Llama 3.2 3B Pricing & Token Calculator (Meta)

Input / Prompt

$0.07/ 1M tokens

$0.0001 per 1K tokens

Output / Completion

$0.07/ 1M tokens

$0.0001 per 1K tokens

Prompt Cache Read

$0.02/ 1M tokens

Save 75% vs prompt

Batch Inference

$0.04/ 1M in

Save 50% via Batch API

Price Ratio

1 : 1.0

Prompt to completion factor

Regional Reach

6AWS Regions

Compare 6 regions ↓

Llama 3.2 3B 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(75% 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: $0.07/1M · Output: $0.07/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with Llama 3.2 3B on AWS

Prompt CachingSave ~75%

Caches repetitive system instructions, schemas, and document prefixes in memory with a 5-minute TTL.

Read Rate: $0.02 / 1M tokens
Write Rate: $0.09 / 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: $0.04 / 1M tokens
Batch Output: $0.04 / 1M tokens
Ideal for: Offline classification, synthetic data generation, benchmark evals

Cross-Region & Tiers6 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 6 available AWS cloud regions

Baseline: $0.07/1M inGovCloud: $0.20/1M in6 Global Regions

AWS Region	Prompt Input	Completion Output	Cache Read	Batch Input	Pricing Tier
US East (N. Virginia)`us-east-1`	$0.15/1M	$0.15/1M	—/1M	—/1M	+100% vs Baseline
US West (Oregon)`us-west-2`	$0.07/1M	$0.15/1M	—/1M	—/1M	Baseline
EU (Frankfurt)`eu-central-1`	$0.10/1M	$0.19/1M	—/1M	—/1M	+27% vs Baseline
EU (Ireland)`eu-west-1`	$0.17/1M	$0.17/1M	—/1M	—/1M	+127% vs Baseline
EU (Paris)`eu-west-3`	$0.20/1M	$0.20/1M	—/1M	—/1M	+167% vs Baseline
US East (Ohio)`us-east-2`	$0.15/1M	$0.15/1M	—/1M	—/1M	+100% vs Baseline

Developer Quickstart

Invoke Llama 3.2 3B 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="meta.llama-3-2-3b-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of Llama 3.2 3B."}]
        }
    ],
    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")