Kling AI Video Generator: Pricing, API and Code (2026)

The Kling AI video generator is Kuaishou’s family of video models, and the current version, Kling 3.0 Omni, is one of the few models that will take a text prompt, a start frame, an end frame, reference images and a multi-shot script in the same request. This article covers what the model actually does, what each clip costs, how to call it from code, and the specific mistakes that waste credits.

What is the Kling AI video generator?

Kling is a generative video service built by Kuaishou, the Beijing-based short-video company, and first released in 2024. The Kling AI video generator takes a written description, a still image, or both, and produces a short video clip with synchronised sound.

Two terms worth pinning down before going further:

• Text to video means the model receives only words and invents every frame. Text-to-video models emerged publicly in 2022 and became usable for real production work around 2024.
• Image to video means you supply the first frame as a picture and the model animates from there. This is the mode most commercial users want, because you control exactly how the subject looks.

Under the hood these systems are diffusion models: they start from noise and remove it step by step, guided by your prompt, in a compressed representation rather than at full pixel resolution. That compression trick, introduced by the latent diffusion architecture, is what makes generating 360 frames of video affordable at all. You do not need to care about the mechanics day to day, but it explains two behaviours you will meet: cost scales almost linearly with seconds and pixels, and the model has no frame-by-frame plan, so long clips drift.

There are two ways to reach the Kling AI video generator. The first is Kuaishou’s own web app and mobile app, where you log in, buy a subscription and render in a browser. The second is an API, which is what you want if the videos are going into a product, a batch pipeline or an automated content workflow. Most of the “free Kling” sites that rank for this keyword are the third category: resellers wrapping the same API with their own limits on length, resolution and queue priority.

What Kling 3.0 Omni can actually do

Kling 3.0 Omni, released in August 2026, is the version worth building on. Its capabilities, as exposed through Unicorn API’s Kling 3.0 Omni endpoint:

• Resolutions: 720p, 1080p and 4K.
• Durations: 3 to 15 seconds, in whole seconds.
• Aspect ratios: 16:9, 9:16 and 1:1. No 4:3, so letterbox in post if you need it.
• Native audio: set with_audio to true and the model generates ambience, effects and dialogue in sync with the picture, instead of you dubbing it afterwards.
• Start and end frames: image sets the first frame, end_image sets the last. Give both and the model fills the middle, which is the cleanest way to get a controlled camera move.
• References: reference_images and reference_videos carry a character, product or style across multiple shots.
• Elements: the elements field lets you define recurring subjects so the same face or object appears consistently.
• Multi-shot: multi_prompt, prefer_multi_shots and customize_multi_shots turn one request into a sequence of camera angles rather than a single continuous take.

That last group is the real differentiator. Most video models give you one shot per request, and you stitch shots together in an editor. The Kling AI video generator can storyboard inside a single 15 second render, which removes an entire editing step from short-form ad and trailer work.

Kling AI video generator pricing in credits

On Unicorn API everything is billed in credits, and credits cost $0.01 each on a monthly plan: Starter $25 for 2,500 credits, Creator $50 for 5,000, Studio $100 for 10,000, Pro $200 for 20,000. Top-up credits cost $0.015 each and never expire, so they are the right choice for bursty work and the wrong choice for steady volume.

Kling 3.0 Omni is priced per second:

• 720p: 9.8 credits per second, or 12.6 with audio
• 1080p: 12.6 credits per second, or 16.1 with audio
• 4K: 46.9 credits per second

Totals are rounded up to whole credits. Worked out for the clip lengths people actually use:

Clip	Resolution	Audio	Credits	Cost on a monthly plan
5 seconds	720p	No	49	$0.49
5 seconds	720p	Yes	63	$0.63
5 seconds	1080p	No	63	$0.63
5 seconds	1080p	Yes	81	$0.81
10 seconds	720p	Yes	126	$1.26
10 seconds	1080p	Yes	161	$1.61
15 seconds	1080p	No	189	$1.89
5 seconds	4K	No	235	$2.35

Put that against a plan and the budgeting becomes obvious. A $25 Starter plan buys about 51 five-second 720p clips, or about 30 of the same clip at 1080p with audio, or about 10 at 4K. If you are iterating on a prompt, that is 10 attempts at 4K versus 51 at 720p, which is the whole argument for drafting low and finishing high.

You never have to guess. Every request can be priced before it runs by posting the same body to /v1/models/price, which returns the exact credit cost for that combination of duration, resolution and audio. Full rates for every model are on the pricing page.

How to call the Kling AI video generator from your own code

Video models are asynchronous. You submit a job, get an ID back immediately, then poll until the render finishes. Here is the minimal request:

curl -X POST https://api.unicornapi.net/v1/jobs \
  -H "Authorization: Bearer $UNICORN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kling-3.0-omni/video",
    "input": {
      "prompt": "A slow dolly shot through a rain-soaked Tokyo alley at night, neon signs reflecting in puddles, steam rising from a noodle stall",
      "duration": 5,
      "resolution": "1080p",
      "aspect_ratio": "16:9",
      "with_audio": true
    }
  }'

The response contains an id and a status. Poll it:

curl https://api.unicornapi.net/v1/jobs/JOB_ID \
  -H "Authorization: Bearer $UNICORN_API_KEY"

When status becomes "succeeded", the video URL is at outputs[0].url. Here is the whole loop in Python, with a price check first so nothing renders without you knowing the cost:

import os
import time
import requests

BASE = "https://api.unicornapi.net"
HEADERS = {"Authorization": f"Bearer {os.environ['UNICORN_API_KEY']}"}


def render(prompt, **options):
    body = {
        "model": "kling-3.0-omni/video",
        "input": {"prompt": prompt, **options},
    }

    price = requests.post(f"{BASE}/v1/models/price", json=body, headers=HEADERS)
    price.raise_for_status()
    print("quoted credits:", price.json())

    created = requests.post(f"{BASE}/v1/jobs", json=body, headers=HEADERS)
    created.raise_for_status()
    job_id = created.json()["id"]

    while True:
        job = requests.get(f"{BASE}/v1/jobs/{job_id}", headers=HEADERS).json()
        status = job["status"]

        if status == "succeeded":
            return job["outputs"][0]["url"]
        if status in ("failed", "canceled"):
            raise RuntimeError(f"job {job_id} {status}: {job.get('error')}")

        time.sleep(5)


url = render(
    "A slow dolly shot through a rain-soaked Tokyo alley at night, "
    "neon signs reflecting in puddles, steam rising from a noodle stall",
    duration=5,
    resolution="1080p",
    aspect_ratio="16:9",
    with_audio=True,
)
print(url)

For image to video, pass an image URL as the start frame. This JavaScript version animates a product photo and locks the final frame too, so the clip ends on a composition you chose:

const res = await fetch("https://api.unicornapi.net/v1/jobs", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.UNICORN_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "kling-3.0-omni/video",
    input: {
      prompt:
        "The camera orbits slowly to the right around the bottle on a wet stone slab, soft studio light, shallow depth of field",
      image: "https://example.com/bottle-front.jpg",
      end_image: "https://example.com/bottle-three-quarter.jpg",
      duration: 5,
      resolution: "1080p",
      aspect_ratio: "9:16",
      with_audio: false,
    },
  }),
});

const { id } = await res.json();
// then poll GET /v1/jobs/{id} until status === "succeeded"

One API key covers every model in the catalog, so switching from Kling to Wan or Grok is a change to the model string and nothing else. Create a key at /keys, read the request and response schemas in the docs, and if you would rather have an agent write the integration, the MCP server exposes the same models to Cursor and Claude Code.

Writing prompts that hold up

The Kling AI video generator responds to prompts written like a shot list, not like a wish. Four components, in this order, get the most predictable results:

1. Subject and setting: who or what, and where. Be specific about materials and surfaces, since those drive the lighting.
2. Action: one action, described as a continuous motion. “Pours coffee into a glass cup” works. “Makes breakfast, then sits down, then reads” is three shots fighting for 5 seconds.
3. Camera: name the move. Slow dolly in, handheld tracking shot, static wide, orbit left. Without this instruction the model picks its own, and it tends to pick drift.
4. Light and mood: golden hour backlight, hard overhead fluorescent, candlelit. This is the single biggest lever on whether output looks cheap or shot.

If with_audio is on, add one short clause about sound: “ambient rain and distant traffic, no music”. Left unsaid, the model guesses, and the guess is often a music bed you do not want.

Draft at 720p without audio at 9.8 credits per second, lock the prompt, then re-render the keeper at 1080p or 4K. Ten drafts plus one 1080p final with audio costs about 571 credits; ten drafts straight at 4K costs 2,350.

For multi-shot work, keep each shot in the sequence to one idea and reuse the same reference_images across every request in a campaign. That is how you keep a face or a product recognisable across clips that were rendered hours apart.

Kling 3.0 Omni compared with other video models

Public leaderboards that rank video generators place Kling 3.0 in the same top tier as Sora, Runway 4.5 and Seedance 2.0, so the decision is almost never about which model is best in the abstract. It is about cost per second, maximum length, and which controls you need. Here is how Kling sits against the other video models on the same API:

Model	Modes	Length	Credits per second	Audio
Kling 3.0 Omni	Text, image, start/end frame, references, multi-shot	3-15s	9.8 at 720p, 12.6 at 1080p, 46.9 at 4K	Optional, +30%
Wan 3.0	Text or image, start/end frame, references	2-30s	6 at 480p, 12 at 720p, 24 at 1080p	Included free
Wan 3.0 Video Prime	Text, first/last frame, references, audio input	Per second	12.2 at 480p, 25.2 at 720p, 50.4 at 1080p	Audio input supported
Grok Imagine Video 1.5	Image to video only	1-15s	3 at 480p, 4 at 720p, 7 at 1080p	No
Grok Imagine Video 1.0	Text or image to video	6-30s	3 at 480p, 4 at 720p, 7 at 1080p	No
MiniMax H3 Image to Video	Image to video, first/last frame	Per second	8 at 768P, 13 at 2K	No
MiniMax H3 Reference to Video	Reference image, video and audio inputs	Per second	8 at 768P, 13 at 2K	Audio input supported

The practical read on that table:

• Choose Kling 3.0 Omni when you need the controls: end frames, character elements, multi-shot storyboards, or 4K output. Nothing else in the list offers all four.
• Choose Wan 3.0 when you need length or free audio. Up to 30 seconds and audio at no extra cost makes a 10 second 720p clip with sound 120 credits, against 126 for Kling at the same spec, and the gap widens at 480p where Kling does not go.
• Choose Grok Imagine Video 1.5 for volume animation of existing images. At 7 credits per second for 1080p it is roughly a third of Kling’s 1080p rate with audio off, which matters when you are animating a 500-product catalog. There is more on that workflow in our guide to image to video generation.
• Choose MiniMax H3 when you want 2K from a reference image or need to drive motion from an audio track.

Against the consumer tools people also search for, the comparison is a different axis. Canva, CapCut, InVideo, Synthesia and Adobe Firefly are editors and templating products with a video model attached; several of them run Kling or similar models behind the scenes. They are the right answer if a human is making one video in a browser. An API is the right answer if software is making a thousand.

Mistakes people make with the Kling AI video generator

These are the ones that cost real money and real hours:

• Prompting at 4K. At 46.9 credits per second, a single 10 second 4K render is 469 credits. Iterate at 720p. The composition, motion and timing you are judging do not change with resolution.
• Leaving with_audio on during iteration. Audio adds about 30 percent to every draft, and you are not listening to the drafts.
• Polling too aggressively. A five second interval is plenty. Hammering the status endpoint every 200 milliseconds gets you rate limited and does not make the GPU faster.
• Treating a failed job as a crash. Video renders fail occasionally, and the right response is a bounded retry with backoff around the same body, not a stack trace in production.
• Packing three shots into one prompt. Within 5 seconds the model will blend them into something incoherent. Use the multi-shot fields, or render separate clips and cut them together.
• Expecting legible text. Signage, labels and UI in generated video are unreliable. Composite real text over the clip afterwards.
• Sending an unsupported duration or aspect ratio. Durations are whole seconds from 3 to 15, and ratios are 16:9, 9:16 and 1:1 only. Validate before you submit.
• Skipping the price call. One line gets you the exact cost before the job runs. There is no reason to be surprised by a bill.

Getting a first clip rendered today

The shortest useful path through the Kling AI video generator:

1. Try a prompt in the browser playground to see what the model does with your subject before writing any code.
2. Create a key at /keys and set it as UNICORN_API_KEY.
3. Run the Python function above at 720p, no audio, 5 seconds. That is 49 credits per attempt.
4. Once the prompt is right, re-render at 1080p with audio for 81 credits, or 4K at 235 if it is going on a big screen.
5. If you need clips longer than 15 seconds, chain renders by passing the last frame of one clip as the image of the next, or move that job to Wan 3.0, which goes to 30 seconds.

Everything else is production plumbing: store the job ID with your own record, retry failures with backoff, download and re-host the output rather than hotlinking it, and keep the exact request body you sent so a clip can be reproduced. If you are also generating or cleaning up the still images that feed into the video, the image editing models guide and the video enhancement options cover the two steps either side of the render.