Jina Embeddings v4
Jina Embeddings v4 encodes text, images and visually rich documents. It supports dense search and multi-vector matching, with dedicated adapters for retrieval, text matching and code.
How the model works
Jina adapts Qwen2.5-VL-3B-Instruct and uses mean pooling for its dense output. The default dense vector has 2,048 dimensions, with trained reductions to 128. Multi-vector retrieval instead produces 128-dimensional token representations. The context limit is 32,768 tokens. Tables, figures and page layout can therefore contribute directly to document retrieval.Jina Embeddings v4 model details
Use and access
The model card explicitly corrects an early license error: Qwen Research License applies, not the initially listed CC BY-NC 4.0. Merged vLLM task packages and GGUF files remain distributions of this model. The API and downloaded model are separate access routes. Do not infer commercial weight rights from the availability of hosted inference or a converted package.Jina Embeddings v4 model detailsJina Embeddings v4 technical overview
Specifications & access
| Detail | Value and conditions |
|---|---|
| Launch date | |
| Access | API, Downloadable |
| Model identifiers | jina-embeddings-v4jinaai/jina-embeddings-v4jina-embeddings-v4-vllm-retrievaljina-embeddings-v4-vllm-text-matchingjina-embeddings-v4-vllm-code |
| Context window | 32,768 tokens Maximum input sequence in the model card; serving limits can be lower. Jina Embeddings v4 model details |
| Parameters | Approximately 3.8 billionJina Embeddings v4 model details |
| Embedding dimensions | 2,048 dense; 128 per token for multi-vector outputJina Embeddings v4 model details |
| Weight license | Qwen Research License; corrected from an earlier CC BY-NC labelJina Embeddings v4 model details |
| Base model | Qwen2.5-VL-3B-InstructJina Embeddings v4 model details |
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