Video

Kling 2.5 Turbo Pro API

Fast, stylized Kling animation.

10s
Max clip length: ~135s
Avg. generation: 22 credits
From

Video · made with Kling 3.0

Cinematic push-in on a barista pouring rust-colored latte art into a white Ember Coffee cup, steam, warm tungsten, gentle cafe sound.

Playground

Try Kling 2.5 Turbo Pro right here.

model: kling-2.5-turbo-pro

Prompt

Aspect ratio Duration

Media inputs

Start image End image

Advanced settings

Negative prompt Guidance scale

Estimated cost 22–44 credits · $0.22–$0.44 Generate in Studio

Output Example · Kling 3.0

Cinematic push-in on a barista pouring rust-colored latte art into a white Ember Coffee cup, steam, warm tungsten, gentle cafe sound.

Pricing

Simple per-generation pricing.

22–44 credits per generation ≈ $0.22–$0.44

The rate depends on the resolution and options you pick.

You only pay for successful generations. Failed jobs are refunded automatically.
One credit balance across every model on Apiframe, no per-model plans.
Start with free credits. No subscription required.
Volume discounts on larger plans.

See plans and credit packs

Specs

At a glance.

Provider Kuaishou

Model ID kling-2.5-turbo-pro

Aspect ratios 1:1, 9:16, 16:9

Durations 5s, 10s

Image input Supported

Avg. time ~135s

Quickstart

How to call the Kling 2.5 Turbo Pro API.

Send one POST /v2/videos/generate request with your API key. The call returns a jobId you can poll, or pass a webhook_url and the result is pushed to you the moment it's ready.

Swap kling-2.5-turbo-pro for any other model and nothing else changes.

Get API key Read the docs

generate.js

const response = await fetch("https://api.apiframe.ai/v2/videos/generate", {
  method: "POST",
  headers: {
    "X-API-Key": "afk_your_api_key_here",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "prompt": "a cinematic sunrise over a futuristic cityscape, smooth camera push-in",
    "model": "kling-2.5-turbo-pro",
    "klingParams": {
      "start_image": "https://example.com/input.jpg",
      "end_image": "https://example.com/input.jpg",
      "negative_prompt": "What to avoid (e.g. blurry, distorted, watermark)",
      "guidance_scale": 0.5
    }
  }),
});
const { jobId } = await response.json();
console.log(jobId);

import requests

response = requests.post(
    "https://api.apiframe.ai/v2/videos/generate",
    headers={
        "X-API-Key": "afk_your_api_key_here",
        "Content-Type": "application/json",
    },
    json={
        "prompt": "a cinematic sunrise over a futuristic cityscape, smooth camera push-in",
        "model": "kling-2.5-turbo-pro",
        "klingParams": {
            "start_image": "https://example.com/input.jpg",
            "end_image": "https://example.com/input.jpg",
            "negative_prompt": "What to avoid (e.g. blurry, distorted, watermark)",
            "guidance_scale": 0.5
        }
    },
)
print(response.json())  # { "jobId": "...", "status": "QUEUED" }

curl -X POST https://api.apiframe.ai/v2/videos/generate \
  -H "X-API-Key: afk_your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
        "prompt": "a cinematic sunrise over a futuristic cityscape, smooth camera push-in",
        "model": "kling-2.5-turbo-pro",
        "klingParams": {
            "start_image": "https://example.com/input.jpg",
            "end_image": "https://example.com/input.jpg",
            "negative_prompt": "What to avoid (e.g. blurry, distorted, watermark)",
            "guidance_scale": 0.5
        }
    }'

let job;
do {
  await new Promise((r) => setTimeout(r, 2000));
  job = await fetch("https://api.apiframe.ai/v2/jobs/JOB_ID", {
    headers: { "X-API-Key": "afk_your_api_key_here" },
  }).then((r) => r.json());
} while (job.status !== "COMPLETED" && job.status !== "FAILED");
console.log(job.result);

import requests, time

while True:
    job = requests.get(
        "https://api.apiframe.ai/v2/jobs/JOB_ID",
        headers={"X-API-Key": "afk_your_api_key_here"},
    ).json()
    if job["status"] in ("COMPLETED", "FAILED"):
        break
    time.sleep(2)
print(job["result"])

curl https://api.apiframe.ai/v2/jobs/JOB_ID \
  -H "X-API-Key: afk_your_api_key_here"

Input schema

Every field Kling 2.5 Turbo Pro accepts, with types and defaults. The full reference lives in the docs.

Field	Type	Description
prompt required	string	Text description of what to generate.
model required	string	The model identifier for this endpoint. Default:"kling-2.5-turbo-pro" · "kling-2.5-turbo-pro"
klingParams.start_image	string (URL)	Use a still as the first frame of the clip.
klingParams.end_image	string (URL)	Optional last frame to control the ending.
klingParams.negative_prompt	string	Negative prompt
klingParams.guidance_scale	number	How strictly to follow the prompt (0–1). Default:0.5 · min 0, max 1, step 0.05

The Kling flagship

Kling 2.5 Turbo Pro is one of 6 Kling video models on Apiframe. The full editorial page (showcase, comparison, and use cases) lives on the flagship.

Flagship from 11 cr/s Kling 3.0

Multi-shot video with pro-grade 4K modes.

Explore Kling 3.0 All video models

Kling 2.5 Turbo Pro questions

What teams ask before shipping with Kling 2.5 Turbo Pro. Everything else lives in the docs.

Is there an API for Kling 2.5 Turbo Pro?

Yes. Apiframe exposes Kling 2.5 Turbo Pro through a single REST endpoint (POST /v2/videos/generate with model: "kling-2.5-turbo-pro"). You get one unified API, key, and billing across every supported model, with no separate Kuaishou account required.

How much does the Kling 2.5 Turbo Pro API cost?

Kling 2.5 Turbo Pro uses simple pay-as-you-go pricing on Apiframe. You only pay for successful generations. See the pricing page for plans and volume discounts.

How do I call the Kling 2.5 Turbo Pro API?

Send a POST request to /v2/videos/generate with your X-API-Key and a JSON body containing model: "kling-2.5-turbo-pro". The call returns a jobId; poll GET /v2/jobs/{id} or use a webhook to receive the result.

What parameters does Kling 2.5 Turbo Pro support?

Kling 2.5 Turbo Pro accepts 4 model-specific parameters (nested under klingParams) plus the common prompt and webhook_url fields. See the input schema above for the full list with types and defaults.