# Kling Kling v2.6 Std — Text To Video

> Explore Kling v2.6-std/Text-to-Video on GPT Proto: 1080p text-to-video with native audio, lip-sync, & realistic physics. Get the most affordable AI video API now.

## Overview

- **Endpoint**: `POST https://gptproto.com/api/v3/kling/kling-v2.6-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-v2.6-std`
- **Vendor**: Kling
- **Scene**: `text-to-video`
- **Category**: text-to-video
- **Modalities**: input text → output video
- **Playground**: https://gptproto.com/model/kling/kling-v2.6-std
- **API documentation**: https://docs.gptproto.com/docs/allapi/Kling/kling-v2.6-std/gptproto-format/text-to-video
- **Other scenes of this model**: `image-to-video`, `motion-control` — 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-v2.6-std

## 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.

- **`cfg_scale`** (`range`, _optional_):
  Flexibility in video generation; The higher the value, the lower the model’s degree of flexibility, and the stronger the relevance to the user’s prompt.
  - 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": "",
  "cfg_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-v2.6-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-v2.6-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-v2.6-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": "",
        "cfg_scale": 0.5,
        "aspect_ratio": "16:9",
        "duration": 5
    }'
```

```python
import os
import requests

url = "https://gptproto.com/api/v3/kling/kling-v2.6-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": "",
    "cfg_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-v2.6-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: "",
        cfg_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 the afternoon room, sunlight filters through the blinds, creating striped patches of light, and a cat lies on the windowsill. The cat breathes slowly, its body rising and falling with each breath. In the background, the distant, muffled chirping of birds and the rustling of fa…
- A solo contemporary dancer in flowing linen attire performing fluid, expressive spins and leaps within an industrial abandoned warehouse with cracked concrete floors and rusted beams. A dynamic 360-degree orbital tracking shot circles the dancer as dust particles swirl through hi…
- A contemporary dancer in loose linen attire performing fluid floor-work and expressive pirouettes; dust particles erupt from the concrete and swirl rhythmically with the dancer's movements within a vast, derelict industrial warehouse featuring rusted beams and cracked masonry. Th…

## 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-v2.6-std)
- [API documentation](https://docs.gptproto.com/docs/allapi/Kling/kling-v2.6-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/llms-full.txt)
- Any other model: `https://gptproto.com/model/{vendor}/{model}/{scene}/llms.txt`