Asia encompasses over 4.7 billion people across vastly different ethnic groups, cultures, and physical characteristics. Yet many AI image models, trained predominantly on Western datasets, struggle to distinguish between Japanese, Korean, Chinese, Vietnamese, and Thai features—often producing a generic "Asian" face that doesn't accurately represent any specific nationality.
In this comparison, we evaluate four models with notably different origins and training approaches. Qwen Image 2512 from Alibaba brings particularly strong Asian representation—developed by a Chinese company with access to diverse Asian training data, it often shows superior distinction between nationalities. Seedream V4.5 from ByteDance (the company behind TikTok/Douyin) also benefits from extensive Asian data exposure. Nano Banana Pro represents Google's Gemini 3 Pro capabilities, while Flux 2 Pro offers Black Forest Labs' Western-trained flagship.
The differences between East Asian nationalities are subtle but meaningful: Japanese faces tend toward refined, delicate features with cooler skin undertones; Korean features often show angular jawlines and the distinctive "glass skin" complexion; Northern Chinese faces typically display broader bone structure; while Southeast Asian features—Vietnamese and Thai—show warmer skin tones and softer facial structures.
These distinctions matter for anyone creating content for Asian markets, developing diverse character representations, or simply wanting accurate cultural depictions rather than homogenized stereotypes. Our tests reveal which models understand these nuances and which default to generic composites.
NoteQwen Image, developed in China, often shows the strongest distinction between Asian nationalities—particularly for East Asian features. This likely reflects more diverse Asian representation in its training data.