Qwen Text Embedding v3 overview
Qwen Text Embedding v3 by Alibaba Cloud
Built on Qwen3 foundations, it prioritizes long-context understanding and semantic accuracy for real-world applications.
Qwen Text Embedding v3 specs
- Model ID
alibaba/text-embedding-v3- Developer
- Alibaba Cloud
- Input
- Text
- Output
- Text
- Endpoint
POST https://api.aimlapi.com/v1/embeddings
Overview
Qwen Text Embedding V3 represents a cutting-edge embedding model optimized for dense vector representations, excelling in semantic search, retrieval-augmented generation (RAG), and multilingual similarity tasks across 100+ languages. It delivers high-dimensional embeddings up to 4096 in length, with dynamic dimensionality reduction for efficiency, enabling precise capture of nuanced meanings in long texts and cross-lingual contexts.
Technical Specifications
- Architecture: Transformer-based dual-encoder with asymmetric fine-tuning
- Vector Dimensionality: 1024 (configurable down to 256 via projection layers)
- Supported Tasks: Semantic similarity, document clustering, cross-lingual retrieval, reranking support
- Training Data: Multilingual corpus spanning 30+ languages, enriched with technical, academic, and conversational domains
Performance Benchmarks
- Semantic retrieval (MTEB & BEIR): Top-tier scores in passage ranking and asymmetric search
- Multilingual tasks: Outperforms prior versions by 5-10% on C-MTEB leaderboard
Output Quality & Semantic Fidelity
Users report marked improvements in vector consistency across paraphrases, domain shifts, and query-document asymmetry. Embeddings exhibit reduced topic drift in iterative retrieval systems and stronger alignment with human-judged relevance rankings. The model excels in distinguishing subtle sentiment and intent variations, critical for customer support routing and compliance filtering.
Quality Improvements
- Noise resilience: Robust to OCR errors, informal syntax, and mixed-language inputs
- Temporal stability: Embedding drift minimized over time, ensuring index compatibility in production systems
- Bias mitigation: Enhanced fairness controls reduce stereotypical associations in gender, profession, and geographic representations
New Features & Technical Upgrades
Qwen Text Embedding v3 introduces several architectural and training innovations to push the boundaries of dense retrieval.
Use cases
- Semantic Search: Powers RAG systems by embedding queries and docs for precise matching in multilingual corpora.
- Recommendation Engines: Clusters user preferences across languages for personalized content delivery.
- Code Intelligence: Retrieves relevant snippets in polyglot repositories, aiding developers in IDE plugins.
- Sentiment Analysis: Classifies multilingual reviews with high fidelity for global market insights.
Qwen Text Embedding v3 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 v3 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/text-embedding-v3",
"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/text-embedding-v3",
"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/text-embedding-v3",
"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 v3
Qwen Text Embedding v3 FAQ
What inputs and outputs does Qwen Text Embedding v3 support?
Qwen Text Embedding v3 takes text as input and returns embedding.
When was Qwen Text Embedding v3 released?
Qwen Text Embedding v3 became available on December 13, 2025.
How much does Qwen Text Embedding v3 cost?
Qwen Text Embedding v3 costs $0.07 per 1M input tokens on AI/ML API, billed as you go with no subscription.
Who created Qwen Text Embedding v3?
Qwen Text Embedding v3 was built by Alibaba Cloud.
How do I call Qwen Text Embedding v3 on AI/ML API?
Send a request to https://api.aimlapi.com/v1/embeddings with alibaba/text-embedding-v3 as the model id.
Is Qwen Text Embedding v3 available through a single API key?
Yes. Qwen Text Embedding v3 is served through AI/ML API, so the same key and endpoint format used for other models applies.
What embedding dimensions can I choose with Qwen Text Embedding v3?
You can select a custom embedding dimension between 512 and 1024.
How many languages does Qwen Text Embedding v3 support?
It supports more than 50 languages.
What tasks is Qwen Text Embedding v3 designed for?
It is built for semantic search, recommendation systems, and text analysis.
What is the Qwen Text Embedding v3 model ID?
Use alibaba/text-embedding-v3 as the model parameter. Requests go to https://api.aimlapi.com/v1/embeddings with your AI/ML API key.