Laguna S 2.1 vs Sakana Namazu
Laguna S 2.1 and Sakana Namazu are closely matched at 41.4 and 43.2 on the LLM Stats Score. Laguna S 2.1 is 13.7x cheaper per token.
Poolside · Sakana AI · Updated for 2026
Which is better?
Laguna S 2.1 and Sakana Namazu are closely matched on the overall LLM Stats Score at 41.4 and 43.2.
On price, Laguna S 2.1 is roughly 13.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Laguna S 2.1 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 Laguna S 2.1
- cost matters — it's about 13.7x 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
6 reported for Laguna S 2.1 · 3 for Sakana Namazu
Laguna S 2.1 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, Laguna S 2.1 ($0.10/1M tokens) is 9.5x cheaper than Sakana Namazu ($0.95/1M tokens).
For output processing, Laguna S 2.1 ($0.20/1M tokens) is 20.0x cheaper than Sakana Namazu ($4.00/1M tokens).
In conclusion, Sakana Namazu is more expensive than Laguna S 2.1.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Laguna S 2.1 accepts 1,048,576 input tokens compared to Sakana Namazu's 256,000 tokens. Only Sakana Namazu specifies output context (256,000 tokens).
Input capabilities
Documented input modalities across available providers
Sakana Namazu supports multimodal inputs, whereas Laguna S 2.1 does not.
Sakana Namazu can handle both text and other forms of data like images, making it suitable for multimodal applications.
Laguna S 2.1
Sakana Namazu
License
Usage and distribution terms
Laguna S 2.1 is licensed under OpenMDW License v1.1, while Sakana Namazu uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
OpenMDW License v1.1
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Laguna S 2.1 was released on 2026-07-21, while Sakana Namazu was released on 2026-08-03.
Sakana Namazu is 0 month newer than Laguna S 2.1.
Jul 21, 2026
1 months ago
Aug 3, 2026
3 weeks ago
1w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Laguna S 2.1 is available from Poolside. Sakana Namazu is available from Sakana AI.
Laguna S 2.1
Sakana Namazu
Outputs Comparison
Judge for yourself.
Run your own prompts against Laguna S 2.1 and Sakana Namazu side-by-side, then vote on the output you prefer.
FAQ
Common questions about Laguna S 2.1 vs Sakana Namazu.