DeepSeek-V4.1-Flash vs Sakana Namazu
DeepSeek-V4.1-Flash and Sakana Namazu are closely matched at 51.8 and 42.1 on the LLM Stats Score. DeepSeek-V4.1-Flash is 5.2x cheaper per token.
DeepSeek · Sakana AI · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash and Sakana Namazu are closely matched on the overall LLM Stats Score at 51.8 and 42.1.
On price, DeepSeek-V4.1-Flash is roughly 5.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 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-V4.1-Flash
- cost matters — it's about 5.2x cheaper per token
- you process long inputs — it offers a 1,040,000 token context window
- you want the most recent training data — it shipped Sep 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
20 reported for DeepSeek-V4.1-Flash · 3 for Sakana Namazu
DeepSeek-V4.1-Flash 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-V4.1-Flash ($0.22/1M tokens) is 4.3x cheaper than Sakana Namazu ($0.95/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 6.1x cheaper than Sakana Namazu ($4.00/1M tokens).
In conclusion, Sakana Namazu is more expensive than DeepSeek-V4.1-Flash.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Sakana Namazu's 256,000 tokens. DeepSeek-V4.1-Flash 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
Both DeepSeek-V4.1-Flash and Sakana Namazu support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash
Sakana Namazu
License
Usage and distribution terms
DeepSeek-V4.1-Flash 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-V4.1-Flash was released on 2026-09-10, while Sakana Namazu was released on 2026-08-03.
DeepSeek-V4.1-Flash is 1 month newer than Sakana Namazu.
Sep 10, 2026
1 weeks ago
1mo 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-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. Sakana Namazu is available from Sakana AI.
DeepSeek-V4.1-Flash
Sakana Namazu
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Sakana Namazu side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Sakana Namazu.