# Kling Kling v2.5 Turbo Pro — Image To Video

> Access the Kling 2.5 Turbo API for realistic AI video. Generate 1080p clips with physics-aware motion and cinematic camera controls via GPTProto.com today.

## Overview

- **Endpoint**: `POST https://gptproto.com/api/v3/kling/kling-v2.5-turbo-pro/image-to-video`
- **Result URL**: `GET https://gptproto.com/api/v3/predictions/{result_id}/result` — the submit response also returns the authoritative URL in `data.urls.get`
- **Model ID**: `kling-v2.5-turbo-pro`
- **Vendor**: Kling
- **Scene**: `image-to-video`
- **Category**: image-to-video
- **Modalities**: input image → output video
- **Playground**: https://gptproto.com/model/kling/kling-v2.5-turbo-pro
- **API documentation**: https://docs.gptproto.com/docs/allapi/Kling/kling-v2.5-turbo-pro/gptproto-format/image-to-video
- **Other scenes of this model**: `text-to-video`, `start-end-frame` — same auth, different endpoint path and input schema

## Authentication

Every request needs a GPTProto API key in the `Authorization` header. Create one at https://gptproto.com/dashboard/api-key, then export it:

```bash
export GPTPROTO_API_KEY="your-api-key"
```

Header: `Authorization: Bearer $GPTPROTO_API_KEY` (plus `Content-Type: application/json` on POST).

## Pricing

Platform price by tier (USD, already includes the GPTProto discount):

- **5** — $0.28 per run
- **10** — $0.56 per run

Price range: $0.28 – $0.56 per generation.

Prices may change. The model page always shows the live price: https://gptproto.com/model/kling/kling-v2.5-turbo-pro

## API Information

The API is asynchronous: POST the endpoint to create a prediction, then poll its result URL until `status` is `completed` or `failed`.

### Input Schema

The endpoint accepts the following JSON body parameters:

- **`prompt`** (`string`, _required_):
  The positive prompt for the generation.

- **`image`** (`string`, _required_):
  First frame of the video; Supported image formats include.jpg/.jpeg/.png; The image file size cannot exceed 10MB, and the image resolution should not be less than 300*300px, and the aspect ratio of the image should be between 1:2.5 ~ 2.5:1.

- **`negative_prompt`** (`string`, _optional_):
  The negative prompt for the generation.

- **`guidance_scale`** (`range`, _optional_):
  The guidance scale to use for the generation.
  - Default: `0.5`
  - Range: 0–1

- **`duration`** (`enum`, _optional_):
  The duration of the generated media in seconds.
  - Default: `5`
  - Options: 5, 10

**Required Parameters Example**:

```json
{
  "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
  "image": "https://tos.gptproto.com/resource/cat.png"
}
```

**Full Example**:

```json
{
  "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
  "image": "https://tos.gptproto.com/resource/cat.png",
  "negative_prompt": "",
  "guidance_scale": 0.5,
  "duration": 5
}
```

### Output Schema

Both submit and poll return the same envelope:

- **`data.id`** (`string`): Prediction id. Use it as `result_id` when polling for the result.
- **`data.status`** (`string`): One of `created`, `running`, `completed`, `failed`. See Status Values below.
- **`data.outputs`** (`array of string`): Generated files. Empty until `status` is `completed`; then it holds the output URLs (or base64 strings when the model exposes a base64 option).
- **`data.urls.get`** (`string`): Authoritative result URL for this prediction. Prefer it over building the poll URL yourself.
- **`data.error`** (`string | null`): Failure reason when `status` is `failed`, otherwise `null`.
- **`data.executionTime`** (`integer`): Total processing time in milliseconds.
- **`data.timings.inference`** (`integer`): Model inference time in milliseconds.
- **`data.hasNsfwContents`** (`array of boolean`): Per-output moderation flags.
- **`message`** (`string`): `success` on a normal response, otherwise the error message.
- **`code`** (`integer`): Business status code. `200` means the request was accepted.

**Example Response — submit (task accepted)**:

```json
{
  "data": {
    "id": "pred_example_01",
    "model": "kling-v2.5-turbo-pro",
    "outputs": [],
    "urls": {
      "get": "https://gptproto.com/api/v3/predictions/pred_example_01/result"
    },
    "hasNsfwContents": [],
    "status": "created",
    "createdAt": "2026-01-01T12:00:00Z",
    "executionTime": 0,
    "timings": {
      "inference": 0
    }
  },
  "message": "success",
  "code": 200
}
```

**Example Response — poll (completed)**:

```json
{
  "data": {
    "id": "pred_example_01",
    "model": "kling-v2.5-turbo-pro",
    "outputs": [
      "https://oss-us.gptproto.com/example/output.mp4"
    ],
    "urls": {
      "get": "https://gptproto.com/api/v3/predictions/pred_example_01/result"
    },
    "status": "completed",
    "error": null,
    "executionTime": 12345,
    "timings": {
      "inference": 12000
    },
    "has_nsfw_contents": [],
    "created_at": "2026-01-01T12:00:00Z"
  },
  "message": "success",
  "code": 200
}
```

### Status Values

- `created` — Task accepted. Use `data.id` as `result_id` for polling.
- `running` — Generation is in progress. Keep polling.
- `completed` — Finished successfully. Generated files are in `data.outputs`.
- `failed` — Generation failed. Read `data.error` for the reason.

## Usage Examples

### 1. Submit a request

```bash
curl --request POST "https://gptproto.com/api/v3/kling/kling-v2.5-turbo-pro/image-to-video" \
  --header "Authorization: Bearer $GPTPROTO_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
    "image": "https://tos.gptproto.com/resource/cat.png",
    "negative_prompt": "",
    "guidance_scale": 0.5,
    "duration": 5
  }'
```

```python
import os
import requests

url = "https://gptproto.com/api/v3/kling/kling-v2.5-turbo-pro/image-to-video"
headers = {
  "Authorization": f"Bearer {os.environ['GPTPROTO_API_KEY']}",
  "Content-Type": "application/json"
}
payload = {
  "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
  "image": "https://tos.gptproto.com/resource/cat.png",
  "negative_prompt": "",
  "guidance_scale": 0.5,
  "duration": 5
}

response = requests.request("POST", url, headers=headers, json=payload)
print(response.json())
```

```typescript
const response = await fetch("https://gptproto.com/api/v3/kling/kling-v2.5-turbo-pro/image-to-video", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.GPTPROTO_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    prompt: "A tiny origami fox sailing a teacup across a moonlit puddle",
    image: "https://tos.gptproto.com/resource/cat.png",
    negative_prompt: "",
    guidance_scale: 0.5,
    duration: 5,
  }),
});

const data = await response.json();
console.log(data);
```

### 2. Poll until the task finishes

Replace `YOUR_RESULT_ID` with `data.id` from the submit response (or call `data.urls.get` directly), and keep polling every 1–3 seconds while `status` is `created` or `running`.

```bash
result_id="YOUR_RESULT_ID"

curl --request GET "https://gptproto.com/api/v3/predictions/$result_id/result" \
  --header "Authorization: Bearer $GPTPROTO_API_KEY"
```

```python
import os
import requests

result_id = "YOUR_RESULT_ID"
url = f"https://gptproto.com/api/v3/predictions/{result_id}/result"
headers = {
  "Authorization": f"Bearer {os.environ['GPTPROTO_API_KEY']}"
}
response = requests.request("GET", url, headers=headers)
print(response.json())
```

```typescript
const result_id = "YOUR_RESULT_ID";
const response = await fetch(`https://gptproto.com/api/v3/predictions/${result_id}/result`, {
  method: "GET",
  headers: {
    "Authorization": `Bearer ${process.env.GPTPROTO_API_KEY}`,
  },
});

const data = await response.json();
console.log(data);
```

## Example Prompts

- The elderly woodcarver meticulously carves the feathers of a wooden eagle with a sharp chisel, his hands steady as thin wood shavings curl away and fall onto the workbench. Dust motes swirl through the warm amber light of the workshop, and his eyes blink with deep concentration t…
- The focused street chef vigorously tosses the wok with "wok hei" technique, causing massive, vibrant flames to erupt and dance upwards in a rhythmic motion. Sweat beads glisten on his skin and drip down his face as he moves with expert precision. Thick white steam and swirling sm…
- The weathered street food chef with visible tattoos vigorously tosses a wok full of noodles over an intense, leaping flame. Sweat glistens and drips from his forehead as he works with rhythmic intensity, while thick clouds of steam and smoke swirl upward, mixed with glowing orang…

## HTTP Status Codes

- `400` — malformed body, a parameter or value this model does not accept, or input blocked by the provider's content moderation
- `401` — missing or invalid API key
- `403` — insufficient credits
- `413` — request body too large
- `429` — rate limited; retry with backoff
- `500` — An internal server error occurred
- `502` — An internal server error occurred
- `504` — Gateway timeout — upstream service did not respond in time; retry later

## Additional Resources

- [Model playground](https://gptproto.com/model/kling/kling-v2.5-turbo-pro)
- [API documentation](https://docs.gptproto.com/docs/allapi/Kling/kling-v2.5-turbo-pro/gptproto-format/image-to-video)
- [All models](https://gptproto.com/model)
- [API keys](https://gptproto.com/dashboard/api-key)
- [Platform overview for LLMs](https://gptproto.com/llm-full.txt)
- Any other model: `https://gptproto.com/model/{vendor}/{model}/{scene}/llms.txt`