Verified 2026 API Rates • Prompt Caching & Batch ROI

DeepSeek-R1 (deepseek-reasoner) vs Z.ai: GLM 5.1

Compare real-world API costs for DeepSeek-R1 (deepseek-reasoner) ($0.55/$2.19) vs Z.ai: GLM 5.1 ($1.4/$4.4). Calculate Prompt Caching ROI & Batch API savings.

DeepSeek-R1 (deepseek-reasoner) saves up to 61% on base input tokens
DeepSeek

DeepSeek-R1 (deepseek-reasoner)

deepseek-r1
Base Input Rate
$0.55 / 1M
Output Rate
$2.19 / 1M
Prompt Cache Read
$0.14 / 1M (-74.55%)
Cache Write Surcharge
$0.00 (Free write)
Batch API Discount
Not Supported
Context Window
128k tokens
Quality Index (MMLU)
90.8
Zhipu AI

Z.ai: GLM 5.1

glm-5.1
Base Input Rate
$1.40 / 1M
Output Rate
$4.40 / 1M
Prompt Cache Read
$0.26 / 1M (-81.43%)
Cache Write Surcharge
$0.00 (Free write)
Batch API Discount
-50% Off
Context Window
204.8k tokens
Quality Index (MMLU)
88
Differential Module 1

Prompt Caching ROI & Write Surcharge Break-Even Analysis

Prompt caching drastically reduces input token costs for static prefixes like system prompts, tool schemas, and document chunks. Compare caching mechanisms between DeepSeek-R1 (deepseek-reasoner) and Z.ai: GLM 5.1:

DeepSeek-R1 (deepseek-reasoner) Caching Profile-74.55% read discount

  • Cached Read Price: $0.140 per 1M tokens.
  • Write Surcharge: $0.00 (automatic caching with zero write penalty).
  • Minimum Cache Prefix: 1,024 tokens.
  • Amortization Rule: Immediate net savings from first cached read call.

Z.ai: GLM 5.1 Caching Profile-81.43% read discount

  • Cached Read Price: $0.260 per 1M tokens.
  • Write Surcharge: $0.00 (automatic caching with zero write penalty).
  • Minimum Cache Prefix: 1,024 tokens.
  • Amortization Rule: Immediate net savings from first cached read call.
Differential Module 2

Batch API 50% Discount Offload vs Real-Time SLA

Asynchronous Batch APIs cut total token expenditures in half (50% off input and output) in exchange for non-realtime execution (up to 24-hour turnaround SLA).

Model / ArchitectureStandard Input / OutputBatch 50% Off Input / OutputTurnaround SLAOptimal Use Cases
DeepSeek-R1 (deepseek-reasoner)$0.55 / $2.19Standard on-demand rateInstant real-timeBulk evaluation, dataset labeling, background summarization
Z.ai: GLM 5.1$1.40 / $4.40$0.70 / $2.20 (-50%)24 Hours max (typically 30m - 2h)Offline document translation, embedding backfills, test suites
Differential Module 3

Context Window Escalation & Tiered Pricing Architecture

Massive context windows allow ingesting entire repositories or books, but pricing models differ drastically between flat-rate vendors and tiered step-up pricing.

DeepSeek-R1 (deepseek-reasoner) Context Capacity

Provides a 128k token context window with a maximum output limit of 8.192k tokens.✓ Flat-rate billing across all prompt sizes up to max context ceiling.

Z.ai: GLM 5.1 Context Capacity

Provides a 204.8k token context window with a maximum output limit of 131.072k tokens.✓ Flat-rate billing across all prompt sizes up to max context ceiling.

Quill Strategic Content

Real-World Production Scenarios: Monthly Spend Projections

Pre-computed monthly engineering bill projections across 3 representative enterprise application workloads:

Scenario 1

RAG Knowledge Base & Enterprise Search

100,000 queries/month, 3,500 input tokens (system + 5 retrieved chunks), 400 output tokens, 75% prompt cache hit rate.

DeepSeek-R1 (deepseek-reasoner):$172.48/mo
Z.ai: GLM 5.1:$366.75/mo
Delta Savings:-$194.27 (53%)
Architecture Verdict:DeepSeek-R1 (deepseek-reasoner) delivers 53% lower monthly spend ($194.27/mo savings) for this workload profile.
Scenario 2

Multi-Turn Coding Agent & ReAct Loops

25,000 tasks/month, 8,000 input tokens (accumulating conversation context & tool schemas), 1,200 output tokens, 85% cache hit rate.

DeepSeek-R1 (deepseek-reasoner):$106.00/mo
Z.ai: GLM 5.1:$201.83/mo
Delta Savings:-$95.83 (47%)
Architecture Verdict:DeepSeek-R1 (deepseek-reasoner) delivers 47% lower monthly spend ($95.83/mo savings) for this workload profile.
Scenario 3

High-Throughput Offline Data Extraction

500,000 records/month, 1,500 input tokens, 250 output tokens, 100% batch offload (24-hour turnaround SLA).

DeepSeek-R1 (deepseek-reasoner):$624.75/mo
Z.ai: GLM 5.1:$714.50/mo
Delta Savings:-$89.75 (13%)
Architecture Verdict:DeepSeek-R1 (deepseek-reasoner) delivers 13% lower monthly spend ($89.75/mo savings) for this workload profile.
毫秒级实时交互模拟引擎

DeepSeek-R1 (deepseek-reasoner) 对比 Z.ai: GLM 5.1

针对企业典型三大工程落地形态(RAG 知识库、Coding Agent 多轮、离线批量清洗)进行精确成本演算:

Live 50ms Engine
月度请求量 (Requests)100,000
单次输入 Tokens (Input)4,000
单次输出 Tokens (Output)1,000
Prompt 缓存命中率 (Cache Hit)70%
Batch API 批处理分流比例0%
DeepSeek-R1 (deepseek-reasoner) 更便宜

预计每月节省

DeepSeek-R1 (deepseek-reasoner)$324.20$3.242// 千次请求
对比
Z.ai: GLM 5.1$680.80$6.808// 千次请求
Developer FAQs

Frequently Asked Questions: DeepSeek-R1 (deepseek-reasoner) vs Z.ai: GLM 5.1

Authoritative answers on API rate limits, pricing math, break-even thresholds, and architectural migration.

1Which model is cheaper overall: DeepSeek-R1 (deepseek-reasoner) or Z.ai: GLM 5.1?

At base pricing, DeepSeek-R1 (deepseek-reasoner) costs $0.55/1M input and $2.19/1M output, while Z.ai: GLM 5.1 costs $1.4/1M input and $4.4/1M output. The cheaper option depends on your input-to-output token ratio and whether prompt caching or batch discounts are utilized.

2How does Prompt Caching impact the cost of DeepSeek-R1 (deepseek-reasoner) vs Z.ai: GLM 5.1?

DeepSeek-R1 (deepseek-reasoner) offers a 74.55% read discount ($0.14/1M), whereas Z.ai: GLM 5.1 provides a 81.43% read discount ($0.26/1M). High cache hit ratios (>70%) significantly compress the cost delta in agentic and RAG architectures.

3Can I use Batch API with DeepSeek-R1 (deepseek-reasoner) or Z.ai: GLM 5.1 to save 50%?

DeepSeek-R1 (deepseek-reasoner) does not have a native 50% batch endpoint. Z.ai: GLM 5.1 supports 50% batch discounts.

4What are the context window limits and tiered pricing rules for DeepSeek-R1 (deepseek-reasoner) vs Z.ai: GLM 5.1?

DeepSeek-R1 (deepseek-reasoner) features a 128k context window with flat per-token pricing. In comparison, Z.ai: GLM 5.1 supports 204.8k tokens with flat per-token pricing.