# ZeroReader Qwen3 Embedding 0.6B

> ZeroReader Qwen3 Embedding 0.6B is a paid API for AI agents from api.zeroreader.com, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-02).

Generates compact vector embeddings from text using Alibaba's Qwen3 0.6B embedding model via the ZeroReader AI API

## Facts

- Endpoint: POST https://api.zeroreader.com/v1/ai/embed-qwen3?utm_source=zero.xyz
- Price: $0.001/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/zeroreader-qwen3-embedding-0-6b-cc038edc
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_AFBBAAfTBlw5NTXtGnCdT

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability zeroreader-qwen3-embedding-0-6b-cc038edc -d '<json body>'
```

Example prompt: Can you embed this text using the Qwen3 model: 'Artificial intelligence is transforming the software industry'? I need the vector to index it in my semantic search system.

## When to prefer this

Choose this endpoint when you need compact, fast embeddings suitable for semantic similarity, clustering, or retrieval-augmented generation tasks, especially in multilingual or mixed-language contexts where Qwen's training data is an advantage. Prefer it over BGE Large EN v1.5 (also on this provider) when you want a smaller, faster model at lower cost, and don't require maximum English-only embedding quality. Good for high-throughput pipelines where embedding speed matters more than peak accuracy.

## Known failure modes

- Invalid input type (neither string nor array of strings) returns a 400 error
- Empty text input may return a zero vector or error
- Payment failure via x402 protocol results in 402 response blocking the call
- Overly long input texts may be truncated or rejected depending on model token limits
- Network timeout if the model inference takes too long under high load

## How this service works

Qwen3 Embedding 0.6B — Compact embedding model from Qwen.

## Output

A list of embedding objects, each containing an index, an object type of 'embedding', and a floating-point vector array representing the semantic content of the input text. The response wraps one or more embeddings in a 'data' array with object type 'list'.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "text": {
   "oneOf": [
    {
     "type": "string"
    },
    {
     "type": "array",
     "items": {
      "type": "string"
     }
    }
   ]
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "data": [
  {
   "index": 0,
   "object": "embedding",
   "embedding": [
    0.1,
    0.2,
    0.3
   ]
  }
 ],
 "object": "list"
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/zeroreader-qwen3-embedding-0-6b-cc038edc/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from api.zeroreader.com](https://www.zero.xyz/host/api.zeroreader.com/llms.txt)