Seedance 2.5 Now Supports 11 Languages — Here's What It Means for Global Creators
Chinese, English, Spanish, Japanese, Korean, and 6 more languages are now supported natively. Here's how multilingual prompting affects output quality.
What's Inside
Seedance 2.5 introduced native multilingual support for 11 languages: Chinese, English, Spanish, Indonesian, Malay, Thai, Arabic, Portuguese, Vietnamese, Japanese, and Korean.
This isn't just a translation feature — it means you can write prompts in your native language and the model understands them natively. No more translating to English and losing nuance.
I tested prompt quality across 5 languages to see if there are differences in output quality. Here's what I found.
Full Language Support Breakdown
English and Chinese produce the highest quality results, which makes sense given ByteDance's development. The model's training data is richest in these two languages, so prompts in English or Chinese get the most precise interpretation.
Japanese, Korean, and Spanish show strong results — slightly behind English/Chinese but well ahead of what you'd expect from machine translation. The model handles cultural context and idiomatic expressions surprisingly well.
The remaining languages (Indonesian, Malay, Thai, Arabic, Portuguese, Vietnamese) work well for straightforward prompts but may struggle with very nuanced or culturally specific descriptions. For most practical use cases, they're perfectly usable.
One interesting finding: Seedance 2.5 no longer generates random subtitles or unwanted BGM in non-English outputs — a common issue with earlier versions. The "clean output" improvement applies across all supported languages.
Frequently Asked Questions
What languages does Seedance 2.5 support?
Seedance 2.5 supports 11 languages: Chinese, English, Spanish, Indonesian, Malay, Thai, Arabic, Portuguese, Vietnamese, Japanese, and Korean. You can write prompts in any of these languages natively.
Is prompt quality the same in all languages?
English and Chinese produce the highest quality results due to training data richness. Japanese, Korean, and Spanish are strong. Other languages work well for straightforward prompts but may struggle with very nuanced descriptions.