Model Marketplace

Qwen-Embedding

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Overview

This is a multilingual unified text vector model trained by Tongyi Lab based on Qwen3.7. Compared to text-embedding-v4, it has significantly improved performance in text retrieval, clustering, and classification. It achieves a 20% improvement on evaluation tasks such as MTEB multilingual, Chinese-English, and code retrieval. It supports user-defined vector dimensions ranging from 256 to 2560.

Input

Text

Output

Text

Features

Prefix Completion

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Asynchronously process requests in batches to reduce costs.View docs

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

Fine-tuning

Train models on sample data to better adapt them to specific tasks.View docs

Pricing

  • Text Input
    $0.07Per 1M tokens

Rate Limits & Context

  • Max Input
    131K
  • Context
    131K
  • TPMTokens Per Minute
    1M
  • RPMRequests Per Minute
    2K

API Reference

Call API
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import dashscope
from http import HTTPStatus
input_texts = "The quality of the clothes is excellent, very beautiful. It was worth the long wait. I like it and will come back to buy here again"

resp = dashscope.TextEmbedding.call(
model="qwen3.7-text-embedding",
input=input_texts
)
print(resp)