Input / Prompt
$0.15/ 1M tokens
$0.0001 per 1K tokens
Output / Completion
$0.60/ 1M tokens
$0.0006 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 : 4.0
Prompt to completion factor
Regional Reach
15AWS Regions

On Google Cloud Vertex AI

The same model is sold through Vertex AI at one global rate per price type. These are Google's published rates, not the AWS rates above; both hosts are shown because the model is sold on both.

RateVertex AI, USD per 1M tokens
Input$0.09
Output$0.36
Cache readβ€”
Batch input$0.04
Batch output$0.18
Input, long contextβ€”
Output, long contextβ€”
Output, thinkingβ€”

Vertex AI rates are global: Google publishes one rate covering every region where the model runs, against the per-region AWS prices above. A dash means Google publishes no rate for that price type.

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Also from OpenAI

gpt-oss-120b Spend Estimator

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

Workload Presets:
M tokens
M tokens
%
Estimated Monthly Spend
$0.00
Fresh Prompts:$0.00
Cached Prompts:$0.00
Completions:$0.00
Active: Input: $0.15/1M Β· Output: $0.60/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with gpt-oss-120b 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): $0.19 / 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: $0.30 / 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

Rates by region

Live rates across all 15 available AWS cloud regions
Baseline: $0.15/1M inGovCloud: $0.23/1M 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)
$0.15$0.60β€”$0.07Baseline
us-west-2
US West (Oregon)
$0.15$0.60β€”$0.07Baseline
eu-central-1
EU (Frankfurt)
$0.20$0.79β€”$0.10+33% vs Baseline
eu-west-1
EU (Ireland)
$0.18$0.70β€”$0.09+20% vs Baseline
ap-northeast-1
Asia Pacific (Tokyo)
$0.18$0.73β€”$0.09+20% vs Baseline
us-gov-west-1
AWS GovCloud (US-West)
$0.18$0.72β€”$0.09GovCloud (+20%)
ap-southeast-3
Asia Pacific (Jakarta)
$0.16$0.62β€”$0.08+7% vs Baseline
ap-south-1
Asia Pacific (Mumbai)
$0.18$0.71β€”$0.09+20% vs Baseline
ap-southeast-2
Asia Pacific (Sydney)
$0.15$0.62β€”$0.08+3% vs Baseline
us-gov-east-1
AWS GovCloud (US-East)
$0.18$0.72β€”$0.09GovCloud (+20%)
eu-west-2
EU (London)
$0.23$0.93β€”$0.12+53% vs Baseline
eu-south-1
EU (Milan)
$0.20$0.79β€”$0.10+33% vs Baseline
eu-north-1
EU (Stockholm)
$0.15$0.60β€”$0.07Baseline
sa-east-1
South America (Sao Paulo)
$0.18$0.73β€”$0.09+20% vs Baseline
us-east-2
US East (Ohio)
$0.15$0.60β€”$0.07Baseline

Developer Quickstart

Invoke gpt-oss-120b via Amazon Bedrock Converse API
Python (boto3) Β· Amazon Bedrock Runtime
import boto3

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

response = client.converse(
    modelId="openai.gpt-oss-120b-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of gpt-oss-120b."}]
        }
    ],
    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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