Qwen Text Embedding v4 is a 4B-parameter dual-encoder model from the Qwen3 family, optimized specifically for dense embeddings and ranking tasks rather than general chat. It supports over 100 languages (including major programming languages) and is tuned for semantic search, retrieval, classification, clustering, and bitext mining in a single shared embedding space.
Qwen Text Embedding v4 introduces a suite of innovations focused on semantic fidelity, efficiency, and multilingual equity:
These enhancements translate into stronger RAG accuracy, more coherent document clustering, and reduced false positives in semantic search, especially in multilingual support portals, research knowledge bases, and cross-border enterprise analytics.
vs OpenAI text-embedding-3-large: Qwen v4 matches or exceeds OpenAI’s performance on MTEB while offering 8K context (vs 8K for OpenAI, but with lower cost and data residency flexibility). Unlike OpenAI, Qwen provides transparent licensing for commercial deployment and avoids data usage for model training.
vs Google’s textembedding-gecko: Qwen v4 provides better zero-shot retrieval scores on BEIR and avoids vendor lock-in through open-weight availability. Gecko integrates tightly with Vertex AI, while Qwen offers greater deployment flexibility across clouds and on-prem.
Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.
import requests r = requests.post( "https://api.aimlapi.com/v1/embeddings", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "alibaba/text-embedding-v4", "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-v4", "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-v4","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 v4 This page | Semantic search + RAG | |||
| Semantic search + RAG | ||||
| Semantic search + RAG | ||||
| Semantic search + RAG |
Qwen Text Embedding v4 takes text as input and returns embedding.
Qwen Text Embedding v4 became available on December 13, 2025.
Qwen Text Embedding v4 is priced at input $0.091 / 1M tokens, output $0.074 / 1M tokens.
Qwen Text Embedding v4 was built by Alibaba Cloud.
Send a request to https://api.aimlapi.com/v1/embeddings with alibaba/text-embedding-v4 as the model id.
Yes. Qwen Text Embedding v4 is served through AI/ML API, so the same key and endpoint format used for other models applies.
Qwen Text Embedding v4 supports custom embedding dimensions ranging from 64 to 2048.
Qwen Text Embedding v4 supports over 100 languages.
Yes, it belongs to the Qwen3-Embedding series from Alibaba Cloud.
It is designed for semantic search, clustering, and classification tasks.
