Qwen-Rerank

Copied!
Add to Compare
Embedding

Overview

Embedding

A text-ranking model trained on the Qwen LLM foundation performs relevance ranking for input queries and candidate documents. It supports over 100 languages and long-text inputs, and is suitable for applications such as text retrieval and RAG. Its performance is aligned with the open-source Qwen3-Rerank series models.

Input

Text

Output

Text

Features

Prefix Completion

Function Calling

Cache

Structured Outputs

Batches

Web Search

Pricing

  • Text Input
    $0.1Per 1M tokens

Context

Context
32.76K
Max Input
32.76K
Max Output
-

Rate Limits

  • RPMRequests Per Minute
    5.40K
  • TPMTokens Per Minute
    5B

API Reference

Get API Key
Copied!
123456789101112131415161718
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3-rerank",
    "input":{
         "query": "What is a text rerank model",
         "documents": [
         "Text rerank models are widely used in search engines and recommendation systems. They sort candidate texts based on text relevance",
         "Quantum computing is a cutting-edge field in computer science",
         "The development of pre-trained language models has brought new progress to text rerank models"
         ]
    },
    "parameters": {
        "return_documents": true,
        "top_n": 5
    }
}'