이 모델의 강점은 무엇인가요?

Optimized for Japanese cultural context Improved answer refusal rate from 72% to nearly 0% Open-source (Apache 2.0) Maintains inference performance equal to the base model

이 모델의 약점은 무엇인가요?

Relies on the base model (only post-training applied) Limited API availability Few evaluations on global benchmarks Still in alpha stage

어떤 용도에 가장 적합한가요?

Japanese chatbot Q&A on political/historical topics Content generation on Japanese culture/society Education and research use

모델 목록으로

Sakana AI오픈소스

Namazu-DeepSeek-V3.1-Terminus

Name: Namazu-DeepSeek-V3.1-Terminus
Author: Sakana AI

An open-source LLM specialized for Japan, developed by Sakana AI. Based on DeepSeek-V3.1-Terminus, this model has been fine-tuned through post-training to correct biases to fit Japanese cultural and social contexts. It features significant improvements in neutrality and accuracy in themes related to politics, history, and diplomacy.

파라미터

685B (MoE)

컨텍스트

128K

라이선스

Apache 2.0

출시일

2026-03-15

일본어 처리 능력

🇯🇵Native JP

Model developed by a Japanese company or specialized for Japanese. Highest Japanese understanding and generation capability.

API 가격

이 모델의 API 가격 정보는 현재 공개되지 않았습니다

강점

・Optimized for Japanese cultural context
・Improved answer refusal rate from 72% to nearly 0%
・Open-source (Apache 2.0)
・Maintains inference performance equal to the base model

약점

・Relies on the base model (only post-training applied)
・Limited API availability
・Few evaluations on global benchmarks
・Still in alpha stage

활용 사례

・Japanese chatbot
・Q&A on political/historical topics
・Content generation on Japanese culture/society
・Education and research use

심층 분석

Base Model

DeepSeek-V3.1-Terminus

671B params, 37B active

Context Window

163,840 tokens

Extended from base 128K

Input Price

$0.27/1M tokens

via DeepInfra

Output Price

$0.95/1M tokens

via DeepInfra

Specialization

Japanese Cultural & Social Contexts

Corrected biases for Japan

License

MIT

Open-source, commercially permissive

강점

・Specialized post-training corrects cultural and historical biases for Japanese contexts
・Open-source MIT license allows commercial use and self-hosting
・Significantly cheaper than comparable Western frontier models
・Maintains strong coding and agentic capabilities from DeepSeek-V3.1-Terminus base
・Extended 163K context window from some providers

약점

・Limited to no global benchmark data; performance in non-Japanese contexts unverified
・Dependent on DeepSeek V3.1-Terminus base, which trails newer DeepSeek V4 models
・Regional focus may limit multilingual versatility outside Japanese use cases
・Potential for residual biases or inaccuracies in specialized domains
・Community and ecosystem much smaller than major model families

경쟁사 비교

Model	Arena	SWE	GPQA	Price
DeepSeek-V3.1-Terminus (Original)	N/A	68.4%	80.7%	$0.27/$0.95 (per 1M tokens)
DeepSeek-V4 Pro	N/A	~68.5% (Verified)	Higher than V3.1-T	$0.30/$0.50 (per 1M tokens)
Sakana's Japanese-optimized LLM (Hypothetical)	N/A	N/A	N/A	Not publicly listed

개요

Namazu-DeepSeek-V3.1-Terminus is a specialized open-source large language model developed by Sakana AI, tailored specifically for Japanese linguistic and cultural contexts. Built upon the DeepSeek-V3.1-Terminus architecture (671B total parameters, 37B active per token), it undergoes targeted post-training to correct inherent biases and improve neutrality in sensitive areas such as politics, history, and diplomacy relevant to Japan. The model aims to provide more accurate and contextually appropriate responses for Japanese users and applications, addressing a critical gap where general-purpose models may fall short. The model inherits the strong technical foundation of DeepSeek-V3.1-Terminus, including its hybrid reasoning capabilities (thinking/non-thinking modes), support for structured tool calling, and competitive performance in coding and agentic benchmarks like SWE-Bench. However, its primary value proposition lies in its specialized alignment rather than raw benchmark dominance. It is positioned as a niche yet essential tool for developers and organizations requiring an AI that deeply understands Japanese societal norms and avoids Western-centric biases. From an operational standpoint, Namazu is deployed via API through infrastructure partners like DeepInfra at a highly competitive price point, undercutting most Western frontier models by an order of magnitude. Its open-source MIT license further enhances its accessibility for local deployment and customization. While it represents a significant step forward for Japan-focused AI, its performance on global benchmarks remains unpublicized, and it operates in a highly specialized segment of the market.

벤치마크 및 성능

Specific benchmark performance for Namazu-DeepSeek-V3.1-Terminus has not been publicly disclosed by Sakana AI. Its capabilities are derived from its base model, DeepSeek-V3.1-Terminus, and the effects of its specialized post-training. The base model's publicly available benchmarks provide a baseline for its technical competence: | Benchmark | DeepSeek-V3.1-Terminus Score | Context | |-----------|------------------------------|----------| | MMLU-Pro | 85.0 | Reasoning mode w/o tool use | | GPQA-Diamond | 80.7 | Reasoning mode w/o tool use | | SWE-Bench Verified | 68.4 | Agentic tool use | | SWE-bench Multilingual | 57.8 | Agentic tool use | | SimpleQA | 96.8 | Agentic tool use | | LiveCodeBench | 74.9 | Reasoning mode w/o tool use | | BrowseComp | 38.5 | Agentic tool use | | Terminal-bench | 36.7 | Agentic tool use | | Context Window | 163,840 tokens | Via DeepInfra | **Key Performance Notes:** - **Medical Reasoning:** The base model achieved a 94.5% accuracy and #68 rank in the MIR 2026 medical benchmark, demonstrating strong knowledge retention. - **Pricing Efficiency:** At $0.27/$0.95 per 1M tokens (input/output) via DeepInfra, it is approximately 97% cheaper on output than models like GPT-5.4. - **Global vs. Specialized Performance:** While strong on general and coding benchmarks, its accuracy and neutrality in *Japanese-specific* historical, political, and cultural contexts are its designed core differentiators, for which no standardized public benchmarks exist.

상세 비교

**Head-to-Head Comparisons:** 1. **Namazu-DeepSeek-V3.1-Terminus vs. Base DeepSeek-V3.1-Terminus** - **Price:** Identical pricing (~$0.27/$0.95 per 1M tokens). - **Context Window:** Both offer up to 163K tokens via providers like DeepInfra. - **Strengths:** Namazu offers specialized neutrality for Japan; the base model offers proven, general-purpose performance with published benchmarks. - **Weaknesses:** Namazu's global performance is unverified; the base model may contain cultural biases outside a Western/Chinese context. 2. **Namazu-DeepSeek-V3.1-Terminus vs. DeepSeek-V4 Pro** - **Price:** V4 Pro is slightly cheaper ($0.30/$0.50 per 1M tokens). - **Context Window:** V4 Pro has a 1M token context window, vastly larger. - **Performance:** V4 Pro surpasses V3.1-Terminus on all major benchmarks (e.g., ~68.5% on SWE-Bench Verified). - **Use Case:** Choose Namazu for Japan-specific accuracy; choose V4 Pro for highest general performance, long-context tasks, or agentic coding at scale. 3. **Namazu-DeepSeek-V3.1-Terminus vs. Japanese Enterprise Models (e.g., from NEC, Fujitsu)** - **Price:** Namazu is likely much cheaper due to open-source API pricing. Enterprise Japanese models are typically expensive, proprietary cloud services. - **Performance:** Enterprise models may have deeper integration with local business systems and potentially more refined cultural training, but are closed-source. - **Accessibility:** Namazu can be self-hosted and customized; enterprise models are vendor-locked.

커뮤니티 평가

The developer and researcher community reaction to Namazu-DeepSeek-V3.1-Terminus is currently niche and primarily focused within the Japanese tech ecosystem. Key observations include: - **Positive Reception in Japan:** There is significant interest among Japanese developers and companies seeking AI solutions that respect local cultural norms and avoid awkward or offensive outputs regarding sensitive historical topics. The model addresses a clear need. - **Skepticism from International Community:** Some global AI researchers view such region-specific models with caution, questioning whether they introduce new forms of censorship or if post-training sufficiently removes biases rather than just aligning them to a different cultural context. - **Developer Adoption:** Early adopters are likely Japanese startups and enterprises building customer-facing products (e.g., content moderation, educational tools, conversational AI) where cultural appropriateness is critical. - **Open-Source Contribution:** The MIT license has been well-received, with developers expressing interest in further fine-tuning for specific Japanese industries or dialects. The base model's strong coding ability is also seen as a practical advantage. - **Benchmark Scrutiny:** A common request in technical forums is for Sakana AI to release evaluations on Japanese-specific NLP benchmarks (e.g., JGLUE, MAQA) to validate its claims of improved neutrality and accuracy.

활용 사례

**1. Content Moderation and Brand Safety for Japanese Platforms:** - **Example:** A Japanese social media company uses Namazu to automatically filter user-generated content for culturally insensitive remarks about historical events (e.g., WWII), political discourse, or social issues. The model's training reduces false positives from cultural misunderstanding compared to a generic model. - **When to Choose:** Over the base DeepSeek model when the cost of a cultural misstep is high (brand damage, user backlash). **2. Localized Education and Historical Learning Tools:** - **Example:** An ed-tech app develops a chatbot tutor that answers student questions about Japanese history and politics. Namazu provides explanations that are balanced and adhere to mainstream Japanese educational perspectives. - **When to Choose:** Over a Western model (e.g., Claude, GPT) to ensure alignment with the national curriculum and avoid presenting narratives that are at odds with local textbooks. **3. Enterprise Customer Support for Japanese Markets:** - **Example:** A multinational automaker uses Namazu as the backbone for its Japanese-language customer service chatbot. It handles complaints, answers queries about products, and navigates sensitive topics (like product recalls) with the appropriate level of nuance and politeness expected in Japan. - **When to Choose:** Over a cheaper, general-purpose model when customer satisfaction scores and brand perception in Japan are key performance indicators. **4. Research on Bias Mitigation:** - **Example:** A university research group studies methods for debiasing LLMs for specific cultures. They use Namazu as a case study, comparing its outputs on politically sensitive prompts to the base model and other LLMs to analyze the effectiveness of its post-training. - **When to Choose:** As a specialized dataset and model for academic research into AI alignment and cultural adaptation.