Name: Alibaba Qwen3 Embedding 8B Text Embedding API
Brand: magicapi
SKU: api-magicapi-text-embedding-ranking-lucataco-qwen3-embedding-8b
Availability: InStock
Author: API Market

Shashank Agarwal

# 🔎 Text Embedding API (Alibaba Qwen3 Embedding 8B)

## Overview
Qwen3-Embedding-8B is a text embedding model from Alibaba's Qwen team. Its developer describes the Qwen3 Embedding series as designed for text embedding and ranking tasks.

Send `text` as one string or as a list of strings. Set the vector size with `embedding_dim` (32 to 4096, default 1024), add an optional task `instruction`, and choose whether to `normalize` the vectors. The API returns a JSON object with one embedding per text.

## Features
- **One or many texts**: `text` is a string or an array of strings.
- **Adjustable size**: `embedding_dim` from 32 to 4096 (default 1024).
- **Task instruction**: `instruction` gives an optional instruction for task-specific embeddings.
- **Normalized vectors**: `normalize` (default `true`) normalizes the embeddings.
- **Batching**: `batch_size` (1 to 32, default 8) sets how many texts are processed at a time.
- **Shape in the response**: the output reports `num_texts`, `embedding_dim` and `shape` next to the vectors.

## Use Cases
1. **Semantic search**: embed documents and queries and compare them by similarity.
2. **Retrieval for LLM apps**: find the passages to send to a language model.
3. **Clustering and deduplication**: group similar texts or find near duplicates.
4. **Ranking**: score candidate texts against a query by vector similarity.

## Visual Examples
### Example Input
`text` was sent as one string that contains three sentences written as a list, with `embedding_dim` 1024 and `normalize` `true`. Because the value is a single string, the model embedded it as one text and returned one vector (`num_texts` is 1). To embed several texts in one request, send `text` as a JSON array of strings.

```json
{
  "text": "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
  "normalize": true,
  "batch_size": 8,
  "instruction": "",
  "embedding_dim": 1024
}
```

### Example Output
The vector is shortened to its first 4 of 1024 values here.

```json
{
  "embedding_dim": 1024,
  "embeddings": [
    [
      -0.037322998046875,
      0.0020694732666015625,
      -0.019744873046875,
      0.00251007080078125
    ]
  ],
  "instruction": null,
  "model": "Qwen3-Embedding-8B",
  "normalized": true,
  "num_texts": 1,
  "shape": [
    1,
    1024
  ]
}
```

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `text` | string or array of strings | Yes |  |  | Text or list of texts to embed |
| `instruction` | string | No | `""` |  | Optional instruction for task-specific embeddings |
| `embedding_dim` | integer | No | `1024` | 32 to 4096 | Output embedding dimension |
| `normalize` | boolean | No | `true` |  | Whether to normalize embeddings |
| `batch_size` | integer | No | `8` | 1 to 32 | Batch size for processing multiple texts |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.0012 per API unit.
- **1 API unit = 1 request with up to 1 second of processing.** A request that takes longer is billed one more unit for each started 1-second block (for example, 3 seconds of processing is 3 units, $0.0036).
- **Example cost**: the example request took 0.5 seconds of processing, which is 1 unit ($0.0012).
- More or longer texts take longer to process and can use more units.
- **Failed or cancelled predictions are not billed.**
- **GET requests to check prediction status** are free and don't count toward API unit usage.
- **No monthly subscription fee.**

## API Documentation

### Base URL
All API requests go to: `https://prod.api.market/api/v1/magicapi/text-embedding-ranking-lucataco-qwen3-embedding-8b`

### Authentication
Send your api.market API key in the `x-api-market-key` header:
```
x-api-market-key: YOUR_API_MARKET_KEY
```

### Asynchronous Processing
**IMPORTANT**: This API is asynchronous. The flow is:
1. Send a POST request to `/predictions`. A successful request returns **201 Created** with a prediction `id` and a `status` of `starting` or `processing`.
2. Poll GET `/predictions/{id}` with that ID.
3. Keep polling while `status` is `starting` or `processing`.
4. Stop when `status` is `succeeded` (read `output`), `failed` or `canceled` (read `error`).

A polling interval of 1 second and an overall timeout of 600 seconds (10 minutes) work well.

### Endpoints

#### Create a Prediction
```bash
curl -X POST "https://prod.api.market/api/v1/magicapi/text-embedding-ranking-lucataco-qwen3-embedding-8b/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
    "version": "42d968487820032a1535d81ea20df16f442ea308ec5abae6b5d6cf4675eb3e2f",
    "input": {
      "text": "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
      "normalize": true,
      "batch_size": 8,
      "instruction": "",
      "embedding_dim": 1024
    }
  }'
```

Response (201 Created):
```json
{
  "id": "ys4bw0cxxsrmr0d1mvmrjegn5r",
  "version": "42d968487820032a1535d81ea20df16f442ea308ec5abae6b5d6cf4675eb3e2f",
  "status": "starting",
  "created_at": "2026-11-04T14:14:50.606Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/text-embedding-ranking-lucataco-qwen3-embedding-8b/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "ys4bw0cxxsrmr0d1mvmrjegn5r",
  "status": "succeeded",
  "output": {
    "embedding_dim": 1024,
    "embeddings": [
      [
        -0.037322998046875,
        0.0020694732666015625,
        -0.019744873046875,
        0.00251007080078125
      ]
    ],
    "instruction": null,
    "model": "Qwen3-Embedding-8B",
    "normalized": true,
    "num_texts": 1,
    "shape": [
      1,
      1024
    ]
  },
  "error": null,
  "created_at": "2026-11-04T14:14:50.606Z",
  "started_at": "2026-11-04T14:15:22.243Z",
  "completed_at": "2026-11-04T14:15:22.755Z"
}
```

`output` is a JSON object: `embeddings` is a list with one vector per text, `embedding_dim` is the vector size, `num_texts` is the number of texts embedded, `shape` is `[num_texts, embedding_dim]`, and `instruction`, `model` and `normalized` echo the settings. The vector above is shortened to 4 of its 1024 values.

### Status Codes
- **201 Created**: prediction created (POST `/predictions`). This is the normal success response.
- **200 OK**: prediction details returned (GET `/predictions/{id}`).
- **400 Bad Request**: invalid input, for example a missing `text` or an `embedding_dim` outside 32 to 4096.
- **401 Unauthorized**: missing or invalid API key.
- **404 Not Found**: no prediction with that ID.
- **429 Too Many Requests**: rate limit exceeded.
- **500 Internal Server Error** or **503 Service Unavailable**: temporary server problem; retry later.

## Rate Limits
Requests are rate limited to keep usage fair. If you receive a 429 response, wait and retry with exponential backoff.

## Error Handling
- Check the HTTP status code of every request and read the response body for the error message.
- A prediction can also end with `status` set to `failed`; the `error` field then explains why. Failed predictions are not billed.
- Retry 429, 500 and 503 responses with exponential backoff. Fix the input before retrying a 400.

## Implementation Guide
### Example Code

#### JavaScript
```javascript
const apiKey = "YOUR_API_MARKET_KEY";
const baseUrl = "https://prod.api.market/api/v1/magicapi/text-embedding-ranking-lucataco-qwen3-embedding-8b";

async function createAndWaitForPrediction(input) {
  const response = await fetch(`${baseUrl}/predictions`, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "x-api-market-key": apiKey
    },
    body: JSON.stringify({ version: "42d968487820032a1535d81ea20df16f442ea308ec5abae6b5d6cf4675eb3e2f", input })
  });

  if (response.status !== 201) {
    throw new Error(`API error: ${response.status} ${await response.text()}`);
  }

  const prediction = await response.json();
  return pollForResults(prediction.id);
}

const delay = (ms) => new Promise((resolve) => setTimeout(resolve, ms));

async function pollForResults(predictionId) {
  const maxTimeout = 600000;
  const pollInterval = 1000;
  const startTime = Date.now();

  while (Date.now() - startTime < maxTimeout) {
    const response = await fetch(`${baseUrl}/predictions/${predictionId}`, {
      headers: { "x-api-market-key": apiKey }
    });

    if (!response.ok) {
      throw new Error(`API error: ${response.status} ${await response.text()}`);
    }

    const prediction = await response.json();

    if (prediction.status === "succeeded") {
      return prediction.output;
    }

    if (prediction.status === "failed" || prediction.status === "canceled") {
      throw new Error(`Prediction ${prediction.status}: ${prediction.error || "Unknown error"}`);
    }

    await delay(pollInterval);
  }

  throw new Error("Prediction timed out after 10 minutes");
}

createAndWaitForPrediction({
  "text": "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
  "normalize": true,
  "batch_size": 8,
  "instruction": "",
  "embedding_dim": 1024
}).then((output) => console.log(output));
```

#### Python
```python
import time
import requests

API_KEY = "YOUR_API_MARKET_KEY"
BASE_URL = "https://prod.api.market/api/v1/magicapi/text-embedding-ranking-lucataco-qwen3-embedding-8b"


def create_and_wait_for_prediction(input_data):
    headers = {"Content-Type": "application/json", "x-api-market-key": API_KEY}
    response = requests.post(
        f"{BASE_URL}/predictions",
        headers=headers,
        json={"version": "42d968487820032a1535d81ea20df16f442ea308ec5abae6b5d6cf4675eb3e2f", "input": input_data},
    )

    if response.status_code != 201:
        raise Exception(f"API error: {response.status_code} {response.text}")

    return poll_for_results(response.json()["id"])


def poll_for_results(prediction_id):
    max_timeout = 600
    poll_interval = 1
    headers = {"x-api-market-key": API_KEY}
    start_time = time.time()

    while time.time() - start_time < max_timeout:
        response = requests.get(f"{BASE_URL}/predictions/{prediction_id}", headers=headers)

        if response.status_code != 200:
            raise Exception(f"API error: {response.status_code} {response.text}")

        prediction = response.json()

        if prediction["status"] == "succeeded":
            return prediction["output"]

        if prediction["status"] in ("failed", "canceled"):
            raise Exception(f"Prediction {prediction['status']}: {prediction.get('error') or 'Unknown error'}")

        time.sleep(poll_interval)

    raise Exception("Prediction timed out after 10 minutes")


print(create_and_wait_for_prediction({
    "text": "[\"In the water, fish are swimming.\", \"Fish swim in the water.\", \"A book lies open on the table.\"]",
    "normalize": True,
    "batch_size": 8,
    "instruction": "",
    "embedding_dim": 1024
}))
```

## FAQ
- **How do I embed several texts at once?**
  - Send `text` as a JSON array of strings, for example `["first text", "second text"]`. A single string, even one that looks like a list, is embedded as one text.
- **What `embedding_dim` should I use?**
  - Any value from 32 to 4096 (default 1024). Use the same value for every text you plan to compare.
- **What is `instruction` for?**
  - An optional instruction for task-specific embeddings, such as describing the search task. Leave it empty for general embeddings.
- **How much does a request cost?**
  - $0.0012 for each started second of processing. The example took 0.5 seconds and cost 1 unit ($0.0012).
- **What if my prediction fails?**
  - Failed predictions are not billed. Read the `error` field, adjust the input and try again.

## Contact Us
If you have any questions, reach out to us through the chat on the bottom right of the page.


Create text embeddings with Alibaba's Qwen3-Embedding-8B: one text or a list per request, 32 to 4096 dimensions, optional task instruction. $0.0012 per request, pay as you go.