DeepSeek-V4-Pro-0813 vs Sakana Namazu
DeepSeek-V4-Pro-0813 and Sakana Namazu are closely matched at 52.4 and 42.2 on the LLM Stats Score. DeepSeek-V4-Pro-0813 is 3.1x cheaper per token.
DeepSeek · Sakana AI · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 and Sakana Namazu are closely matched on the overall LLM Stats Score at 52.4 and 42.2.
On price, DeepSeek-V4-Pro-0813 is roughly 3.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-0813 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-Pro-0813
- cost matters — it's about 3.1x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
- you need open weights you can self-host or fine-tune
Choose Sakana Namazu
- you want predictable pricing at $0.95/M input and $4.00/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
12 reported for DeepSeek-V4-Pro-0813 · 3 for Sakana Namazu
DeepSeek-V4-Pro-0813 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-Pro-0813 ($0.43/1M tokens) is 2.2x cheaper than Sakana Namazu ($0.95/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 4.6x cheaper than Sakana Namazu ($4.00/1M tokens).
In conclusion, Sakana Namazu is more expensive than DeepSeek-V4-Pro-0813.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to Sakana Namazu's 256,000 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 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-Pro-0813 does not.
Sakana Namazu can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
Sakana Namazu
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 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-Pro-0813 was released on 2026-08-13, while Sakana Namazu was released on 2026-08-03.
DeepSeek-V4-Pro-0813 is 0 month newer than Sakana Namazu.
Aug 13, 2026
3 weeks ago
1w newerAug 3, 2026
1 months ago
Knowledge 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-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Sakana Namazu is available from Sakana AI.
DeepSeek-V4-Pro-0813
Sakana Namazu
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Sakana Namazu side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Sakana Namazu.