The main limitation of jina-embeddings-v2-base-en is that it is designed specifically for English text. If your dataset includes other languages or mixed-language content, embedding quality will drop noticeably because the model is not trained to represent multilingual semantics. Developers working with global or multilingual datasets need to be aware of this constraint early, as it affects both indexing and query behavior.
Another practical limitation is that, while the model handles long inputs up to 8192 tokens, longer text does not always mean better embeddings. Very long documents can contain multiple topics, which can blur semantic focus. This can lead to less precise similarity search results when vectors are stored in systems like Milvus or Zilliz Cloud. Developers often still need to think carefully about chunking strategy, even though the model technically supports long sequences.
Finally, jina-embeddings-v2-base-en is a general-purpose model. It works well across many domains, but it is not specialized for highly technical, legal, or medical language. In such cases, similar-looking content may cluster well, but subtle domain-specific distinctions can be lost. This is not a flaw in implementation, but a natural tradeoff of using a broadly trained embedding model. Understanding these limits helps developers design pipelines that complement the model rather than expecting it to solve every semantic edge case on its own.
For more information, click here: https://zilliz.com/ai-models/jina-embeddings-v2-base-en