DeepSeek V3.2 is $0.48 cheaper per 1M input tokens (65.5% lower; 2.9x difference).
API cost decision in 10 seconds
🔥DeepSeek V3.2 vs 🔥Kimi K2.6
Pick DeepSeek V3.2 for lower cost; pick Kimi K2.6 only if the larger context window matters more.
Budget verdict
Pick DeepSeek V3.2 for lower cost; pick Kimi K2.6 only if the larger context window matters more.
On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $2.48 for Kimi K2.6, saving $2.03 (82.2% lower).
Kimi K2.6 has more context, but DeepSeek V3.2 saves $2.03 on the standard workload. At 10x that traffic, the same price gap is about $20.34. Use the calculator below to replace the sample workload with your own token volume.
DeepSeek V3.2 is $3.11 cheaper per 1M output tokens (89.2% lower; 9.23x difference).
Kimi K2.6 has 131.07K more context (2x larger).
DeepSeek V3.2 is $2.03 cheaper on the standard workload (82.2% 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
DeepSeek V3.2 has the lower input price; DeepSeek V3.2 has the lower output price; Kimi K2.6 offers the larger context window. For the 1M input plus 500K output sample, DeepSeek V3.2 is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $0.44 for DeepSeek V3.2 and $2.48 for Kimi K2.6.
Choose DeepSeek V3.2 when you care most about lower input-token price, and lower output-token price.
Choose Kimi K2.6 when you care most about larger context window.
- On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $2.48 for Kimi K2.6, saving $2.03 (82.2% lower).
- DeepSeek V3.2 is $2.03 cheaper on the standard workload (82.2% lower).
- DeepSeek V3.2 is $0.48 cheaper per 1M input tokens (65.5% lower; 2.9x difference).
- DeepSeek V3.2 is $3.11 cheaper per 1M output tokens (89.2% lower; 9.23x difference).
- Kimi K2.6 has 131.07K more context (2x larger).
| Feature | 🔥DeepSeek V3.2 (DeepSeek) | 🔥Kimi K2.6 (MoonshotAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.252 | $0.73 |
| Completion Price per 1M tokens | $0.378 | $3.49 |
| Sample Workload Cost 1M input + 500K output | $0.44 | $2.48 |
| Context Window | 131.07K | 262.14K |
| Release Date | ||
| Popularity Rank current rank | #6 | #7 |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | DeepSeek V3.2 | On the standard 1M input plus 500K output workload, DeepSeek V3.2 is estimated at $0.44 vs $2.48 for Kimi K2.6, saving $2.03 (82.2% lower). |
| High-volume input processing | DeepSeek V3.2 | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | DeepSeek V3.2 | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Kimi K2.6 | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
- DeepSeek V4 Flash (free) can replace DeepSeek V3.2 when lower sample workload cost matters most: $0.
- DeepSeek V4 Flash can replace DeepSeek V3.2 when lower sample workload cost matters most: $0.25.
- R1 Distill Qwen 32B can replace DeepSeek V3.2 when lower sample workload cost matters most: $0.43.
- Kimi K2.5 can replace Kimi K2.6 when lower sample workload cost matters most: $1.35.
- Grok 4.1 Fast offers 2M context with $0.45 sample workload cost.
- Grok 4.20 offers 2M context with $2.5 sample workload cost.
- Grok 4 Fast offers 2M context with $0.45 sample workload cost.
- Owl Alpha offers 1.05M context with $0 sample workload cost.
- Hy3 preview · Tencent · #1
- Claude Opus 4.7 · Anthropic · #2
- Claude Sonnet 4.6 · Anthropic · #3
- DeepSeek V4 Flash · DeepSeek · #4
Cheaper alternatives
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Open MoonshotAI modelsDeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and...