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

# Create Prediction

> Kick off a new prediction for any AI model.

## Endpoint

```
POST https://api.eachlabs.ai/v1/prediction
```

## Request Body

| Field            | Type   | Required | Description                                  |
| ---------------- | ------ | -------- | -------------------------------------------- |
| `model`          | string | Yes      | Model slug (e.g., `flux-1-1-pro`)            |
| `version`        | string | Yes      | Model version (e.g., `1.0.0`)                |
| `input`          | object | Yes      | Input parameters (varies by model)           |
| `webhook_url`    | string | No       | URL to receive prediction result via webhook |
| `webhook_secret` | string | No       | Secret used to sign webhook requests         |

The `input` object accepts model-specific parameters. Some models also support:

| Input Field             | Type    | Default | Description                                                                                                     |
| ----------------------- | ------- | ------- | --------------------------------------------------------------------------------------------------------------- |
| `enable_safety_checker` | boolean | `true`  | Set to `false` to disable NSFW filtering. Only works on [supported models](/api/nsfw-content#supported-models). |

## Code Examples

<CodeGroup>
  ```bash cURL theme={"dark"}
  curl -X POST https://api.eachlabs.ai/v1/prediction \
    -H "Content-Type: application/json" \
    -H "X-API-Key: YOUR_API_KEY" \
    -d '{
      "model": "flux-1-1-pro",
      "version": "1.0.0",
      "input": {
        "prompt": "A beautiful sunset over the ocean with vibrant colors",
        "aspect_ratio": "16:9"
      },
      "webhook_url": "https://your-app.com/webhook",
      "webhook_secret": "your-secret-key"
    }'
  ```

  ```python Python theme={"dark"}
  import requests

  response = requests.post(
      "https://api.eachlabs.ai/v1/prediction",
      headers={
          "Content-Type": "application/json",
          "X-API-Key": "YOUR_API_KEY"
      },
      json={
          "model": "flux-1-1-pro",
          "version": "1.0.0",
          "input": {
              "prompt": "A beautiful sunset over the ocean with vibrant colors",
              "aspect_ratio": "16:9"
          }
      }
  )

  data = response.json()
  print(f"Prediction ID: {data['predictionID']}")
  ```

  ```javascript JavaScript theme={"dark"}
  const response = await fetch("https://api.eachlabs.ai/v1/prediction", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "X-API-Key": "YOUR_API_KEY",
    },
    body: JSON.stringify({
      model: "flux-1-1-pro",
      version: "1.0.0",
      input: {
        prompt: "A beautiful sunset over the ocean with vibrant colors",
        aspect_ratio: "16:9",
      },
    }),
  });

  const { predictionID } = await response.json();
  console.log(`Prediction ID: ${predictionID}`);
  ```
</CodeGroup>

## Response

```json theme={"dark"}
{
  "status": "success",
  "message": "Prediction created successfully",
  "predictionID": "abc123-def456-ghi789"
}
```

### Response Fields

| Field          | Type   | Description                           |
| -------------- | ------ | ------------------------------------- |
| `status`       | string | `"success"` if prediction was created |
| `message`      | string | Human-readable status message         |
| `predictionID` | string | Unique ID to track the prediction     |

## Error Responses

| Status | Body                                       | Description                      |
| ------ | ------------------------------------------ | -------------------------------- |
| `400`  | `{"error": "Invalid input for model"}`     | Input doesn't match model schema |
| `400`  | `{"error": "model field is required"}`     | Missing required field           |
| `401`  | `{"error": "Invalid or missing API key"}`  | Authentication failure           |
| `500`  | `{"error": "Failed to create prediction"}` | Server error                     |

## What Happens Next

After creating a prediction, the model processes your input asynchronously. Here's how you can get your results:

1. **Poll**: Use [Get Prediction](/api/predictions/get-prediction) to check status
2. **Webhook**: Provide a `webhook_url` and get results delivered automatically when they're ready

<Note>
  Every model has different input parameters. Use [Get Model](/api/models/get-model) to grab the `request_schema` for any model and see exactly what it expects.
</Note>