You should use text-embedding-3-large when your application depends on deep semantic understanding and you can afford slightly higher compute or storage costs. It is most appropriate when text is long, dense, or technically nuanced, and when retrieval errors are costly or hard to explain to users.
Common scenarios include enterprise search, research document retrieval, legal or policy text analysis, and complex developer documentation search. In these cases, smaller or simpler embeddings may miss subtle distinctions, such as scope, constraints, or intent. text-embedding-3-large captures more of this information, which leads to better retrieval and clustering. It is also a good choice when you expect users to ask vague or underspecified questions, because the model handles implicit meaning more reliably.
From an infrastructure standpoint, the model fits well into systems built around Milvus or Zilliz Cloud. You can prototype locally with Milvus and then scale using Zilliz Cloud without changing your data model. If cost or latency becomes a concern later, you can selectively apply text-embedding-3-large only to critical collections while using smaller embeddings elsewhere. This selective use is common in mature systems that balance quality and efficiency.
For more information, click here： https://zilliz.com/ai-models/text-embedding-3-large