Qwen Text Embedding v3 is Alibaba Cloud’s embedding model for turning text into vectors for retrieval and related semantic tasks.
The available documentation and model pages indicate it is a multilingual embedding model with configurable output dimensions and support for text-search workflows.
| Developer | Alibaba Cloud |
|---|---|
| Model ID | alibaba/qwen-text-embedding-v3 |
| Release Date | December 12, 2025 |
| Context Window | 8,192 tokens (~8K) |
| Modalities | Text → Embedding |
Multilingual coverage
Alibaba Cloud documents Qwen Text Embedding v3 as supporting 50+ major languages, including Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, and Russian.
Configurable embedding dimensions
The model supports output dimensions of 1,024 by default, with additional dimension options listed in Alibaba Cloud documentation such as 768, 512, 256, 128, and 64.
Long input support
The documentation lists a maximum batch token limit of 8,192 for the embedding service.
Embedding service on Model Studio
Alibaba Cloud exposes the model through its Model Studio embedding offering, which is presented as an embedding API for vector generation.
Semantic retrieval
The model is suitable for transforming queries and documents into embeddings for search and retrieval workflows.
Text classification
Alibaba Cloud and related Qwen materials describe embedding models like this one as useful for classification tasks.
RAG pipelines
The model is positioned for retrieval-augmented generation workflows where document vectors are used to fetch relevant context.
Cross-lingual search
Its multilingual support makes it appropriate for queries and documents in different languages.
Qwen3 embedding models
Qwen’s later embedding series is described as a family of text embedding and reranking models with 0.6B, 4B, and 8B variants.
OpenAI and Gemini embedding APIs
A third-party comparison article describes Qwen as a strong alternative to existing embedding solutions such as Gemini Embedding and OpenAI’s embedding APIs.
Sources: https://www.alibabacloud.com/blog/mastering-text-embedding-and-reranker-with-qwen3_602308 · https://www.alibabacloud.com/blog/602349 · https://docs.aimlapi.com/api-references/embedding-models/alibaba-cloud/qwen-text-embedding-v3
Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.
Semantic search is a documented use case for Qwen Text Embedding v3, since the model converts text into vectors for similarity-based retrieval.
Recommendation systems are another documented use case, including web search and recommendation applications, where embeddings support downstream matching and ranking.
Clustering is also explicitly listed, with embeddings used to group similar texts or documents together.
Classification is documented as a supported downstream task, including text categorization and related analysis workflows.
import requests r = requests.post( "https://api.aimlapi.com/v1/embeddings", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "alibaba/qwen-text-embedding-v3", "input": "The quick brown fox" }, ) print(r.json())
const r = 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-v3", "input": "The quick brown fox" }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/embeddings \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"alibaba/qwen-text-embedding-v3","input":"The quick brown fox"}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
The documented pricing caveat is that Qwen Text Embedding v3 includes a free quota of 500,000 tokens, valid for 90 days after Model Studio activation, model release, or application approval, whichever is later.
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Qwen Text Embedding v3 This page | Semantic search and RAG | |||
| Semantic search + RAG | ||||
| Semantic search + RAG |
Qwen Text Embedding v3 has a 8,192 tokens context window.
Qwen Text Embedding v3 takes text as input and returns embedding.
Qwen Text Embedding v3 became available on December 13, 2025.
Qwen Text Embedding v3 is priced at input $0.091 / 1M tokens.
Qwen Text Embedding v3 was built by Alibaba Cloud.
Send a request to https://api.aimlapi.com/v1/embeddings with alibaba/qwen-text-embedding-v3 as the model id.
Yes. Qwen Text Embedding v3 is served through AI/ML API, so the same key and endpoint format used for other models applies.
It supports over 50 languages.
It supports custom output dimensions ranging from 512 to 1024.
Its model ID is alibaba/qwen-text-embedding-v3.
It is suited for semantic search, recommendation systems, and text analysis.
