GLM 5 Pricing & Token Calculator (Zhipu AI (GLM))

Input / Prompt

$1.00/ 1M tokens

$0.0010 per 1K tokens

Output / Completion

$3.20/ 1M tokens

$0.0032 per 1K tokens

Prompt Cache Read

$0.25/ 1M tokens

Save 75% vs prompt

Batch Inference

$0.50/ 1M in

Save 50% via Batch API

Price Ratio

1 : 3.2

Prompt to completion factor

Regional Reach

13AWS Regions

Compare 13 regions ↓

Alternatives to GLM 5

Option	Price vs GLM 5	Why look at it
GLM 4.7 Compare →	input-40%-$0.40/1M output-31%-$1.00/1M Standard tier, per 1M tokens	Next step down in price from Zhipu AI (GLM)
GLM 4.7 Flash Compare →	input-93%-$0.93/1M output-87%-$2.80/1M Standard tier, per 1M tokens	Same General line, different version (lightweight tier)

GLM 5 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: $1.00/1M · Output: $3.20/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with GLM 5 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: $0.25 / 1M tokens
Write Rate (5m TTL): $1.25 / 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: $0.50 / 1M tokens
Batch Output: $1.60 / 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: $1.00/1M inGovCloud: $1.55/1M in13 Global Regions

AWS Region	Prompt Input $/1M	Completion Output $/1M	Cache Read $/1M	Batch Input $/1M	Pricing Tier
us-east-1 US East (N. Virginia)	$1.00	$3.20	—	$0.50	Baseline
us-west-2 US West (Oregon)	$1.00	$3.20	—	$0.50	Baseline
eu-central-1 EU (Frankfurt)	$1.20	$3.84	—	$0.60	+20% vs Baseline
eu-west-1 EU (Ireland)	$1.20	$3.84	—	$0.60	+20% vs Baseline
ap-northeast-1 Asia Pacific (Tokyo)	$1.20	$3.84	—	$0.60	+20% vs Baseline
ap-southeast-3 Asia Pacific (Jakarta)	$1.20	$3.84	—	$0.60	+20% vs Baseline
ap-south-1 Asia Pacific (Mumbai)	$1.20	$3.84	—	$0.60	+20% vs Baseline
ap-southeast-2 Asia Pacific (Sydney)	$1.03	$3.30	—	$0.52	+3% vs Baseline
eu-west-2 EU (London)	$1.55	$4.96	—	$0.78	+55% vs Baseline
eu-south-1 EU (Milan)	$1.20	$3.84	—	$0.60	+20% vs Baseline
eu-north-1 EU (Stockholm)	$1.20	$3.84	—	$0.60	+20% vs Baseline
sa-east-1 South America (Sao Paulo)	$1.20	$3.84	—	$0.60	+20% vs Baseline
us-east-2 US East (Ohio)	$1.00	$3.20	—	$0.50	Baseline

Developer Quickstart

Invoke GLM 5 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="zhipu-ai.glm-5-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of GLM 5."}]
        }
    ],
    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")