---
title: qwen3-embedding-0.6b
description: The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. 
image: https://edgetunnel-b2h.pages.dev/dev-products-preview.png
---

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#  qwen3-embedding-0.6b 

Text Embeddings • Qwen 

`@cf/qwen/qwen3-embedding-0.6b` 

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. 

| Model Info                                                                 |                           |
| -------------------------------------------------------------------------- | ------------------------- |
| Context Window[ ↗](https://edgetunnel-b2h.pages.dev/workers-ai/glossary/) | 8,192 tokens              |
| Unit Pricing                                                               | $0.012 per M input tokens |

## Usage

* [  TypeScript ](#tab-panel-5545)
* [  Python ](#tab-panel-5546)
* [  curl ](#tab-panel-5547)

```ts
export interface Env {
  AI: Ai;
}


export default {
  async fetch(request, env): Promise<Response> {


    // Can be a string or array of strings]
    const stories = [
      "This is a story about an orange cloud",
      "This is a story about a llama",
      "This is a story about a hugging emoji",
    ];


    const embeddings = await env.AI.run(
      "@cf/qwen/qwen3-embedding-0.6b",
      {
        text: stories,
      }
    );


    return Response.json(embeddings);
  },
} satisfies ExportedHandler<Env>;
```

```py
import os
import requests


ACCOUNT_ID = "your-account-id"
AUTH_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")


stories = [
  'This is a story about an orange cloud',
  'This is a story about a llama',
  'This is a story about a hugging emoji'
]


response = requests.post(
  f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/qwen/qwen3-embedding-0.6b",
  headers={"Authorization": f"Bearer {AUTH_TOKEN}"},
  json={"text": stories}
)


print(response.json())
```

```sh
curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/qwen/qwen3-embedding-0.6b  \
  -X POST  \
  -H "Authorization: Bearer $CLOUDFLARE_API_TOKEN"  \
  -d '{ "text": ["This is a story about an orange cloud", "This is a story about a llama", "This is a story about a hugging emoji"] }'
```

OpenAI compatible endpoints 

Workers AI also supports OpenAI compatible API endpoints for `/v1/chat/completions` and `/v1/embeddings`. For more details, refer to [Configurations ](https://edgetunnel-b2h.pages.dev/workers-ai/configuration/open-ai-compatibility/). 

## Parameters

* [ Input ](#tab-panel-5548)
* [ Output ](#tab-panel-5549)

▶queries

`one of`

instruction

`string`default: Given a web search query, retrieve relevant passages that answer the queryOptional instruction for the task

▶documents

`one of`

▶text

`one of`

▶data\[\]

`array`

▶shape\[\]

`array`

## API Schemas (Raw)

Input [ ](https://edgetunnel-b2h.pages.dev/workers-ai/models/qwen3-embedding-0.6b/schema-input.json "Open") [ ](https://edgetunnel-b2h.pages.dev/workers-ai/models/qwen3-embedding-0.6b/schema-input.json "Download") 

Output [ ](https://edgetunnel-b2h.pages.dev/workers-ai/models/qwen3-embedding-0.6b/schema-output.json "Open") [ ](https://edgetunnel-b2h.pages.dev/workers-ai/models/qwen3-embedding-0.6b/schema-output.json "Download")

```json
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```