Input / Prompt
$0.14/ 1M tokens
$0.0001 per 1K tokens
Output / Completion
$1.20/ 1M tokens
$0.0012 per 1K tokens
Prompt Cache Read
$0.04/ 1M tokens
Save 75% vs prompt
Batch Inference
$0.07/ 1M in
Save 50% via Batch API
Price Ratio
1 : 8.6
Prompt to completion factor
Regional Reach
13AWS Regions

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Qwen3 Next 80B A3B 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: $0.14/1M ยท Output: $1.20/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with Qwen3 Next 80B A3B on AWS
Prompt CachingSave ~75%

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

  • Read Rate: $0.04 / 1M tokens
  • Write Rate (5m TTL): $0.18 / 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: $0.07 / 1M tokens
  • Batch Output: $0.60 / 1M tokens
  • Ideal for: Offline classification, synthetic data generation, benchmark evals
Cross-Region & Tiers13 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 13 available AWS cloud regions
Baseline: $0.14/1M inGovCloud: $0.22/1M in13 Global Regions
Showing 5 benchmark regions
AWS RegionPrompt Input $/1MCompletion Output $/1MCache Read $/1MBatch Input $/1MPricing Tier
us-east-1
US East (N. Virginia)
$0.14$1.20โ€”$0.07Baseline
us-west-2
US West (Oregon)
$0.14$1.20โ€”$0.07Baseline
eu-central-1
EU (Frankfurt)
$0.17$1.44โ€”$0.08+20% vs Baseline
eu-west-1
EU (Ireland)
$0.17$1.44โ€”$0.08+20% vs Baseline
ap-northeast-1
Asia Pacific (Tokyo)
$0.17$1.44โ€”$0.08+20% vs Baseline
ap-southeast-3
Asia Pacific (Jakarta)
$0.17$1.44โ€”$0.08+20% vs Baseline
ap-south-1
Asia Pacific (Mumbai)
$0.17$1.41โ€”$0.08+20% vs Baseline
ap-southeast-2
Asia Pacific (Sydney)
$0.14$1.24โ€”$0.07+3% vs Baseline
eu-west-2
EU (London)
$0.22$1.86โ€”$0.11+55% vs Baseline
eu-south-1
EU (Milan)
$0.17$1.44โ€”$0.08+20% vs Baseline
eu-north-1
EU (Stockholm)
$0.17$1.44โ€”$0.08+20% vs Baseline
sa-east-1
South America (Sao Paulo)
$0.17$1.44โ€”$0.08+20% vs Baseline
us-east-2
US East (Ohio)
$0.14$1.20โ€”$0.07Baseline

Developer Quickstart

Invoke Qwen3 Next 80B A3B 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="alibaba.qwen3-next-80b-a3b-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of Qwen3 Next 80B A3B."}]
        }
    ],
    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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