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.
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.
Qwen Text Embedding v3 introduces several architectural and training innovations to push the boundaries of dense retrieval.
import requests r = requests.post( "https://api.aimlapi.com/v1/embeddings", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "alibaba/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/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/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 | |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Qwen Text Embedding v3 This page | Semantic search + RAG | |||
| Semantic search + RAG | ||||
| Semantic search + RAG | ||||
| Semantic search + RAG | ||||
| Semantic search + RAG |
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, output $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/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.
You can select a custom embedding dimension between 512 and 1024.
It supports more than 50 languages.
It is built for semantic search, recommendation systems, and text analysis.
