Sakana Namazu is $0.08 cheaper per 1M input tokens (7.5% lower; 1.08x difference).
API cost decision in 10 seconds
Sakana Namazu vs Qwen3.6 Max Preview
Pick Sakana Namazu when budget is the priority.
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Data confirmed:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick Sakana Namazu when budget is the priority.
On the standard 1M input plus 500K output workload, Sakana Namazu is estimated at $2.95 vs $4.11 for Qwen3.6 Max Preview, saving $1.16 (28.2% lower).
Cost-first pickSakana Namazu
Context-first pickBoth models
Sample savings$1.1628.2%
10x traffic gap$11.58
The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $11.58. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
Sakana Namazu stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | Sakana Namazu | Qwen3.6 Max Preview |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Sakana Namazu | $6.75 | $8.22 |
| Balanced workload | 1M input + 1M output | Sakana Namazu | $4.95 | $7.19 |
| Output-heavy chatbot | 1M input + 5M output | Sakana Namazu | $20.95 | $31.84 |
Sakana Namazu is $2.16 cheaper per 1M output tokens (35.1% lower; 1.54x difference).
Both models report the same context window at 262.14K tokens.
Sakana Namazu is $1.16 cheaper on the standard workload (28.2% 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
Sakana Namazu has the lower input price; Sakana Namazu has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, Sakana Namazu is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $2.95 for Sakana Namazu and $4.11 for Qwen3.6 Max Preview.
Choose Sakana Namazu when you care most about lower input-token price, and lower output-token price.
Choose Qwen3.6 Max Preview when its provider, model quality, latency, or availability is more important than the numeric price/context winner.
- On the standard 1M input plus 500K output workload, Sakana Namazu is estimated at $2.95 vs $4.11 for Qwen3.6 Max Preview, saving $1.16 (28.2% lower).
- Sakana Namazu is $1.16 cheaper on the standard workload (28.2% lower).
- Sakana Namazu is $0.08 cheaper per 1M input tokens (7.5% lower; 1.08x difference).
- Sakana Namazu is $2.16 cheaper per 1M output tokens (35.1% lower; 1.54x difference).
- Both models report the same context window at 262.14K tokens.
| Feature | Sakana Namazu (Sakana) | Qwen3.6 Max Preview (Qwen) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.95 | $1.027 |
| Completion Price per 1M tokens | $4 | $6.162 |
| Sample Workload Cost 1M input + 500K output | $2.95 | $4.11 |
| Context Window | 262.14K | 262.14K |
| Release Date |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Sakana Namazu | On the standard 1M input plus 500K output workload, Sakana Namazu is estimated at $2.95 vs $4.11 for Qwen3.6 Max Preview, saving $1.16 (28.2% lower). |
| High-volume input processing | Sakana Namazu | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | Sakana Namazu | 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
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Cheaper alternatives
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Open Qwen modelsSakana Namazu is a Japanese-specialized reasoning model from Sakana AI, based on Kimi K2.6 with additional training for Japanese language and business contexts. It is suited for Japanese instruction following,...
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