Foundation Model · Amazon BedrockFable FamilyBalanced TierTextMulti-Tier Pricing

Claude Fable 5.1

Amazon Bedrock token pricing and cost optimization reference for Claude Fable 5.1.Rates normalized to USD per 1 Million Tokens ($/1M) with prompt caching and batch API discounts across 35 AWS regions.

ProviderAnthropic

Maker HQSan Francisco, California, USA

Class / TierBalanced

ModalityText

Prompt CachingSave 98%

Batch APISave 50%

AWS Regions35 Global

Bedrock Model IDanthropic.claude-fable-5-1-v1:0

Input / Prompt

$10.00/ 1M tokens

$0.0100 per 1K tokens

Output / Completion

$50.00/ 1M tokens

$0.0500 per 1K tokens

Prompt Cache Read

$0.25/ 1M tokens

Save 98% vs prompt

Batch Inference

$5.00/ 1M in

Save 50% via Batch API

Price Ratio

1 : 5.0

Prompt to completion factor

Regional Reach

35AWS Regions

Compare 35 regions ↓

Alternatives to Claude Fable 5.1

Option	Price vs Claude Fable 5.1	Why look at it
Claude 2.1 Compare →	input-20%-$2.00/1M output-52%-$26.00/1M Standard tier, per 1M tokens	Next step down in price from Anthropic (general tier)
Claude Mythos 5 Compare →	input+10%+$1.00/1M output+10%+$5.00/1M Standard tier, per 1M tokens	Next step up in price from Anthropic (flagship tier)
Claude Fable 5 Compare →	inputsame price outputsame price Standard tier, per 1M tokens	Same Fable line, different version
Claude Mythos 5.1 Compare →	inputsame price outputsame price Standard tier, per 1M tokens	Also from Anthropic (flagship tier)

Claude Fable 5.1 Spend Estimator

Model inference spend by prompt volume, cache hit rate, and batch processing.

Prompt Tokens (Millions / month)

M tokens

Completion Tokens (Millions / month)

M tokens

Prompt Cache Hit Rate(98% discount on cached context)

30%

Batch API Mode50% discount on non-realtime async jobs via S3

Estimated Monthly Spend

$0.00

Fresh Prompts:$0.00

Cached Prompts:$0.00

Completions:$0.00

Active: Input: $10.00/1M · Output: $50.00/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with Claude Fable 5.1 on AWS

Prompt CachingSave ~98%

Caches repetitive system instructions, schemas, and document prefixes in memory with 5-minute and extended 1-hour TTL tiers.

Read Rate: $0.25 / 1M tokens
Write Rate (5m TTL): $12.50 / 1M tokens
Write Rate (1h TTL): $20.00 / 1M tokens
Ideal for: Multi-turn conversations, RAG document queries, structured schemas

Batch InferenceSave 50%

Asynchronous bulk processing submitted via Amazon S3 with a standard 24-hour turnaround SLA.

Batch Input: $5.00 / 1M tokens
Batch Output: $25.00 / 1M tokens
Ideal for: Offline classification, synthetic data generation, benchmark evals

Cross-Region & Tiers35 Regions

AWS Bedrock supports standard on-demand inference, priority reserved throughput, and cross-region routing.

Standard: Lowest latency, pay-per-token with zero commitments
Cross-Region Routing: Automatically burst traffic to available regional capacity
GovCloud: Dedicated isolated compliance partitions with +20% uplift

Regional Pricing Matrix

Live rates across all 35 available AWS cloud regions

Baseline: $10.00/1M inGovCloud: $12.00/1M in35 Global Regions

AWS Region	Prompt Input $/1M	Completion Output $/1M	Cache Read $/1M	Batch Input $/1M	Pricing Tier
us-east-1 US East (N. Virginia)	$10.00	$50.00	$0.25	—	Baseline
us-west-2 US West (Oregon)	$10.00	$50.00	$0.25	—	Baseline
eu-central-1 Europe (Frankfurt)	$10.00	$50.00	$0.25	—	Baseline
eu-west-1 Europe (Ireland)	$10.00	$50.00	$0.25	—	Baseline
ap-northeast-1 Asia Pacific (Tokyo)	$10.00	$50.00	$0.25	—	Baseline
us-gov-west-1 AWS GovCloud (US-West)	$12.00	$60.00	$0.30	—	GovCloud (+20%)
af-south-1 Africa (Cape Town)	$10.00	$50.00	$0.25	—	Baseline
ap-south-2 Asia Pacific (Hyderabad)	$10.00	$50.00	$0.25	—	Baseline
ap-southeast-3 Asia Pacific (Jakarta)	$10.00	$50.00	$0.25	—	Baseline
ap-southeast-5 Asia Pacific (Malaysia)	$10.00	$50.00	$0.25	—	Baseline
ap-southeast-4 Asia Pacific (Melbourne)	$10.00	$50.00	$0.25	—	Baseline
ap-south-1 Asia Pacific (Mumbai)	$10.00	$50.00	$0.25	—	Baseline
ap-southeast-6 Asia Pacific (New Zealand)	$10.00	$50.00	$0.25	—	Baseline
ap-northeast-3 Asia Pacific (Osaka)	$10.00	$50.00	$0.25	—	Baseline
ap-northeast-2 Asia Pacific (Seoul)	$10.00	$50.00	$0.25	—	Baseline
ap-southeast-1 Asia Pacific (Singapore)	$10.00	$50.00	$0.25	—	Baseline
ap-southeast-2 Asia Pacific (Sydney)	$10.00	$50.00	$0.25	—	Baseline
ap-east-2 Asia Pacific (Taipei)	$10.00	$50.00	$0.25	—	Baseline
ap-southeast-7 Asia Pacific (Thailand)	$10.00	$50.00	$0.25	—	Baseline
us-gov-east-1 AWS GovCloud (US-East)	$12.00	$60.00	$0.30	—	GovCloud (+20%)
ca-central-1 Canada (Central)	$10.00	$50.00	$0.25	—	Baseline
ca-west-1 Canada West (Calgary)	$10.00	$50.00	$0.25	—	Baseline
eu-west-2 Europe (London)	$10.00	$50.00	$0.25	—	Baseline
eu-south-1 Europe (Milan)	$10.00	$50.00	$0.25	—	Baseline
eu-west-3 Europe (Paris)	$10.00	$50.00	$0.25	—	Baseline
eu-south-2 Europe (Spain)	$10.00	$50.00	$0.25	—	Baseline
eu-north-1 Europe (Stockholm)	$10.00	$50.00	$0.25	—	Baseline
eu-central-2 Europe (Zurich)	$10.00	$50.00	$0.25	—	Baseline
il-central-1 Israel (Tel Aviv)	$10.00	$50.00	$0.25	—	Baseline
mx-central-1 Mexico (Central)	$10.00	$50.00	$0.25	—	Baseline
me-south-1 Middle East (Bahrain)	$10.00	$50.00	$0.25	—	Baseline
me-central-1 Middle East (UAE)	$10.00	$50.00	$0.25	—	Baseline
sa-east-1 South America (Sao Paulo)	$10.00	$50.00	$0.25	—	Baseline
us-east-2 US East (Ohio)	$10.00	$50.00	$0.25	—	Baseline
us-west-1 US West (N. California)	$10.00	$50.00	$0.25	—	Baseline

Developer Quickstart

Invoke Claude Fable 5.1 via Amazon Bedrock Converse API

Python (boto3) · Amazon Bedrock Runtime

import boto3

# Amazon Bedrock Converse API invocation
client = boto3.client("bedrock-runtime", region_name="us-east-1")

response = client.converse(
    modelId="anthropic.claude-fable-5-1-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of Claude Fable 5.1."}]
        }
    ],
    inferenceConfig={
        "maxTokens": 1024,
        "temperature": 0.7
    }
)

output_text = response["output"]["message"]["content"][0]["text"]
usage = response["usage"]
print(f"Response: {output_text}")
print(f"Usage: {usage['inputTokens']} in, {usage['outputTokens']} out")