Mistral Large Pricing & Token Calculator (Mistral AI)

Input / Prompt

$4.00/ 1M tokens

$0.0040 per 1K tokens

Output / Completion

$12.00/ 1M tokens

$0.0120 per 1K tokens

Prompt Cache Read

$1.00/ 1M tokens

Save 75% vs prompt

Batch Inference

$2.00/ 1M in

Save 50% via Batch API

Price Ratio

1 : 3.0

Prompt to completion factor

Regional Reach

9AWS Regions

Compare 9 regions ↓

Mistral Large 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: $4.00/1M · Output: $12.00/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with Mistral Large on AWS

Prompt CachingSave ~75%

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

Read Rate: $1.00 / 1M tokens
Write Rate: $5.00 / 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: $2.00 / 1M tokens
Batch Output: $6.00 / 1M tokens
Ideal for: Offline classification, synthetic data generation, benchmark evals

Cross-Region & Tiers9 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 9 available AWS cloud regions

Baseline: $4.00/1M inGovCloud: $6.70/1M in9 Global Regions

AWS Region	Prompt Input	Completion Output	Cache Read	Batch Input	Pricing Tier
US East (N. Virginia)`us-east-1`	$4.00/1M	$12.00/1M	—/1M	—/1M	Baseline
US West (Oregon)`us-west-2`	$4.00/1M	$12.00/1M	—/1M	—/1M	Baseline
EU (Ireland)`eu-west-1`	$4.30/1M	$13.00/1M	—/1M	—/1M	+7% vs Baseline
Asia Pacific (Mumbai)`ap-south-1`	$4.80/1M	$14.40/1M	—/1M	—/1M	+20% vs Baseline
Asia Pacific (Sydney)`ap-southeast-2`	$5.20/1M	$15.60/1M	—/1M	—/1M	+30% vs Baseline
Canada (Central)`ca-central-1`	$4.60/1M	$13.80/1M	—/1M	—/1M	+15% vs Baseline
EU (London)`eu-west-2`	$5.20/1M	$15.60/1M	—/1M	—/1M	+30% vs Baseline
EU (Paris)`eu-west-3`	$5.20/1M	$15.60/1M	—/1M	—/1M	+30% vs Baseline
South America (Sao Paulo)`sa-east-1`	$6.70/1M	$20.20/1M	—/1M	—/1M	+68% vs Baseline

Developer Quickstart

Invoke Mistral Large 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="mistral-ai.mistral-large-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of Mistral Large."}]
        }
    ],
    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")