Jina Embeddings v3
Jina Embeddings v3 is Jina AI's flagship text embedding model, built for high-quality semantic representation across more than 100 languages. It delivers strong multilingual retrieval performance, making it well suited for globally oriented search pipelines and cross-language semantic matching tasks.
The model supports longer input sequences than earlier Jina embedding releases and is designed to perform well on asymmetric retrieval scenarios — where short queries are matched against longer passages. It fits naturally into RAG systems, semantic search applications, and multilingual document clustering pipelines.
Key Features
Multilingual support across 100+ languages
Strong performance on asymmetric query-to-passage retrieval
Longer input sequence handling for document-level embeddings
Suitable for clustering, classification, and similarity ranking
Compact, efficient vector output for low-latency search systems
Works with standard vector databases (Pinecone, Weaviate, Qdrant)
Ideal Use Cases
Multilingual semantic search over diverse document collections
Retrieval-augmented generation (RAG) knowledge retrieval
Cross-language document similarity and deduplication
Clustering large multilingual content libraries
Building recommendation systems based on content semantics
Example Prompts for Jina Embeddings v3
Technical Specifications
| Provider | Jina |
| Category | Embedding |
| Modality | Text -> Vector |
Frequently Asked Questions
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