# Alibaba Text Embedding v4 API

> Alibaba Cloud Model Studio text-embedding-v4 is a Qwen3-Embedding series text embedding model for semantic search, retrieval-augmented generation, clustering, classification, and multilingual code/text retrieval. It supports configurable output dimensions and OpenAI-compatible embeddings requests.

Source: https://www.sandbase.ai/model/alibaba/text-embedding-v4
API reference: https://www.sandbase.ai/model/alibaba/text-embedding-v4/docs

## Facts

- Provider: Alibaba
- Type: Embedding model
- Model ID: `alibaba/text-embedding-v4`
- Price: $0.10 per 1M input tokens, $0 per 1M output tokens
- Context window: 8.2K tokens
- Capabilities: embedding, text embedding, multilingual
- Billing: pay per use from one SandBase balance; no provider account or subscription needed

## Call it

- Embeddings API: `POST https://api.sandbase.ai/v1/embeddings`

```bash
curl -X POST https://api.sandbase.ai/v1/embeddings \
  -H "Authorization: Bearer $SANDBASE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "alibaba/text-embedding-v4",
  "input": "SandBase provides a unified API for AI models, agents, and developer tools.",
  "dimensions": 1024,
  "encoding_format": "float"
}'
```

## FAQ

### How do I call Alibaba Text Embedding v4 through SandBase?

Create a SandBase API key, then send requests with the model ID "alibaba/text-embedding-v4" to Embeddings API (/v1/embeddings) at https://api.sandbase.ai. The request examples on this page show the exact payload.

### How much does Alibaba Text Embedding v4 cost?

$0.10 per 1M input tokens and $0 per 1M output tokens, billed per request from your SandBase balance. Discounts, when available, are shown in the pricing on this page.

### What is the context window of Alibaba Text Embedding v4?

8.2K tokens of context.

### Do I need a separate Alibaba account?

No. One SandBase API key and balance gives you access to Alibaba Text Embedding v4 and the other models in the catalog; you do not need to sign up with Alibaba separately.

## README

Alibaba text-embedding-v4 converts text into dense vectors for semantic search, RAG, clustering, classification, and multilingual retrieval. It belongs to the Qwen3-Embedding series and supports configurable output dimensions: 2048, 1536, 1024, 768, 512, 256, 128, and 64.