Qwen3 Reranker 4B
Qwen3 Reranker 4B is a 4-billion-parameter reranking model from Qwen designed to score and reorder retrieved documents by relevance.
Qwen
DeepInfraReranking model for retrieval pipeline relevance
Qwen3 Reranker 4B is a reranking model developed by Qwen and released in June 2025, built on the Qwen3 model family. It is designed to take a query and a set of candidate documents and assign relevance scores, making it suitable for use as a second-stage ranker in retrieval-augmented generation (RAG) pipelines and information retrieval systems. The model supports a context window of 32,768 tokens, allowing it to process longer documents during scoring.
The model is hosted on DeepInfra under the identifier Qwen/Qwen3-Reranker-4B and is available for inference without requiring users to manage their own infrastructure. Its primary use case is improving the precision of retrieval systems by reordering an initial set of candidates retrieved by a first-stage retriever such as a dense or sparse embedding model. At 4 billion parameters, it sits in a range that balances scoring quality with inference cost for production retrieval workflows.
What Qwen3 Reranker 4B supports
Document Reranking
Scores and reorders a list of candidate documents against a query to improve retrieval precision. Operates as a second-stage ranker after an initial retrieval step.
Long Context Support
Accepts inputs up to 32,768 tokens, enabling relevance scoring of longer documents without truncation.
RAG Pipeline Integration
Designed to slot into retrieval-augmented generation pipelines as a reranking stage, improving the quality of context passed to a generation model.
Relevance Scoring
Produces a relevance score for each query-document pair, which can be used to filter or rank candidates before downstream processing.
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Get Started FreeCommon questions about Qwen3 Reranker 4B
What is the context window for Qwen3 Reranker 4B?
Qwen3 Reranker 4B supports a context window of 32,768 tokens, which applies to the combined query and document input during reranking.
What type of model is Qwen3 Reranker 4B?
It is a reranking model, meaning it takes a query and a set of retrieved documents and outputs relevance scores to reorder them. It is not a generative or embedding model.
How is Qwen3 Reranker 4B typically used in a pipeline?
It is typically used as a second-stage ranker after a first-stage retriever (such as a BM25 or dense embedding model) returns an initial set of candidates. The reranker then scores each candidate for relevance to the query.
What is the pricing for Qwen3 Reranker 4B on MindStudio?
Pricing information is not specified in the available metadata. You can check the DeepInfra provider page for current pricing details.
Who developed Qwen3 Reranker 4B and when was it released?
Qwen3 Reranker 4B was developed by Qwen and released in June 2025. It is part of the Qwen3 model family and is served via DeepInfra.
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