# Kling Kling v2.5 Turbo Std — Text To Video

> Access Kling 2.5 turbo video capabilities for cinematic 1080p AI generation. Experience high temporal stability and physics-based motion on GPTProto.com.

## Overview

- **Endpoint**: `POST https://gptproto.com/api/v3/kling/kling-v2.5-turbo-std/text-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-std`
- **Vendor**: Kling
- **Scene**: `text-to-video`
- **Category**: text-to-video
- **Modalities**: input text → output video
- **Playground**: https://gptproto.com/model/kling/kling-v2.5-turbo-std/text-to-video
- **API documentation**: https://docs.gptproto.com/docs/allapi/Kling/kling-v2.5-turbo-std/gptproto-format/text-to-video
- **Other scenes of this model**: `image-to-video` — 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** — $0.168 per run
- **10** — $0.336 per run

Price range: $0.168 – $0.336 per generation.

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

## 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.

- **`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

- **`aspect_ratio`** (`enum`, _optional_):
  The aspect ratio of the generated media.
  - Default: `16:9`
  - Options: 1:1, 9:16, 16:9

- **`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"
}
```

**Full Example**:

```json
{
  "prompt": "A tiny origami fox sailing a teacup across a moonlit puddle",
  "negative_prompt": "",
  "guidance_scale": 0.5,
  "aspect_ratio": "16:9",
  "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-std",
    "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-std",
    "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-std/text-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",
    "negative_prompt": "",
    "guidance_scale": 0.5,
    "aspect_ratio": "16:9",
    "duration": 5
  }'
```

```python
import os
import requests

url = "https://gptproto.com/api/v3/kling/kling-v2.5-turbo-std/text-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",
  "negative_prompt": "",
  "guidance_scale": 0.5,
  "aspect_ratio": "16:9",
  "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-std/text-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",
    negative_prompt: "",
    guidance_scale: 0.5,
    aspect_ratio: "16:9",
    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

- In a sound-proofed home studio, a young ASMR artist closes her eyes in deep concentration, gently brushing a soft makeup brush against a high-fidelity binaural microphone. The room is warmly lit, creating a cozy and intimate atmosphere. She whispers softly into the mic: "You can…
- A contemporary dancer in translucent silk garments performing fluid, high-energy expressive movements and sharp contortions; the fabric trails through the air while disturbing floating dust motes in an abandoned industrial warehouse with rusted steel beams and cracked concrete fl…
- A professional contemporary dancer performing fluid, expressive movements and leaps through a cavernous, industrial abandoned warehouse. As they spin, dust particles swirl around their limbs and the minimalist fabric of their attire trails behind in slow motion. The camera execut…

## 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-std/text-to-video)
- [API documentation](https://docs.gptproto.com/docs/allapi/Kling/kling-v2.5-turbo-std/gptproto-format/text-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`