hyderabad)
ap-southeast-1 (Singapore)
ap-southeast-2 (Sydney)
ap-southeast-3 (Jakarta)
ap-southeast-4 (Melbourne)
ap-southeast-5 (Malaysia)
ap-southeast-6 (New Zealand)
ap-southeast-7 (Thailand)
il-central-1 (Tel Aviv)
me-central-1 (UAE)
me-south-1 (Bahrain)
af-south-1 (Cape Town)
sa-east-1 (São Paulo)
mx-central-1 (Mexico)
us-gov-east-1 (GovCloud East)
us-gov-west-1 (GovCloud West)

Availability using the bedrock-mantle endpoint

Region	In-Region	Geo	Global
us-gov-west-1 (GovCloud West)

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.

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-fable-5-1",
    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='global.anthropic.claude-fable-5-1',
    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='global.anthropic.claude-fable-5-1',
    messages=[
        {
            'role': 'user',
            'content': [{'text': 'Can you explain the features of Amazon Bedrock?'}]
        }
    ]
)
print(response)