Llama-3.1-Namazu-405BvsQwen3.6-27B
Sakana AI vs Alibaba
Overview
A battle of open-weight models. Sakana AI's Llama-3.1-Namazu-405B is a massive 405B-parameter model optimized for Japanese cultural context. Alibaba's Qwen3.6-27B uses a 27B-parameter MoE architecture with strong coding performance: SWE-bench Pro 53.5, LiveCodeBench 83.9.
Specs & Pricing
| Llama-3.1-Namazu-405B | Qwen3.6-27B | |
|---|---|---|
| Developer | Sakana AI | Alibaba |
| Type | foundation | foundation |
| Parameters | 405B | 非公開 |
| Context Window | 128K | — |
| Open Source | ✓ | ✓ |
| Input Price (/1M tokens) | — | — |
| Output Price (/1M tokens) | — | — |
Recommendations by Use Case
Which model fits your task best
Llama-3.1-Namazu-405B was post-trained by Sakana AI specifically for Japanese cultural and social context, giving it superior Japanese quality and neutrality.
Qwen3.6-27B demonstrates strong coding performance with SWE-bench Pro 53.5 and LiveCodeBench 83.9 — verifiable benchmark results.
Open Source→Tie
Both models are released as open-weight, allowing self-hosted deployment and customization.
Frequently Asked Questions
Which has better Japanese quality?
Llama-3.1-Namazu-405B is clearly superior. Sakana AI post-trained it specifically for Japanese cultural context, delivering high quality in natural Japanese, keigo, and business writing.
Does more parameters mean better performance?
Not necessarily. Namazu is massive at 405B, but Qwen3.6-27B achieves strong coding performance at 27B through its MoE architecture. The best model depends on your use case.