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Today we release jina-reranker-v3.5, a 0.6B-parameter listwise reranker that keeps the last but not late interaction of jina-reranker-v3 and makes it faster and far more capable on the data enterprises actually search. It reaches 63.20 nDCG@10 on BEIR, ahead of Qwen3-Reranker-4B with roughly 7× fewer parameters, and it reranks up to 1.56× faster than v3 on long documents. Its biggest jump is on semi-structured retrieval: +9.6 nDCG@10 over v3 on field-constrained records.
Three changes get us there. A hybrid attention schedule that replaces most global layers with sliding windows while pinning the terminal layer to global. A training mixture curated from the failure modes of legal, medical, financial, multilingual, and structured retrieval. And a three-stage self-distillation recipe where teacher and student are the same size and differ only in attention pattern.
Quality against parameter count on the four benchmark regimes. jina-reranker-v3.5 is on the Pareto front in all four: no model we evaluated is both smaller and better.
English zero-shot retrieval on BEIR across 13 datasets, measured by nDCG@10 after reranking the top-100 candidates from jina-embeddings-v5-text-small. The red line traces the Pareto front and the shaded region is dominated: for every model inside it, some other model is smaller, better, or both. jina-reranker-v3.5 defines the front outright at 63.20, above the 1.5B mxbai-rerank-large-v2 at 62.45 and the 4B Qwen3-Reranker-4B at 62.28. No larger model we evaluated buys any BEIR quality over it.
