DeepSeek-R1 (deepseek-reasoner) vs Qwen: Qwen3.5 397B A17B
Compare real-world API costs for DeepSeek-R1 (deepseek-reasoner) ($0.55/$2.19) vs Qwen: Qwen3.5 397B A17B ($0.6/$3.6). Calculate Prompt Caching ROI & Batch API savings.
DeepSeek-R1 (deepseek-reasoner)
- 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
Qwen: Qwen3.5 397B A17B
- Base Input Rate
- $0.60 / 1M
- Output Rate
- $3.60 / 1M
- Prompt Cache Read
- $0.12 / 1M (-80%)
- Cache Write Surcharge
- $0.00 (Free write)
- Batch API Discount
- -40% Off
- Context Window
- 262.144k tokens
- Quality Index (MMLU)
- 88
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 Qwen: Qwen3.5 397B A17B:
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.
Qwen: Qwen3.5 397B A17B Caching Profile-80% read discount
- Cached Read Price: $0.120 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.
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 |
| Qwen: Qwen3.5 397B A17B | $0.60 / $3.60 | $0.30 / $1.80 (-50%) | 24 Hours max (typically 30m - 2h) | Offline document translation, embedding backfills, test suites |
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.
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.
Provides a 262.144k token context window with a maximum output limit of 64k tokens.✓ Flat-rate billing across all prompt sizes up to max context ceiling.
Real-World Production Scenarios: Monthly Spend Projections
Pre-computed monthly engineering bill projections across 3 representative enterprise application workloads:
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.
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.
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) 對比 Qwen: Qwen3.5 397B A17B
針對企業典型三大工程落地形態（RAG 知識庫、Coding Agent 多輪、離線批次清洗）進行精確成本演算：
預計每月節省
Frequently Asked Questions: DeepSeek-R1 (deepseek-reasoner) vs Qwen: Qwen3.5 397B A17B
Authoritative answers on API rate limits, pricing math, break-even thresholds, and architectural migration.
1Which model is cheaper overall: DeepSeek-R1 (deepseek-reasoner) or Qwen: Qwen3.5 397B A17B?
At base pricing, DeepSeek-R1 (deepseek-reasoner) costs $0.55/1M input and $2.19/1M output, while Qwen: Qwen3.5 397B A17B costs $0.6/1M input and $3.6/1M 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 Qwen: Qwen3.5 397B A17B?
DeepSeek-R1 (deepseek-reasoner) offers a 74.55% read discount ($0.14/1M), whereas Qwen: Qwen3.5 397B A17B provides a 80% read discount ($0.12/1M). 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 Qwen: Qwen3.5 397B A17B to save 50%?
DeepSeek-R1 (deepseek-reasoner) does not have a native 50% batch endpoint. Qwen: Qwen3.5 397B A17B supports 50% batch discounts.
4What are the context window limits and tiered pricing rules for DeepSeek-R1 (deepseek-reasoner) vs Qwen: Qwen3.5 397B A17B?
DeepSeek-R1 (deepseek-reasoner) features a 128k context window with flat per-token pricing. In comparison, Qwen: Qwen3.5 397B A17B supports 262.144k tokens with flat per-token pricing.