Input / Prompt
$2.64/ 1M tokens
$0.0026 per 1K tokens
Output / Completion
$15.84/ 1M tokens
$0.0158 per 1K tokens
Prompt Cache Read
$0.13/ 1M tokens
Save 95% vs prompt
Batch Inference
$1.32/ 1M in
Save 50% via Batch API
Price Ratio
1 : 6.0
Prompt to completion factor
Regional Reach
1AWS Regions

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GPT-5.6-TERRA 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: $2.64/1M · Output: $15.84/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with GPT-5.6-TERRA on AWS
Prompt CachingSave ~95%

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

  • Read Rate: $0.13 / 1M tokens
  • Write Rate (5m TTL): $1.65 / 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: $1.32 / 1M tokens
  • Batch Output: $7.92 / 1M tokens
  • Ideal for: Offline classification, synthetic data generation, benchmark evals
Long Context (>200k)High-Capacity Tier

Dedicated high-memory execution for enterprise documents and large context windows exceeding 200,000 tokens.

  • Long Context Input: $2.64 / 1M tokens
  • Long Context Output: $11.88 / 1M tokens
  • Ideal for: Full codebase ingest, multi-book synthesis, large video transcripts

Regional Pricing Matrix

Live rates across all 1 available AWS cloud regions
Baseline: $2.64/1M in1 Global Regions
Showing 1 benchmark regions
AWS RegionPrompt Input $/1MCompletion Output $/1MCache Read $/1MBatch Input $/1MPricing Tier
us-gov-west-1
AWS GovCloud (US-West)
$2.64$15.84$0.26$1.32GovCloud

Developer Quickstart

Invoke GPT-5.6-TERRA 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="openai.gpt-5-6-terra-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of GPT-5.6-TERRA."}]
        }
    ],
    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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