Input / Prompt
$3.00/ 1M tokens
$0.0030 per 1K tokens
Output / Completion
$15.00/ 1M tokens
$0.0150 per 1K tokens
Prompt Cache Read
$0.30/ 1M tokens
Save 90% vs prompt
Batch Inference
$1.50/ 1M in
Save 50% via Batch API
Price Ratio
1 : 5.0
Prompt to completion factor
Regional Reach
15AWS Regions

Alternatives to Claude 3.5 Sonnet v2

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Claude Sonnet 5.5Compare →
input-33%-$1.00/1M
output-33%-$5.00/1M
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Claude Opus 5.5Compare →
input+33%+$1.00/1M
output+33%+$5.00/1M
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Claude Sonnet 4.6Compare →
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outputsame price
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Claude Opus 5Compare →
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output+67%+$10.00/1M
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Claude 3.5 Sonnet v2 Spend Estimator

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

Workload Presets:
M tokens
M tokens
30%
Estimated Monthly Spend
$0.00
Fresh Prompts:$0.00
Cached Prompts:$0.00
Completions:$0.00
Active: Input: $3.00/1M ยท Output: $15.00/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with Claude 3.5 Sonnet v2 on AWS
Prompt CachingSave ~90%

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

  • Read Rate: $0.30 / 1M tokens
  • Write Rate (5m TTL): $3.75 / 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: $1.50 / 1M tokens
  • Batch Output: $7.50 / 1M tokens
  • Ideal for: Offline classification, synthetic data generation, benchmark evals
Cross-Region & Tiers15 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 15 available AWS cloud regions
Baseline: $3.00/1M in15 Global Regions
Showing 6 benchmark regions
AWS RegionPrompt Input $/1MCompletion Output $/1MCache Read $/1MBatch Input $/1MPricing Tier
us-east-1
US East (N. Virginia)
$3.00$15.00$0.30$1.50Baseline
us-west-2
US West (Oregon)
$3.00$15.00$0.30$1.50Baseline
eu-west-1
Europe (Ireland)
$3.00$15.00โ€”$1.50Baseline
ap-northeast-1
Asia Pacific (Tokyo)
$3.00$15.00โ€”$1.50Baseline
ap-south-2
Asia Pacific (Hyderabad)
$3.00$15.00โ€”$1.50Baseline
ap-south-1
Asia Pacific (Mumbai)
$3.00$15.00โ€”$1.50Baseline
ap-northeast-3
Asia Pacific (Osaka)
$3.00$15.00โ€”$1.50Baseline
ap-northeast-2
Asia Pacific (Seoul)
$3.00$15.00โ€”$1.50Baseline
ap-southeast-1
Asia Pacific (Singapore)
$3.00$15.00โ€”$1.50Baseline
ap-southeast-2
Asia Pacific (Sydney)
$3.00$15.00โ€”$1.50Baseline
eu-south-1
Europe (Milan)
$3.00$15.00โ€”$1.50Baseline
eu-west-3
Europe (Paris)
$3.00$15.00โ€”$1.50Baseline
eu-south-2
Europe (Spain)
$3.00$15.00โ€”$1.50Baseline
eu-north-1
Europe (Stockholm)
$3.00$15.00โ€”$1.50Baseline
us-east-2
US East (Ohio)
$3.00$15.00$0.30$1.50Baseline

Developer Quickstart

Invoke Claude 3.5 Sonnet v2 via Amazon Bedrock Converse API
Python (boto3) ยท 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-3-5-sonnet-v2-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of Claude 3.5 Sonnet v2."}]
        }
    ],
    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")

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