jina reranker v3: Listwise Document Reranker for SOTA Multilingual Retrieval Blog API AWS Azure GCP Arxiv [!TIP] GGUF with quantizations and MLX versions are now available. jina reranker v3 is a 0.6B parameter multilingual document reranker with a novel last but not late interaction architecture. Unlike ColBERT's separate encoding with multi vector matching, this model performs causal self attention between query and documents within the same context window, extracting contextual embeddings from the last token of each document. Built on Qwen3 0.6B with 28 transformer layers and a lightweight MLP projector (1024→512→256), it processes up to 64 documents simultaneously within 131K token context. The model achieves state of the art BEIR performance with 61.94 nDCG@10 while being 10× smaller than generative listwise rerankers. Model Size BEIR MIRACL MKQA CoIR jina reranker v3 0.6B 61.94 66.83 67.92 70.64 jina reranker v2 0.3B 57.06 63.65 67.90 56.14 jina reranker m0 2.4B 58.95 66.75 68.19 63.55 bge reranker v2 m3 0.6B 56.51 69.32 67.88 36.28 mxbai rerank base v2 0.5B 58.40 55.32 64.24 65.71 mxbai rerank large v2 1.5B 61.44 57.94 67.06 70.87 Qwen3 Reranker 0.6B 0.6B 56.28 57.70 65.34 65…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy