MoonshotAI: Kimi K2.5 vs Qwen: Qwen3.5 397B A17B
Compare real-world API costs for MoonshotAI: Kimi K2.5 ($0.5/$2.5) vs Qwen: Qwen3.5 397B A17B ($0.6/$3.6). Calculate Prompt Caching ROI & Batch API savings.
MoonshotAI: Kimi K2.5
- Base Input Rate
- $0.50 / 1M
- Output Rate
- $2.50 / 1M
- Prompt Cache Read
- $0.15 / 1M (-70%)
- Cache Write Surcharge
- $0.00 (Free write)
- Batch API Discount
- -50% Off
- Context Window
- 262.144k tokens
- Quality Index (MMLU)
- 88
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 MoonshotAI: Kimi K2.5 and Qwen: Qwen3.5 397B A17B:
MoonshotAI: Kimi K2.5 Caching Profile-70% read discount
- Cached Read Price: $0.150 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 |
|---|---|---|---|---|
| MoonshotAI: Kimi K2.5 | $0.50 / $2.50 | $0.25 / $1.25 (-50%) | 24 Hours max (typically 30m - 2h) | 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 262.144k token context window with a maximum output limit of 235.929k 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).
MoonshotAI: Kimi K2.5 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: MoonshotAI: Kimi K2.5 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: MoonshotAI: Kimi K2.5 or Qwen: Qwen3.5 397B A17B?
At base pricing, MoonshotAI: Kimi K2.5 costs $0.5/1M input and $2.5/1M 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 MoonshotAI: Kimi K2.5 vs Qwen: Qwen3.5 397B A17B?
MoonshotAI: Kimi K2.5 offers a 70% read discount ($0.15/1M), 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 MoonshotAI: Kimi K2.5 or Qwen: Qwen3.5 397B A17B to save 50%?
MoonshotAI: Kimi K2.5 supports 50% batch offload discounts for asynchronous 24-hour turnaround tasks. Qwen: Qwen3.5 397B A17B supports 50% batch discounts.
4What are the context window limits and tiered pricing rules for MoonshotAI: Kimi K2.5 vs Qwen: Qwen3.5 397B A17B?
MoonshotAI: Kimi K2.5 features a 262.144k context window with flat per-token pricing. In comparison, Qwen: Qwen3.5 397B A17B supports 262.144k tokens with flat per-token pricing.