Qwen3.5 397B A17B is $0.01 cheaper per 1M input tokens (2.5% lower; 1.03x difference).
API cost decision in 10 seconds
Qwen3.5 397B A17B vs Kimi K2.5
Pick Kimi K2.5 when budget is the priority.
Pricing data updated:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick Kimi K2.5 when budget is the priority.
On the standard 1M input plus 500K output workload, Kimi K2.5 is estimated at $1.35 vs $1.56 for Qwen3.5 397B A17B, saving $0.21 (13.5% lower).
Cost-first pickKimi K2.5
Context-first pickBoth models
Sample savings$0.2113.5%
10x traffic gap$2.1
The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $2.1. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
Kimi K2.5 stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | Qwen3.5 397B A17B | Kimi K2.5 |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Kimi K2.5 | $3.12 | $2.95 |
| Balanced workload | 1M input + 1M output | Kimi K2.5 | $2.73 | $2.3 |
| Output-heavy chatbot | 1M input + 5M output | Kimi K2.5 | $12.09 | $9.9 |
Kimi K2.5 is $0.44 cheaper per 1M output tokens (18.8% lower; 1.23x difference).
Both models report the same context window at 262.14K tokens.
Kimi K2.5 is $0.21 cheaper on the standard workload (13.5% lower).
Estimate your workload cost
Your Workload Cost
This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.
Quick Decision
Qwen3.5 397B A17B has the lower input price; Kimi K2.5 has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, Kimi K2.5 is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $1.56 for Qwen3.5 397B A17B and $1.35 for Kimi K2.5.
Choose Qwen3.5 397B A17B when you care most about lower input-token price.
Choose Kimi K2.5 when you care most about lower output-token price.
- On the standard 1M input plus 500K output workload, Kimi K2.5 is estimated at $1.35 vs $1.56 for Qwen3.5 397B A17B, saving $0.21 (13.5% lower).
- Kimi K2.5 is $0.21 cheaper on the standard workload (13.5% lower).
- Qwen3.5 397B A17B is $0.01 cheaper per 1M input tokens (2.5% lower; 1.03x difference).
- Kimi K2.5 is $0.44 cheaper per 1M output tokens (18.8% lower; 1.23x difference).
- Both models report the same context window at 262.14K tokens.
| Feature | Qwen3.5 397B A17B (Qwen) | Kimi K2.5 (MoonshotAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.39 | $0.4 |
| Completion Price per 1M tokens | $2.34 | $1.9 |
| Sample Workload Cost 1M input + 500K output | $1.56 | $1.35 |
| Context Window | 262.14K | 262.14K |
| Release Date |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Kimi K2.5 | On the standard 1M input plus 500K output workload, Kimi K2.5 is estimated at $1.35 vs $1.56 for Qwen3.5 397B A17B, saving $0.21 (13.5% lower). |
| High-volume input processing | Qwen3.5 397B A17B | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | Kimi K2.5 | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Tie | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
- Qwen3 Next 80B A3B Instruct (free) can replace Qwen3.5 397B A17B when lower sample workload cost matters most: $0.
- Qwen3 Coder 480B A35B (free) can replace Qwen3.5 397B A17B when lower sample workload cost matters most: $0.
- Qwen2.5 7B Instruct can replace Qwen3.5 397B A17B when lower sample workload cost matters most: $0.09.
- Qwen3.5-9B can replace Qwen3.5 397B A17B when lower sample workload cost matters most: $0.11.
- Llama 4 Scout offers 10M context with $0.23 sample workload cost.
- Owl Alpha offers 1.05M context with $0 sample workload cost.
- Gemini 3.1 Flash Lite offers 1.05M context with $1 sample workload cost.
- DeepSeek V4 Pro offers 1.05M context with $0.87 sample workload cost.
- No popular competitor is currently available.
Cheaper alternatives
Review low-cost models sorted by a standard 1M input plus 500K output workload.
Open cheapest modelsLarger context alternatives
Find models with larger context windows for RAG, long documents, and codebase review.
Open largest context modelsProvider catalogs
Compare models within provider hubs before choosing a final API vendor.
Open provider hubsQwen catalog
Review all tracked Qwen models before deciding whether this matchup is the right shortlist.
Open Qwen modelsMoonshotAI catalog
Check other MoonshotAI models with comparable pricing, context, or release timing.
Open MoonshotAI modelsThe Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...
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