Model Library

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B)

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B)

Qwen3-Reranker-0.6B scores how well a document answers a query — and it does it in a way no other reranker does. Conventional rerankers are classifiers with a scoring head. This one is a language model asked a yes-or-no question, with the relevance score read from how strongly it leans toward "yes". That design brings two things its classical counterparts lack: a 32,768-token input window, which is enough to rank whole documents rather than fragments, and support for a task-specific instruction that Qwen measure as worth one to five percent. Six hundred million parameters, Apache 2.0, and over a hundred languages.

The llama-nemotron-rerank-vl-1b-v2 is a 1.7B parameter multimodal reranking model designed to evaluate and order the relevance of document images and text against specific user queries. It excels at understanding complex visual content like charts, tables, and infographics.