# Reranking Models

Reranking models score the relevance between a query and a set of candidate documents, enabling precision refinement in two-stage retrieval pipelines. After an initial recall phase using an embedding model, a reranker re-scores and reorders the candidates using a cross-encoder architecture with cross-attention, delivering more accurate relevance rankings.

* [<mark style="color:blue;">**Qwen3 VL Reranker 8B**</mark>](/cloud/ai/ai-model-hub/models/reranking-models/qwen3-vl-reranker-8b.md): A multimodal reranking model that scores query-document relevance across text and images, supporting precision refinement in retrieval pipelines across 30+ languages.


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