DeepSeek-V4-Flash-0731 vs Sakana Namazu
DeepSeek-V4-Flash-0731 and Sakana Namazu are closely matched at 46.1 and 43.2 on the LLM Stats Score. DeepSeek-V4-Flash-0731 is 15.2x cheaper per token.
DeepSeek · Sakana AI · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 and Sakana Namazu are closely matched on the overall LLM Stats Score at 46.1 and 43.2.
On price, DeepSeek-V4-Flash-0731 is roughly 15.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Flash-0731
- cost matters — it's about 15.2x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you need open weights you can self-host or fine-tune
Choose Sakana Namazu
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 3 for Sakana Namazu
DeepSeek-V4-Flash-0731 and Sakana Namazudon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.09/1M tokens) is 10.6x cheaper than Sakana Namazu ($0.95/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 22.2x cheaper than Sakana Namazu ($4.00/1M tokens).
In conclusion, Sakana Namazu is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Sakana Namazu's 256,000 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 384,000 tokens, while Sakana Namazu is limited to 256,000 tokens.
Input capabilities
Documented input modalities across available providers
Sakana Namazu supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
Sakana Namazu can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
Sakana Namazu
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Sakana Namazu uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Sakana Namazu was released on 2026-08-03.
Sakana Namazu is 0 month newer than DeepSeek-V4-Flash-0731.
Jul 31, 2026
4 weeks ago
Aug 3, 2026
3 weeks ago
3d newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Flash-0731 is available from DeepInfra, Novita, Fireworks. Sakana Namazu is available from Sakana AI.
DeepSeek-V4-Flash-0731
Sakana Namazu
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Sakana Namazu side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Sakana Namazu.