Sakana Namazu is $0.55 cheaper per 1M input tokens (36.7% lower; 1.58x difference).
API cost decision in 10 seconds
Sakana Namazu vs Gemini 3.5 Flash
Pick Sakana Namazu for lower cost; pick Gemini 3.5 Flash only if the larger context window matters more.
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Data confirmed:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick Sakana Namazu for lower cost; pick Gemini 3.5 Flash only if the larger context window matters more.
On the standard 1M input plus 500K output workload, Sakana Namazu is estimated at $2.95 vs $6 for Gemini 3.5 Flash, saving $3.05 (50.8% lower).
Cost-first pickSakana Namazu
Context-first pickGemini 3.5 Flash
Sample savings$3.0550.8%
10x traffic gap$30.5
Gemini 3.5 Flash has more context, but Sakana Namazu saves $3.05 on the standard workload. At 10x that traffic, the same price gap is about $30.5. 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 | Gemini 3.5 Flash |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Sakana Namazu | $6.75 | $12 |
| Balanced workload | 1M input + 1M output | Sakana Namazu | $4.95 | $10.5 |
| Output-heavy chatbot | 1M input + 5M output | Sakana Namazu | $20.95 | $46.5 |
Sakana Namazu is $5 cheaper per 1M output tokens (55.6% lower; 2.25x difference).
Gemini 3.5 Flash has 786.43K more context (4x larger).
Sakana Namazu is $3.05 cheaper on the standard workload (50.8% 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; Gemini 3.5 Flash offers the larger 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 $6 for Gemini 3.5 Flash.
Choose Sakana Namazu when you care most about lower input-token price, and lower output-token price.
Choose Gemini 3.5 Flash when you care most about larger context window.
- On the standard 1M input plus 500K output workload, Sakana Namazu is estimated at $2.95 vs $6 for Gemini 3.5 Flash, saving $3.05 (50.8% lower).
- Sakana Namazu is $3.05 cheaper on the standard workload (50.8% lower).
- Sakana Namazu is $0.55 cheaper per 1M input tokens (36.7% lower; 1.58x difference).
- Sakana Namazu is $5 cheaper per 1M output tokens (55.6% lower; 2.25x difference).
- Gemini 3.5 Flash has 786.43K more context (4x larger).
| Feature | Sakana Namazu (Sakana) | Gemini 3.5 Flash (Google) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.95 | $1.5 |
| Completion Price per 1M tokens | $4 | $9 |
| Sample Workload Cost 1M input + 500K output | $2.95 | $6 |
| Context Window | 262.14K | 1.05M |
| 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 $6 for Gemini 3.5 Flash, saving $3.05 (50.8% 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 | Gemini 3.5 Flash | 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 provider hubsSakana catalog
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Open Sakana modelsGoogle catalog
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Open Google 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,...
Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution...