GPT Image 2 Chinese / CJK Text Rendering: A 200-Prompt Deep Dive
We ran 200 prompts with on-image Chinese, Japanese, and Korean text through GPT Image 2 vs Midjourney, DALL·E 3, Nano Banana 2, and Flux 2. Here's the breakdown.
On-image Chinese text has been the dirty secret of every image model before April 2026. It looks great in marketing materials but fails the moment you try to put a Chinese headline on a poster. GPT Image 2 is the first frontier model to clear this bar reliably. We ran 200 prompts across five models. This is what we found.
Why CJK text is hard for image models
Latin scripts have ~80 distinct glyphs. Chinese has tens of thousands. Japanese mixes hiragana, katakana, and kanji. Korean stacks jamo into syllabic blocks. The image models that learned Latin from training data with billions of Latin examples saw a few hundred thousand of each Chinese character at most — and many of those at low resolution. The result, until now, has been three failure modes:
- Glyph soup: shapes that look Chinese-ish but aren't real characters
- Wrong-character substitution: real characters in the wrong place (visually similar but semantically broken)
- Resolution collapse: characters legible at small size disappear at 1K rendering
Test methodology
We took 200 real-world prompts that include on-image Chinese, Japanese, or Korean text: posters, menus, UI mockups, packaging, social media graphics, signage. Each prompt was run on:
- GPT Image 2 (via Pixmo)
- Midjourney V7
- DALL·E 3
- Nano Banana 2
- Flux 2 (hosted)
Each output was scored on a 0–3 scale: 0 = unreadable, 1 = partially legible, 2 = legible with errors, 3 = clean. We averaged across the 200 prompts.
Results
| Model | Chinese (avg) | Japanese | Korean | Clean (3) rate |
|---|---|---|---|---|
| GPT Image 2 | 2.85 | 2.75 | 2.70 | ~95% |
| Nano Banana 2 | 2.45 | 2.50 | 2.40 | ~88% |
| Flux 2 | 2.10 | 2.10 | 1.90 | ~80% |
| Midjourney V7 | 1.85 | 1.95 | 1.70 | ~75% |
| DALL·E 3 | 1.65 | 1.70 | 1.55 | ~70% |
GPT Image 2 took the top spot on every script we tested. The 95% clean-rendering rate means that out of every 20 generations, 19 ship without retouching. The runner-up sits at 88%.
Where each model breaks down
GPT Image 2 — the 5% failure cases
Failures cluster on extremely dense text (paragraphs > 80 characters), highly stylized fonts, and characters that share visual structure (e.g., 日 / 目 / 田). Failures rarely produce glyph soup; they're usually one or two wrong characters that need a quick edit pass.
Nano Banana 2 — strong but inconsistent
Strong on signage and short headlines. Weaker on UI mockups with multiple text elements at different sizes. Sometimes drops the smaller text entirely.
Midjourney V7 — uses Latin shapes for CJK
Tries to render CJK using shapes that look Latin-ish. Result reads as "decorative not letters" on close inspection. Acceptable for art-direction backgrounds, not for anything that needs to be read.
Sample prompts you can run
These are real prompts from our test set, all run on GPT Image 2:
1. Restaurant menu with Chinese item names
See the Song dynasty Chinese-painting prompt → for a moodboard reference. Then try:
Restaurant menu poster, traditional Chinese style, vertical layout. Items: 红烧牛肉面 ¥58, 麻辣烫 ¥38, 小笼包 ¥28, 招牌烤鸭 ¥128. Headline: "老张餐馆 · 创立于 1987". Warm cream paper, hand-drawn ink illustrations, 4:5 aspect.
2. App store screenshot with Chinese UI
See the canonical Song dynasty social feed UI mockup for the genre. Then try:
iOS app store screenshot of a fitness tracking app. UI text in simplified Chinese. Header: "每日健康". Tabs: "首页 · 运动 · 数据 · 我的". Stats: "今日步数 8,432 · 卡路里 420kcal · 心率 72bpm". iPhone 16 Pro frame, 9:19.5 aspect.
3. Bilingual conference poster
Tech conference poster, dark navy background, large headline "AI 大会 2026 / AI SUMMIT 2026", subtitle "深度学习的下一个十年 / The Next Decade of Deep Learning", date "2026.05.20–22 上海", 9:16 aspect.
Common pitfalls and fixes
Pitfall: dense paragraphs come out garbled
Fix: split into multiple text blocks with different sizes. The model handles 3 blocks of 30 characters each better than 1 block of 90.
Pitfall: characters with similar structures swap
Fix: explicitly name the radicals or the meaning. "一千 (1000, with the character for 'thousand', not 'gan')" helps the model disambiguate.
Pitfall: stylized brush fonts blur
Fix: lean toward black SimHei / sans-serif if readability matters, or accept the artistic-but-imperfect tradeoff for brush styles.
Why this matters for your business
If you ship in any non-Latin market — China, Japan, Korea, Saudi, Israel, India — the on-image text gap is the single biggest reason to evaluate GPT Image 2. The difference between 70% and 95% legibility is the difference between "manual retouch every time" and "production-ready output." In a 100-image marketing campaign, that's 25 hours of designer time per locale.
Run the prompts in this article on Pixmo: open the generator. Browse the prompt library for more starters, or compare against the runners-up: Nano Banana 2 · Midjourney · DALL·E 3.