text-embedding-3-large supports a wide range of languages and can generate meaningful embeddings for multilingual text. It is trained on diverse language data, allowing it to represent semantic meaning across many commonly used languages without requiring language-specific configuration.
In practical terms, developers can embed text in languages such as English, Chinese, Japanese, Korean, Spanish, French, German, and many others. The model handles multilingual input naturally, meaning you can store documents in multiple languages and query them in the same language with reliable results. This is useful for global documentation portals, international customer support systems, or multilingual content analysis. While retrieval quality may vary slightly by language and domain, the model generally performs well for standard use cases.
When these multilingual embeddings are stored in a vector database like Milvus or Zilliz Cloud, language becomes just another attribute. The database indexes vectors the same way regardless of language, and developers can add language tags as metadata for filtering if needed. This makes text-embedding-3-large a practical choice for systems that need to operate across regions without maintaining separate models or pipelines for each language.
For more information, click here： https://zilliz.com/ai-models/text-embedding-3-large