Qwen Text Embedding v4 overview
Qwen Text Embedding v4 by Alibaba Cloud
Turns text into vectors for search, clustering, and classification, with stronger retrieval quality than v3.
Qwen Text Embedding v4 specs
- Model ID
alibaba/qwen-text-embedding-v4- Developer
- Alibaba Cloud
- Input
- Text
- Endpoint
POST https://api.aimlapi.com/v1/embeddings
What Is Qwen Text Embedding v4 API?
Qwen Text Embedding v4 is Alibaba Cloud’s text embedding model for semantic search, RAG, clustering, and related text retrieval tasks. It is described as a multilingual model in the Qwen3-Embedding series, with support for 100+ languages.
Technical Reference
| Developer | Alibaba Cloud |
|---|---|
| Model ID | alibaba/qwen-text-embedding-v4 |
| Release Date | December 12, 2025 |
| Context Window | 8,192 tokens (~8K) |
| Modalities | Text → Embedding |
What Qwen Text Embedding v4 Brings to the Table
Multilingual embedding model
The model supports 100+ languages, including Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, and Russian.
Embedding dimensions
Alibaba Cloud documents list selectable dimensions of 2,048, 1,536, 1,024, 768, 512, 256, 128, or 64, with 1,024 marked as the default in the model studio documentation.
Text-focused input and output
The Chinese model information page lists the input modality as Text and the output modality as Text.
Improved retrieval quality over v3
The model is described as improving performance over Qwen embedding v3, with tighter intra-cluster cohesion and sharper inter-cluster separation, and better results on retrieval-related evaluations.
Where Qwen Text Embedding v4 Makes the Most Sense
Semantic search
The model is recommended for text search and semantic search workflows.
RAG
Documentation lists it as suitable for retrieval-augmented generation use cases.
Clustering
Qwen Cloud explicitly lists clustering as a use case for text-embedding-v4.
Classification
Alibaba Cloud documentation says it covers most text processing scenarios, including use cases that benefit from stronger text representations such as classification.
Qwen Text Embedding v4 vs the Alternatives
Qwen Text Embedding v3
Alibaba’s documentation says v4 improves on v3, including better intra-cluster cohesion and inter-cluster separation.
Sources: https://docs.aimlapi.com/api-references/embedding-models/alibaba-cloud/qwen-text-embedding-v4 · https://help.apiyi.com/en/text-embedding-v4-vector-dimensions-guide-en.html · https://help.aliyun.com/en/model-studio/embedding
Use cases
Text-embedding-v4 is documented as suitable for text retrieval, including semantic search and RAG workflows.
It is also suited to clustering, where text items are grouped by similarity for analysis or organization.
The model is documented for classification tasks, where embeddings are used to assign text to categories.
Alibaba Cloud also says it can be used for code retrieval and code snippet vectorization performance improvements in knowledge base and embedding workflows.
Qwen Text Embedding v4 API pricing
| Usage | Price | Unit |
|---|---|---|
| Input | $0.07 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
How to use the Qwen Text Embedding v4 API
View docs
Build with your agent
Give your agent the model details, code and documentation.
1Get your API key
Create API keyexport AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"2Make your first request
curl 'https://api.aimlapi.com/v1/embeddings' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "alibaba/qwen-text-embedding-v4",
"input": "The quick brown fox jumps over the lazy dog."
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "alibaba/qwen-text-embedding-v4",
"input": "The quick brown fox jumps over the lazy dog."
}
response = requests.post("https://api.aimlapi.com/v1/embeddings", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/embeddings", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "alibaba/qwen-text-embedding-v4",
"input": "The quick brown fox jumps over the lazy dog."
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Models similar to Qwen Text Embedding v4
Qwen Text Embedding v4 FAQ
What is the context window of Qwen Text Embedding v4?
Qwen Text Embedding v4 has a 8,192 tokens context window.
What inputs and outputs does Qwen Text Embedding v4 support?
Qwen Text Embedding v4 takes text as input and returns embedding.
When was Qwen Text Embedding v4 released?
Qwen Text Embedding v4 became available on December 13, 2025.
How much does Qwen Text Embedding v4 cost?
Qwen Text Embedding v4 costs $0.07 per 1M input tokens on AI/ML API, billed as you go with no subscription.
Who created Qwen Text Embedding v4?
Qwen Text Embedding v4 was built by Alibaba Cloud.
How do I call Qwen Text Embedding v4 on AI/ML API?
Send a request to https://api.aimlapi.com/v1/embeddings with alibaba/qwen-text-embedding-v4 as the model id.
Is Qwen Text Embedding v4 available through a single API key?
Yes. Qwen Text Embedding v4 is served through AI/ML API, so the same key and endpoint format used for other models applies.
What is the range of embedding dimensions I can choose?
Qwen Text Embedding v4 supports custom output dimensions from 64 to 2048.
Which languages does the model support?
It supports more than 100 languages.
What use cases is this embedding model designed for?
It is designed for semantic search, clustering, and classification tasks.