# Kling 4.0 Reference to Video

> Kling 4.0 generates videos from text prompts and image or video references at 720p or 1080p, with durations from 3 to 30 seconds and per-shot prompts. Kling 4.0 is available in the playground and fal Agent. API access is coming soon.


## Overview

- **Endpoint**: `https://fal.run/kling/v4/reference-to-video`
- **Model ID**: `kling/v4/reference-to-video`
- **Category**: video-to-video
- **Kind**: inference
**Tags**: reference-to-video, multi-shot, image-reference, video-reference, kling



## Pricing

Video costs **$0.157** per second at **720p** and **$0.235** per second at **1080p**. With video references or video elements, it costs **$0.094** per second at **720p** and **$0.141** per second at **1080p**, billed on the input plus output length (minimum 2× the output).

For more details, see [fal.ai pricing](https://fal.ai/pricing).

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:


- **`prompt`** (`string`, _optional_):
  Text prompt. Reference inputs as @Image1, @Video1, @Element1, etc. Either prompt or multi_prompt must be provided.
  - Examples: "@Element1 and @Element2 are having dinner at the table in @Image1"

- **`multi_prompt`** (`list<KlingV4Shot>`, _optional_):
  Per-shot prompts for multi-shot generation. The video duration is the sum of the shot durations.
  - Array of KlingV4Shot
  - Examples: null

- **`duration`** (`integer`, _optional_):
  Video duration in seconds (3-30). Default value: `5`
  - Default: `5`
  - Range: `3` to `30`

- **`start_image_url`** (`string`, _optional_):
  Optional image to use as the first frame. Counts toward the image reference limit.

- **`image_urls`** (`list<string>`, _optional_):
  Reference images, referenced as @Image1, @Image2, ... (max 10).
  - Array of string
  - Examples: null

- **`video_urls`** (`list<string>`, _optional_):
  Reference videos (3-30s each, 30s combined with element videos), referenced as @Video1, @Video2, ... (max 5).
  - Array of string
  - Examples: null

- **`elements`** (`list<KlingV4Element>`, _optional_):
  Characters/objects, referenced as @Element1, @Element2, ... (max 7). Up to 3 elements may carry a voice_id and up to 3 may be video elements.
  - Array of KlingV4Element
  - Examples: null

- **`resolution`** (`ResolutionEnum`, _optional_):
  Output resolution. Default value: `"1080p"`
  - Default: `"1080p"`
  - Options: `"720p"`, `"1080p"`

- **`aspect_ratio`** (`AspectRatioEnum`, _optional_):
  Aspect ratio of the generated video. 'auto' adapts it to the references. Default value: `"16:9"`
  - Default: `"16:9"`
  - Options: `"auto"`, `"16:9"`, `"9:16"`, `"1:1"`, `"21:9"`



**Required Parameters Example**:

```json
{}
```

**Full Example**:

```json
{
  "prompt": "@Element1 and @Element2 are having dinner at the table in @Image1",
  "multi_prompt": null,
  "duration": 5,
  "image_urls": null,
  "video_urls": null,
  "elements": null,
  "resolution": "1080p",
  "aspect_ratio": "16:9"
}
```


### Output Schema

The API returns the following output format:

- **`video`** (`File`, _required_):
  The generated video.
  - Examples: {"content_type":"video/mp4","file_name":"output.mp4","file_size":3020124,"url":"https://v3b.fal.media/files/b/0aac4497/-fHk6MiMZhLy2FncCbyM7_clip_reference.mp4"}



**Example Response**:

```json
{
  "video": {
    "content_type": "video/mp4",
    "file_name": "output.mp4",
    "file_size": 3020124,
    "url": "https://v3b.fal.media/files/b/0aac4497/-fHk6MiMZhLy2FncCbyM7_clip_reference.mp4"
  }
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/kling/v4/reference-to-video \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{}'
```

### Python

Ensure you have the Python client installed:

```bash
pip install fal-client
```

Then use the API client to make requests:

```python
import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "kling/v4/reference-to-video",
    arguments={},
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)
```

### JavaScript

Ensure you have the JavaScript client installed:

```bash
npm install --save @fal-ai/client
```

Then use the API client to make requests:

```javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("kling/v4/reference-to-video", {
  input: {},
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);
```


## Additional Resources

### Documentation

- [Model Playground](https://fal.ai/models/kling/v4/reference-to-video)
- [API Documentation](https://fal.ai/models/kling/v4/reference-to-video/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=kling/v4/reference-to-video)

### fal.ai Platform

- [Platform Documentation](https://fal.ai/docs/documentation)
- [Python Client](https://fal.ai/docs/api-reference/client-libraries/python)
- [JavaScript Client](https://fal.ai/docs/api-reference/client-libraries/javascript)

### Other agent-readable surfaces

This file covers one model. To find anything else:

- [Platform overview](https://fal.ai/llms.txt): Entry points and representative endpoint IDs
- [Documentation index](https://fal.ai/docs/llms.txt): Every documentation page
- [Full documentation text](https://fal.ai/docs/llms-full.txt): The whole documentation inlined
- Any other model: `https://fal.ai/models/<endpoint-id>/llms.txt`