Qwen-Rerank
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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 Minute5.40K
- TPMTokens Per Minute5B
API Reference
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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
}
}'