A rerank model re-scores documents returned by initial retrieval, surfacing the most relevant results at the top.
Model overview
ImportantThe gte-rerank model will be discontinued on May 30, 2026. Switch to qwen3-rerank.
Singapore
Model
Max documents
Max input tokens per item
Max input tokens per request
Supported languages
Scenarios
qwen3-rerank
500
4,000
120,000
Over 100 major languages, such as Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, and Russian
Text semantic retrieval
RAG applications
Beijing
Model
Max documents
Max input tokens per item
Max input tokens per request
Supported languages
Scenarios
qwen3-vl-rerank
Text: 100
Image: 40
Video: 4
8,000
120,000
33 major languages, such as Chinese, English, Japanese, Korean, French, and German
Image clustering
Cross-modal search
Image retrieval
Video retrieval
gte-rerank-v2
500
4,000
30,000
Over 50 languages, such as Chinese, English, Japanese, Korean, Thai, Spanish, French, Portuguese, German, Indonesian, and Arabic
Text semantic retrieval
RAG applications
Max input tokens per item: Maximum tokens per query or document. If a query or document exceeds this limit, the API returns an HTTP 400 error and does not truncate the input.
Max documents: Maximum documents per request. For qwen3-vl-rerank, the limit varies by document type (text, image, video, or mixed).
Max input tokens per request: Calculated as Query Tokens × Number of documents + Total document tokens. Must not exceed the per-request limit.
Input limitations
Model
Image
Video
qwen3-vl-rerank
JPEG, PNG, WEBP, BMP, TIFF, ICO, DIB, ICNS, and SGI (URL or Base64 supported)
qwen3-rerank: POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-api/v1/reranks
qwen3-vl-rerank / gte-rerank-v2: POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank
Replace {WorkspaceId} with your actual workspace ID.
The two APIs differ in request body structure and response format. See the request and response examples for each model.
Request
Request headers
Content-Typestring(Required)
The content type of the request. Must be application/json.
Authorizationstring(Required)
Authenticates the request with a Model Studio API key. Example: Bearer sk-xxxx.
Request body
modelstring(Required)
The model name. Supported values: qwen3-rerank, gte-rerank-v2, qwen3-vl-rerank.
inputobject(Required)
Input content.
For qwen3-rerank without the input object, place query and documents at the same level as model.
Properties
querystring | object(Required)
Query text (max 4,000 tokens).
qwen3-vl-rerank supports two query formats:
String format: Pass a text string directly, e.g., "query": "What is a rerank model?".
Object format: Pass a dictionary specifying modality type and value as {"modality type": "input content"}. Supported types: text and image.
Text query: "query": {"text": "What is a text rerank model?"}
Image query: "query": {"image": "Image URL or Base64-encoded string"}
documentsarray(Required)
Candidate documents to sort. Each element is a string.
qwen3-vl-rerank accepts a dictionary or string per element: {"modality type": "text/image URL/video URL"}. Supported types: text, image, video.
Text: Key is text, value is a string. A plain string without a dictionary wrapper also works.
Image: Key is image, value is a URL or Base64 Data URI (data:image/{format};base64,{data}, where {format} is jpeg/png and {data} is the encoded string).
Video: Key is video, value must be a publicly accessible URL.
parametersobject(Optional)
Optional parameters.
For qwen3-rerank, the parameters object is not required. Place top_n and instruct at the same level as model.
Properties
top_nint(Optional)
Number of top-ranked documents to return. Default: all documents. Values exceeding total documents return all.
return_documentsbool(Optional)
Whether to include document text in results. Default: false. Supported models: gte-rerank-v2, qwen3-vl-rerank.
instructstring(Optional)
Custom sorting instruction. Applies to qwen3-rerank and qwen3-vl-rerank. Guides the model to apply different sorting policies. Examples:
Q&A retrieval task (default): "Given a web search query, retrieve relevant passages that answer the query."
Focus: Find answers. Model prioritizes whether a document answers the query.
Example: Query "How to prevent a cold?" - Document "Washing hands frequently prevents colds" scores high; "A cold is a common illness" scores low (topically relevant but doesn't answer).
Semantic similarity sorting task: "Retrieve semantically similar text."
Focus: Determine semantic equivalence. Model evaluates whether query and document have consistent core meaning, regardless of wording or structure.
Example: In FAQ scenarios, "How do I change my password?" and "What if I forget my password?" are semantically similar (high score). Model focuses on whether both reflect the same user intent.
Write instructions in English. Default: Q&A retrieval task. More instructions are available in the model repository.
# This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region.
curl --request POST \
--url https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/reranks \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "qwen3-rerank",
"documents": [
"Rerank models are widely used in search engines and recommendation systems. They sort candidate documents based on text relevance.",
"Quantum computing is a cutting-edge field of computer science.",
"The development of pre-trained language models has brought new advancements to rerank models."
],
"query": "What is a rerank model?",
"top_n": 2,
"instruct": "Given a web search query, retrieve relevant passages that answer the query."
}'
Replace WorkspaceId with your actual workspace ID.
qwen3-vl-rerank
Text query
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"model": "qwen3-vl-rerank",
"input":{
"query": {"text": "What is a rerank model?"},
"documents": [
{"text": "Rerank models are widely used in search engines and recommendation systems. They sort candidate documents based on text relevance."},
{"image": "https://img.alicdn.com/imgextra/i3/O1CN01rdstgY1uiZWt8gqSL_!!6000000006071-0-tps-1970-356.jpg"},
{"video": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"}
]
},
"parameters": {
"return_documents": true,
"top_n": 2,
"fps": 1.0
}
}'
Image query
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"model": "qwen3-vl-rerank",
"input":{
"query": {"image": "https://img.alicdn.com/imgextra/i3/O1CN01rdstgY1uiZWt8gqSL_!!6000000006071-0-tps-1970-356.jpg"},
"documents": [
{"text": "Text rerank models are widely used in search engines and recommendation systems to sort candidate captions based on text relevance."},
{"image": "https://img.alicdn.com/imgextra/i3/O1CN01rdstgY1uiZWt8gqSL_!!6000000006071-0-tps-1970-356.jpg"},
{"video": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"}
]
},
"parameters": {
"return_documents": true,
"top_n": 2,
"fps": 1.0
}
}'
gte-rerank-v2
curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"model": "gte-rerank-v2",
"input":{
"query": "What is a rerank model?",
"documents": [
"Rerank models are widely used in search engines and recommendation systems. They sort candidate documents based on text relevance.",
"Quantum computing is a cutting-edge field of computer science.",
"The development of pre-trained language models has brought new advancements to rerank models."
]
},
"parameters": {
"return_documents": true,
"top_n": 2
}
}'
dashscope CLI
Call via the dashscope command line.
export DASHSCOPE_API_KEY="your-api-key"
# Replace {WorkspaceId} with your workspace ID, ap-southeast-1 with your region (e.g., us-east-1, eu-central-1)
export DASHSCOPE_HTTP_BASE_URL="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1"
dashscope rerank create -m gte-rerank-v2 -q "machine learning" -d "Machine learning is a branch of AI" -d "The weather is really nice today"
ImportantThe SDK Expert interactive assistant can accomplish the same development and troubleshooting via natural language, see
{
"output": {
"results": [
{
"document": {
"text": "Rerank models are widely used in search engines and recommendation systems. They sort candidate documents based on text relevance."
},
"index": 0,
"relevance_score": 0.9334521178273196
},
{
"document": {
"text": "The development of pre-trained language models has brought new advancements to rerank models."
},
"index": 2,
"relevance_score": 0.34100082626411193
}
]
},
"usage": {
"total_tokens": 79
},
"request_id": "85ba5752-1900-47d2-8896-23f99b13f6e1"
}
Failed response
If a request fails, code and message indicate the error cause.
SDK parameter names match the HTTP API, but the structure differs. HTTP uses nested input and parameters objects; the SDK uses a flat structure.
import dashscope
# China (Beijing) region. Replace {WorkspaceId} with your actual workspace ID. The configuration varies by region.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"
def text_rerank():
resp = dashscope.TextReRank.call(
model="gte-rerank-v2",
query="What is a rerank model?",
documents=[
"Rerank models are widely used in search engines and recommendation systems. They sort candidate documents based on text relevance.",
"Quantum computing is a cutting-edge field of computer science.",
"The development of pre-trained language models has brought new advancements to rerank models."
],
top_n=2,
return_documents=True
)
print(resp)
if __name__ == '__main__':
text_rerank()
Use qwen3-vl-rerank for multimodal reranking with an image query.
import dashscope
from http import HTTPStatus
import json
# China (Beijing) region. Replace {WorkspaceId} with your actual workspace ID.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"
def vl_rerank():
resp = dashscope.TextReRank.call(
model="qwen3-vl-rerank",
query={"image": "https://img.alicdn.com/imgextra/i3/O1CN01rdstgY1uiZWt8gqSL_!!6000000006071-0-tps-1970-356.jpg"},
documents=[
{"text": "Rerank models are widely used in search engines and recommendation systems. They sort candidate documents based on text relevance."},
{"image": "https://img.alicdn.com/imgextra/i3/O1CN01rdstgY1uiZWt8gqSL_!!6000000006071-0-tps-1970-356.jpg"},
{"video": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"}
],
top_n=2,
return_documents=True
)
if resp.status_code == HTTPStatus.OK:
print(json.dumps(resp, default=str, ensure_ascii=False, indent=4))
else:
print(resp)
if __name__ == '__main__':
vl_rerank()
Sample output
NoteThe SDK wraps the HTTP response. For successful requests, code and message are always empty strings.
{
"status_code": 200,
"request_id": "4b0805c0-6b36-490d-8bc1-4365f4c89905",
"code": "",
"message": "",
"output": {
"results": [
{
"index": 0,
"relevance_score": 0.9334521178273196,
"document": {
"text": "Rerank models are widely used in search engines and recommendation systems. They sort candidate documents based on text relevance."
}
},
{
"index": 2,
"relevance_score": 0.34100082626411193,
"document": {
"text": "The development of pre-trained language models has brought new advancements to rerank models."
}
}
]
},
"usage": {
"total_tokens": 79
}
}
Error codes
If the model call fails and returns an error message, see Error codes for resolution.