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Qwen/Qwen3-Reranker-8B cover image

Qwen/Qwen3-Reranker-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)

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)

Public
$0.050 / Mtoken
32,768
ProjectPaperLicense

HTTP/cURL API

You can use cURL or any other http client to run inferences:

curl -X POST \
    -d '{"queries": ["What is the capital of United States of America?"], "documents": ["The capital of USA is Washington DC."]}'  \
    -H "Authorization: bearer $DEEPINFRA_TOKEN"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/Qwen/Qwen3-Reranker-8B'
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which will give you back something similar to:

{
  "scores": [
    0.1,
    0.2,
    0.3
  ],
  "input_tokens": 42,
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

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Input fields

queriesarray

queries to rerank. It should have the same length as documents


documentsarray

documents to rerank. It should have the same length as queries


webhookfile

The webhook to call when inference is done, by default you will get the output in the response of your inference request

Input Schema

Output Schema