> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aieev.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Qwen3-Embedding-8B

> Multilingual embedding model with 4,096-dimensional vectors and 32K input.

<Info>
  **Model ID:** `qwen/qwen3-embedding-8b` | **Endpoint ID:** `qwen3-embedding-8b` | **Parameters:** 8B | **Released:** 2025-06-05
</Info>

|  Input Price | Output Price |   Max Input   | Dimensions |
| :----------: | :----------: | :-----------: | :--------: |
| \$0.010 / 1M |    \$0.000   | 32,768 tokens |    4,096   |

## Overview

Qwen3-Embedding-8B is a multilingual text embedding model that converts input text into dense vector representations. It accepts up to 32,768 tokens of input and outputs 4,096-dimensional embedding vectors. The model is well suited for semantic search, retrieval-augmented generation (RAG), recommendation systems, and classification or clustering tasks.

<CardGroup cols={3}>
  <Card title="Air API Playground" icon="play" href="https://ap-1.aieev.cloud:3007/models/qwen%2Fqwen3-embedding-8b">
    Try the model in the playground.
  </Card>

  <Card title="Deploy with Container" icon="container-storage" href="/docs/air-container/deploy-a-container">
    Deploy with AIR Container.
  </Card>

  <Card title="OpenAI Compatible API" icon="arrow-left-right" href="/docs/air-api/openai-compatible-api#embedding">
    Connect with the OpenAI SDK as-is.
  </Card>
</CardGroup>

## Pricing

| Currency | Input (1M tokens) | Output (1M tokens) |
| :------- | ----------------: | -----------------: |
| USD      |           \$0.010 |            \$0.000 |
| KRW      |               ₩15 |                 ₩0 |

## Key Features

* Multilingual embeddings across a wide range of languages
* Long input support up to 32,768 tokens
* High-quality 4,096-dimensional vector output
* Batch embedding for high-throughput processing

## Use Cases

### Semantic Search

Embed queries and documents into the same vector space to find results by meaning rather than exact keyword match.

### RAG (Retrieval-Augmented Generation)

Generate embeddings for a knowledge base and retrieve the most relevant passages to ground LLM responses in your own data.

### Recommendation Systems

Represent items and user preferences as vectors to power similarity-based recommendations.

### Classification & Clustering

Use embedding vectors as features for downstream classification or to group similar texts through clustering.

## Parameters

| Parameter | Type            | Required | Default | Description                                                                       |
| --------- | --------------- | -------- | ------- | --------------------------------------------------------------------------------- |
| `model`   | string          | Required | -       | Model ID to use for embedding generation                                          |
| `input`   | string \| array | Required | -       | Text to embed. Accepts a single string or an array of strings for batch embedding |

## Model Details

| Property         | Value                                   |
| ---------------- | --------------------------------------- |
| Dimensions       | 4,096                                   |
| Max Input Length | 32,768 tokens                           |
| Input Modalities | text                                    |
| Output           | embedding                               |
| Batch Input      | Supported (array of strings in `input`) |

## Quick Start

<Steps>
  <Step title="Get your API key">
    Generate an API key from your [AirCloud account](https://aieev.com).
  </Step>

  <Step title="Run the code">
    Replace `YOUR_API_KEY` with your actual key and choose your preferred language (Python, cURL, Node.js).
  </Step>
</Steps>

<CodeGroup>
  ```python Python theme={null}
  from openai import OpenAI

  client = OpenAI(
      api_key="YOUR_API_KEY",
      base_url="https://ap-1.aieev.cloud/endpoints/qwen3-embedding-8b/v1"
  )

  response = client.embeddings.create(
      model="qwen/qwen3-embedding-8b",
      input=["Hello, world!", "AirCloud makes deploying models easy."]
  )

  for item in response.data:
      print(item.index, len(item.embedding))  # 4096 dims per vector
  ```

  ```bash cURL theme={null}
  curl https://ap-1.aieev.cloud/endpoints/qwen3-embedding-8b/v1/embeddings \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "qwen/qwen3-embedding-8b",
      "input": ["Hello, world!", "AirCloud makes deploying models easy."]
    }'
  ```

  ```javascript Node.js theme={null}
  import OpenAI from "openai";

  const client = new OpenAI({
    apiKey: "YOUR_API_KEY",
    baseURL: "https://ap-1.aieev.cloud/endpoints/qwen3-embedding-8b/v1",
  });

  const response = await client.embeddings.create({
    model: "qwen/qwen3-embedding-8b",
    input: ["Hello, world!", "AirCloud makes deploying models easy."],
  });

  response.data.forEach((item) => {
    console.log(item.index, item.embedding.length); // 4096 dims per vector
  });
  ```
</CodeGroup>

## Tags

`embedding` `8B` `multilingual` `semantic-search` `open-source`