Jina Colbert V2

jina-reranker-v3.5 is a 0.6B-parameter multilingual listwise document reranker and a drop-in upgrade to jina-reranker-v3. It keeps the same last-but-not-late (LBNL) listwise interface — ranking a query against many candidates in one forward pass — while improving domain robustness, structured-data ranking, multilingual retrieval, and inference efficiency.

A 3.8-billion-parameter general vector model (embedding model) for state-of-the-art multilingual embeddings for edge deployment.

A 3.8-billion-parameter general vector (embedding) model providing state-of-the-art multilingual embeddings with task-specific adapters.

DeepSearch combines search, reading, and reasoning capabilities to pursue the best possible answer. It's fully compatible with OpenAI's Chat API format—just replace api.openai.com with aihubmix.com to get started. The stream will return the thinking process.

A general-purpose vector model with 3.8 billion parameters, used for multimodal and multilingual retrieval, supporting both unidirectional and multi-vector embedding outputs.

Multimodal multilingual document reranker, 131K context, 0.6B parameters, for visual document sorting.