DeepSeek-V4-Pro-0813 vs Sakana Namazu: Specs & Benchmark Comparison
| Characteristic | DeepSeek-V4-Pro-0813 | Sakana Namazu |
|---|---|---|
| Company | DeepSeek | Sakana AI |
| Release Date | August 13, 2026 | August 3, 2026 |
| Parameters | 1.6T | — |
| Multimodal | No | Yes |
| Context (input) | 1.0M | 256K |
| Context (output) | 393K | 256K |
| Input Price / 1M | $1.32 | $0.95 |
| Output Price / 1M | $3.96 | $4.00 |
| Average Score | 0.8 | 0.9 |
Verdict
Both models show equal results — the choice depends on your specific use case.
Overall Performance
Both models show comparable average scores: DeepSeek-V4-Pro-0813 — 0.8, Sakana Namazu — 0.9.
API Cost
Sakana Namazu is 1.1x cheaper: input $0.95/1M vs $1.32/1M tokens.
Context Window
DeepSeek-V4-Pro-0813 supports a larger context: 1M vs 256K tokens.
Recency
Both models were released around the same time: 8/13/2026 and 8/3/2026.
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Frequently Asked Questions
Which is better for coding — DeepSeek-V4-Pro-0813 or Sakana Namazu?
Direct comparison on the SWE-Bench benchmark is not available. We recommend reviewing other metrics on the comparison page.
Which model is cheaper — DeepSeek-V4-Pro-0813 or Sakana Namazu?
Sakana Namazu is cheaper for input: $0.95 per 1M tokens vs $1.32.
Which has a larger context window — DeepSeek-V4-Pro-0813 or Sakana Namazu?
DeepSeek-V4-Pro-0813 supports a larger context: 1,000,000 tokens vs 256,000.
The DeepSeek-V4-Pro-0813 and Sakana Namazu comparison is updated for 2026. Data includes benchmark results, API pricing, context window size and other specifications. For more detailed information, visit the DeepSeek-V4-Pro-0813 or Sakana Namazu page. See also the complete list of AI model comparisons.