이 모델의 강점은 무엇인가요?

Exceptionally high Japanese language quality Lightweight and fast Enterprise adoption by over 100 companies Adopted by Japan's Digital Agency for Government AI

이 모델의 약점은 무엇인가요?

Short context length of 8K Non-commercial license Inferior to top models on global benchmarks Not suitable for complex reasoning on its own

어떤 용도에 가장 적합한가요?

Japanese chatbot Text summarization and proofreading Customer support Japanese content generation

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ELYZA조건부 오픈

Llama-3-ELYZA-JP-8B

Name: Llama-3-ELYZA-JP-8B
Price: 30 JPY
Author: ELYZA

An open-source model specialized for Japanese, developed by ELYZA. Based on Meta's Llama 3, it has been fine-tuned with a large amount of Japanese data, and despite having 8B parameters, the quality of Japanese generation is very high.

파라미터

컨텍스트

라이선스

Llama 3 License

출시일

2025-07-01

일본어 처리 능력

🇯🇵Native JP

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

API 가격

입력 가격 (1M 토큰당)

¥30

출력 가격 (1M 토큰당)

¥120

과금 모드: standard

강점

・Exceptionally high Japanese language quality
・Lightweight and fast
・Enterprise adoption by over 100 companies
・Adopted by Japan's Digital Agency for Government AI

약점

・Short context length of 8K
・Non-commercial license
・Inferior to top models on global benchmarks
・Not suitable for complex reasoning on its own

활용 사례

・Japanese chatbot
・Text summarization and proofreading
・Customer support
・Japanese content generation

심층 분석

Parameters

8.03B

Based on Meta Llama 3 8B Instruct

ELYZA Tasks 100

3.655 / 5.0

Beats GPT-3.5 Turbo (3.475)

Japanese MT-Bench

7.78 / 10.0

Comparable to GPT-3.5 Turbo (8.54)

Hugging Face Downloads

1,038,379

High community adoption

Inference Speed

241 tok/s

Benchmarked on RTX 5090

License

Llama 3 Community

Research & commercial use allowed

강점

・Best-in-class Japanese language quality for an 8B open-source model, surpassing base Llama 3 8B by +0.655 on ELYZA Tasks 100
・Fully open-weight model with commercial use permitted under Llama 3 Community License at no API cost
・Lightweight enough to run on consumer GPUs (4.9GB quantized) with strong inference speed (241 tok/s)

약점

・Struggles with complex multi-step reasoning tasks (e.g., 0/10 on Monty Hall probability problem)
・Limited to 4096 context length, significantly smaller than competitors offering 32K-128K tokens
・Japanese quality still well behind flagship models: 3.655 vs GPT-4o's 4.320 on ELYZA Tasks 100

경쟁사 비교

Model
Llama-3-ELYZA-JP-8B
Meta-Llama-3-8B-Instruct
GPT-3.5 Turbo
Gemini 1.0 Pro

개요

Llama-3-ELYZA-JP-8B is a Japanese-specialized variant of Meta's Llama 3 8B Instruct model, developed by ELYZA, Inc. (a KDDI subsidiary). Released in June 2024, it undergoes additional Japanese pre-training and instruction tuning using proprietary datasets, achieving substantial improvements over the base model: +0.655 points on ELYZA Tasks 100 (3.000 → 3.655) and +0.29 points on Japanese MT-Bench (7.49 → 7.78). The model positions itself as a cost-free, open-weight alternative to paid API models like GPT-3.5 Turbo and Gemini 1.0 Pro for Japanese applications. The development approach—fine-tuning an existing high-quality open model rather than building from scratch—allowed ELYZA to achieve competitive results in under 3 months at a cost in the tens of millions of yen. This "teach Japanese to a Stanford graduate" strategy (as ELYZA's CEO describes it) has proven effective, with the 8B model matching or exceeding GPT-3.5 Turbo on Japanese benchmarks despite having far fewer parameters. The model is freely available under the Meta Llama 3 Community License for both research and commercial use (with the caveat that entities with over 700M monthly active users must request a separate license). While the model excels at standard Japanese conversational and comprehension tasks (10/10 on Theory of Mind, 10/10 on reading comprehension), it reveals clear limitations in complex reasoning scenarios (0/10 on the Monty Hall problem) and operates with a modest 4096-token context window. In the broader Japanese LLM benchmark landscape, it ranks in the B Tier for summarization quality (ROUGE-L 0.258), behind newer models like Qwen3:8b (0.333) but ahead of several other 7-8B models.

벤치마크 및 성능

## Japanese-Specific Benchmarks | Model | ELYZA Tasks 100 (/5) | Japanese MT-Bench (/10) | Category | |-------|---------------------|------------------------|----------| | GPT-4o | 4.320 | 9.40 | Frontier | | Gemini 1.5 Pro | 4.275 | 9.39 | Frontier | | Llama-3-ELYZA-JP-70B | 4.070 | 9.08 | Frontier | | GPT-4 | 4.030 | 9.01 | Frontier | | **Llama-3-ELYZA-JP-8B** | **3.655** | **7.78** | **Lightweight** | | Gemini 1.0 Pro | 3.515 | 8.49 | Lightweight | | GPT-3.5 Turbo | 3.475 | 8.54 | Lightweight | | Meta-Llama-3-8B-Instruct | 3.000 | 7.49 | Lightweight | *Source: ELYZA official benchmarks via Chowagiken* ## Quantization Impact (ELYZA Tasks 100) | Variant | Score | Degradation | |---------|-------|-------------| | Llama-3-ELYZA-JP-8B (FP16) | 3.655 | Baseline | | Llama-3-ELYZA-JP-8B-GGUF (Q4_K_M) | 3.57 | -2.3% | | Llama-3-ELYZA-JP-8B-AWQ | 3.39 | -7.2% | *Source: Hugging Face model card* ## Summarization Benchmark (Japanese LLM Benchmark) | Model | ROUGE-L | Speed (tok/s) | Size | Tier | |-------|---------|---------------|------|------| | Qwopus3.5-9B | 0.533 | 196 | 5.4GB | S+ | | qwen3:8b | 0.333 | 204 | 5.2GB | S | | gemma3:12b | 0.303 | 141 | 8.1GB | A | | **Llama-3-ELYZA-JP-8B** | **0.258** | **241** | **4.9GB** | **B** | | llama3.2:3b | 0.246 | 427 | 2.0GB | B | *Source: shi3z/japanese-llm-benchmark (RTX 5090, April 2026)* ## Qualitative Testing (Chowagiken) | Task | Score | Notes | |------|-------|-------| | Theory of Mind (Sally-Anne) | 10/10 | Correct consistently | | Japanese Reading Comprehension | 10/10 | Accurate answer selection | | Monty Hall Problem | 0/10 | Fails complex probability reasoning | ## Technical Specifications | Property | Value | |----------|-------| | Parameters | 8.03B | | Architecture | Llama 3 (decoder-only transformer) | | Max Context Length | 4,096 tokens | | Precision | BF16 | | Languages | Japanese, English | | Framework | Transformers / vLLM compatible | | Device Support | GPU (CUDA), Qualcomm Snapdragon X Elite |

상세 비교

## Llama-3-ELYZA-JP-8B vs Meta-Llama-3-8B-Instruct (Base Model) The ELYZA variant represents a dramatic improvement for Japanese use cases. On ELYZA Tasks 100, the score jumps from 3.000 to 3.655 (+21.8% improvement). On Japanese MT-Bench, it improves from 7.49 to 7.78 (+3.9%). However, the base Llama 3 8B likely retains advantages on English-centric tasks and has a 8192-token context window (vs ELYZA's 4096). The ELYZA model is the clear choice for Japanese applications; the base model is preferable for multilingual or English-primary workloads. ## Llama-3-ELYZA-JP-8B vs GPT-3.5 Turbo (API) On ELYZA Tasks 100, ELYZA 8B actually scores higher (3.655 vs 3.475). However, GPT-3.5 Turbo edges ahead on Japanese MT-Bench (8.54 vs 7.78), likely due to stronger performance in coding and math subcategories. GPT-3.5 Turbo offers a 16K context window (4x larger) and is available as a managed API with no self-hosting required. For cost-sensitive deployments requiring full data control, ELYZA 8B is superior; for ease of use and broader task coverage, GPT-3.5 Turbo remains competitive. ## Llama-3-ELYZA-JP-8B vs qwen3:8b (Newer Competitor) Qwen3:8b, released in 2025, outperforms ELYZA 8B on summarization (ROUGE-L 0.333 vs 0.258) and keyword extraction (F1 0.899 vs not benchmarked). It also supports 128K context (vs 4096) and achieves comparable inference speed (204 vs 241 tok/s). The ELYZA model retains advantages in Japanese-specific instruction tuning and has a larger community footprint (1M+ downloads). For new deployments in 2026, Qwen3:8b generally offers better raw performance, but ELYZA 8B remains a proven, well-documented choice with established deployment patterns. ## Key Differentiators Summary | Feature | ELYZA 8B | GPT-3.5 Turbo | qwen3:8b | |---------|----------|---------------|----------| | Cost | Free (self-host) | ~$0.50/$1.50 per 1M tokens | Free (self-host) | | Context Window | 4,096 | 16,384 | 128,000 | | Japanese Benchmarks | Strong (3.655) | Slightly weaker (3.475) | Not directly compared | | Commercial License | ✅ | ✅ (via API ToS) | ✅ (Apache 2.0) | | Self-Hosting | ✅ Required | ❌ | ✅ Required | | Data Privacy | Full local control | Cloud-based | Full local control |

커뮤니티 평가

The model has achieved significant adoption with over 1 million total downloads on Hugging Face and 148 likes. It is featured in 31 Hugging Face Spaces and is part of a curated collection of "Llama-3 models augmented for Japanese usage" (7 models). The model has active community discussions (5 threads) and is served by Featherless AI as an inference provider. Japanese developers and researchers have generally praised the model for its practical utility. The Chowagiken evaluation notes it is suitable for "simple dialogue and text editing" and general-purpose use, while cautioning that complex reasoning tasks remain challenging. The Business Insider Japan coverage highlighted ELYZA's efficient development approach—achieving GPT-4-level Japanese performance using open models in just 3 months for tens of millions of yen, rather than the billions required for training from scratch. A notable community benchmark (shi3z/japanese-llm-benchmark) placed the model in the B Tier for Japanese summarization, behind newer models like Qwen3:8b but ahead of several contemporaries. The 241 tok/s inference speed on consumer hardware has been appreciated by self-hosting developers. The model's 4096 context window is frequently cited as its primary limitation in community discussions. The Qualcomm AI Hub listing indicates enterprise interest in edge deployment, with the model optimized for Snapdragon X Elite platforms with w4a16/w8a16 quantization. This suggests growing interest in on-device Japanese AI applications. ELYZA's partnership with KDDI (as a subsidiary) has added credibility, with the company pursuing call center applications through KDDI's Altius Link subsidiary.

활용 사례

### 1. Japanese Chatbot / Customer Support The model's 10/10 score on Theory of Mind testing and natural Japanese output make it well-suited for conversational AI applications. Its lightweight 8B parameter size allows deployment on a single consumer GPU, reducing infrastructure costs. Choose this over GPT-3.5 Turbo when you need full data sovereignty (e.g., handling sensitive customer data in Japan) and want to avoid per-token API costs. The 4096 context window is adequate for single-turn or short multi-turn conversations typical in customer support. ### 2. Japanese Text Summarization and Editing With strong Japanese language generation quality (ELYZA Tasks 100: 3.655), the model handles text summarization, proofreading, and content generation effectively. The 241 tok/s throughput makes it viable for batch processing of documents. For example, summarizing business reports, meeting notes, or news articles in Japanese. However, for documents exceeding 4096 tokens, chunking strategies are necessary. ### 3. On-Device Japanese AI (Edge/Mobile) The availability of AWQ and GGUF quantized versions (as small as ~2GB for GGUF Q4_K_M) enables deployment on edge devices, including Qualcomm Snapdragon X Elite laptops. This is ideal for offline Japanese AI assistants where internet connectivity is unreliable or data privacy is paramount. The model maintains acceptable quality even after 4-bit quantization (ELYZA Tasks 100 drops only from 3.655 to 3.57 with GGUF Q4_K_M). ### 4. Japanese Language Learning / Education Tools The model's ability to explain Japanese language nuances, generate grammatically correct text, and handle conversational context makes it suitable for Japanese language learning applications. Its free, open-source nature and permissive commercial license allow educational startups to integrate it without API budget concerns. For interactive language practice where responses need to be natural and contextually appropriate, this model outperforms the base Llama 3 8B significantly.