Verified 2026 API Rates • Prompt Caching & Batch ROI

DeepSeek-R1 (deepseek-reasoner) vs DeepSeek: DeepSeek V3.2

Compare real-world API costs for DeepSeek-R1 (deepseek-reasoner) ($0.55/$2.19) vs DeepSeek: DeepSeek V3.2 ($0.26/$0.38). Calculate Prompt Caching ROI & Batch API savings.

DeepSeek: DeepSeek V3.2 saves up to 53% 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

Inspect Full Specs →Connect API

DeepSeek

DeepSeek: DeepSeek V3.2

deepseek-v3.2

Base Input Rate: $0.26 / 1M
Output Rate: $0.38 / 1M
Prompt Cache Read: $0.13 / 1M (-50%)
Cache Write Surcharge: $0.00 (Free write)
Batch API Discount: Not Supported
Context Window: 163.84k tokens
Quality Index (MMLU): 88

Inspect Full Specs →Connect API

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 DeepSeek: DeepSeek V3.2:

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.

DeepSeek: DeepSeek V3.2 Caching Profile-50% read discount

Cached Read Price: $0.130 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 / Architecture	Standard Input / Output	Batch 50% Off Input / Output	Turnaround SLA	Optimal Use Cases
DeepSeek-R1 (deepseek-reasoner)	$0.55 / $2.19	Standard on-demand rate	Instant real-time	Bulk evaluation, dataset labeling, background summarization
DeepSeek: DeepSeek V3.2	$0.26 / $0.38	Standard on-demand rate	Instant real-time	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.

DeepSeek: DeepSeek V3.2 Context Capacity

Provides a 163.84k token context window with a maximum output limit of 147.456k 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

DeepSeek: DeepSeek V3.2:$72.08/mo

Delta Savings:-$100.40 (58%)

Architecture Verdict:DeepSeek: DeepSeek V3.2 delivers 58% lower monthly spend ($100.40/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

DeepSeek: DeepSeek V3.2:$41.30/mo

Delta Savings:-$64.70 (61%)

Architecture Verdict:DeepSeek: DeepSeek V3.2 delivers 61% lower monthly spend ($64.70/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

DeepSeek: DeepSeek V3.2:$223.00/mo

Delta Savings:-$401.75 (64%)

Architecture Verdict:DeepSeek: DeepSeek V3.2 delivers 64% lower monthly spend ($401.75/mo savings) for this workload profile.

ミリ秒単位の対話型シミュレータ

DeepSeek-R1 (deepseek-reasoner) vs DeepSeek: DeepSeek V3.2

エンタープライズの代表的な3大ユースケース（RAG検索、コーディングAgent、バッチ抽出）で精密試算：

Live 50ms Engine

月間リクエスト数 (Requests)100,000

入力トークン数/回 (Input Tokens)4,000

出力トークン数/回 (Output Tokens)1,000

Prompt キャッシュヒット率 (Cache Hit)70%

Batch API 分流比率0%

DeepSeek: DeepSeek V3.2 安価

月間推定削減額

DeepSeek-R1 (deepseek-reasoner)$324.20$3.242// 1,000リクエスト

DeepSeek: DeepSeek V3.2$105.60$1.056// 1,000リクエスト

Developer FAQs

Frequently Asked Questions: DeepSeek-R1 (deepseek-reasoner) vs DeepSeek: DeepSeek V3.2

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

1Which model is cheaper overall: DeepSeek-R1 (deepseek-reasoner) or DeepSeek: DeepSeek V3.2?

At base pricing, DeepSeek-R1 (deepseek-reasoner) costs $0.55/1M input and $2.19/1M output, while DeepSeek: DeepSeek V3.2 costs $0.26/1M input and $0.38/1M 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 DeepSeek: DeepSeek V3.2?

DeepSeek-R1 (deepseek-reasoner) offers a 74.55% read discount ($0.14/1M), whereas DeepSeek: DeepSeek V3.2 provides a 50% read discount ($0.13/1M). 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 DeepSeek: DeepSeek V3.2 to save 50%?

DeepSeek-R1 (deepseek-reasoner) does not have a native 50% batch endpoint. DeepSeek: DeepSeek V3.2 uses low base on-demand pricing.

4What are the context window limits and tiered pricing rules for DeepSeek-R1 (deepseek-reasoner) vs DeepSeek: DeepSeek V3.2?

DeepSeek-R1 (deepseek-reasoner) features a 128k context window with flat per-token pricing. In comparison, DeepSeek: DeepSeek V3.2 supports 163.84k tokens with flat per-token pricing.