When the world was still debating ChatGPT and Claude, Alibaba’s Tongyi Qianwen (Qwen) quietly rewrote Japan’s AI landscape. This Chinese large language model not only secured 6th place in Nikkei’s AI rankings with Qwen2.5-Max but also became the development backbone for Japan’s tech giants like ABEJA and ELYZA. From Tokyo University labs to Sapporo medical institutions, Qwen is proving that China’s AI prowess is the real game-changer in global competition.
Tongyi Qianwen’s Disruptive Performance in Japan’s AI Benchmark ??
In April 2025, Nikkei’s AI model rankings dropped like a bombshell—Qwen2.5-Max scored 71.96 globally, outperforming DeepSeek-V3 (71.57) and OpenAI’s o3-mini (70.01). The real shocker? Japan’s local champion ABEJA-Qwen2.5-32B was actually fine-tuned from Qwen, meaning a Chinese engine powered Japan’s victory.
Click to view Nikkei’s key metrics comparison
| Model | Global Avg | Reasoning | Japanese NLP |
|---|---|---|---|
| Qwen2.5-Max | 71.96 | 83.50 | 91.2 |
| DeepSeek-V3 | 71.57 | 80.71 | 87.5 |
| Llama-3-70B | 68.3 | 75.8 | 82.1 |
Dr. Rie Sato from Nomura Research Institute noted that Qwen achieved 90%+ accuracy with just one-third of Japanese training data, thanks to its cross-lingual transfer learning framework. For example, medical AI firm Axcxept built its EZo model on Qwen and passed Japan’s medical AI certification in 3 months—a record-breaking feat.
How Tongyi Qianwen Became Japan’s "Open-Source Bible" ??
While Meta was busy with Llama-3’s "parameter wars," Qwen built an ecosystem with 100,000+ derivative models. Japanese developers love Qwen for three killer features:
Zero-barrier fine-tuning: ABEJA engineers revealed that customizing Qwen2.5-32B for enterprise use required just 500 lines of code, with 47% lower training costs than Llama.
Hybrid-modal architecture: ELYZA’s vision-reasoning system based on Qwen-VL reduced misdiagnosis rates by 62% when analyzing CT scans and medical records simultaneously.
Japanese dialect adaptation: Sapporo startup Lightblue created a Hokkaido dialect speech recognizer in two weeks using Qwen-Audio, hitting 98.3% accuracy??.
The game-changer? Alibaba Cloud’s on-premises data security solution lets Japanese companies process sensitive data locally—no overseas transfers required.
From Qwen2.5 to Qwen3: China’s AI Global Playbook ??
The QwQ-32B model (March 2025) delivers 671B-level performance with just 32B parameters, thanks to:
20 trillion training tokens—equivalent to feeding Wikipedia to the model 200 times across 119 languages (including Okinawan dialects).
Dynamic MoE architecture that auto-activates expert modules, cutting energy use by 53%?.
RLHF-enhanced alignment: University of Tokyo tests showed Qwen scored 18% higher than Claude 3 on Japanese ethics evaluations.
Alibaba’s Qwen Lab confirmed Qwen3 will train on 36 trillion tokens with quantum compression—meaning billion-parameter models could soon run on smartwatches?.
