Ling-3.0-flash (free) is free for input tokens while Sakana Namazu costs $0.95 per 1M tokens.
API cost decision in 10 seconds
Sakana Namazu vs Ling-3.0-flash (free)
Pick Ling-3.0-flash (free) 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 Ling-3.0-flash (free) when budget is the priority.
On the standard 1M input plus 500K output workload, Ling-3.0-flash (free) is estimated at $0 vs $2.95 for Sakana Namazu, saving $2.95 (100% lower).
Cost-first pickLing-3.0-flash (free)
Context-first pickBoth models
Sample savings$2.95100%
10x traffic gap$29.5
The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $29.5. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
Ling-3.0-flash (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | Sakana Namazu | Ling-3.0-flash (free) |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Ling-3.0-flash (free) | $6.75 | $0 |
| Balanced workload | 1M input + 1M output | Ling-3.0-flash (free) | $4.95 | $0 |
| Output-heavy chatbot | 1M input + 5M output | Ling-3.0-flash (free) | $20.95 | $0 |
Ling-3.0-flash (free) is free for output tokens while Sakana Namazu costs $4 per 1M tokens.
Both models report the same context window at 262.14K tokens.
Ling-3.0-flash (free) is free for the standard workload while the other model is estimated at $2.95.
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
Ling-3.0-flash (free) has the lower input price; Ling-3.0-flash (free) has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, Ling-3.0-flash (free) 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 $0 for Ling-3.0-flash (free).
Choose Sakana Namazu when its provider, model quality, latency, or availability is more important than the numeric price/context winner.
Choose Ling-3.0-flash (free) when you care most about lower input-token price, and lower output-token price.
- On the standard 1M input plus 500K output workload, Ling-3.0-flash (free) is estimated at $0 vs $2.95 for Sakana Namazu, saving $2.95 (100% lower).
- Ling-3.0-flash (free) is free for the standard workload while the other model is estimated at $2.95.
- Ling-3.0-flash (free) is free for input tokens while Sakana Namazu costs $0.95 per 1M tokens.
- Ling-3.0-flash (free) is free for output tokens while Sakana Namazu costs $4 per 1M tokens.
- Both models report the same context window at 262.14K tokens.
| Feature | Sakana Namazu (Sakana) | Ling-3.0-flash (free) (inclusionAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $0.95 | $0 |
| Completion Price per 1M tokens | $4 | $0 |
| Sample Workload Cost 1M input + 500K output | $2.95 | $0 |
| Context Window | 262.14K | 262.14K |
| Release Date |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Ling-3.0-flash (free) | On the standard 1M input plus 500K output workload, Ling-3.0-flash (free) is estimated at $0 vs $2.95 for Sakana Namazu, saving $2.95 (100% lower). |
| High-volume input processing | Ling-3.0-flash (free) | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | Ling-3.0-flash (free) | 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
- No lower-cost same-provider swap is currently tracked for this pair.
- Grok 4.20 Multi-Agent offers 2M context with $2.5 sample workload cost.
- Grok 4.20 offers 2M context with $2.5 sample workload cost.
- DeepSeek V4 Flash 0731 offers 1.31M context with $0.17 sample workload cost.
- DeepSeek V4 Flash Latest offers 1.31M context with $0.11 sample workload cost.
- DeepSeek V4 Flash 0731 · DeepSeek · #1
- MiMo-V2.5 · Xiaomi · #2
- Hy3 · Tencent · #3
- Ox Alpha · stealth · #4
Cheaper alternatives
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Open cheapest modelsLarger context alternatives
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Open provider hubsSakana catalog
Review all tracked Sakana models before deciding whether this matchup is the right shortlist.
Open Sakana modelsinclusionAI catalog
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Open inclusionAI 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,...
*Ling-3.0-flash* is a *124B-parameter Mixture-of-Experts (MoE) model*, with approximately *5.1B parameters activated per token*. The model is designed with *token efficiency and production-scale agentic inference* as key priorities, enabling developers...