Kling Omni Video O1 is Kuaishou's first unified multi-modal video model with MVL (Multi-modal Visual Language) technology. Text-to-Video mode generates cinematic videos from text prompts with subject consistency, natural physics simulation, and precise semantic understanding. Ready-to-use REST API, best performance, no coldstarts, affordable pricing.
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$0.56실행당·~17 / $10
Cyberpunk action scene. Two cyborg samurais dash past each other on a rainy street. They strike with glowing laser katanas. A burst of blue and red sparks explodes in the center of the frame. The camera pans quickly to follow the movement. Unreal Engine 5 render, high octane.
Cinematic drone shot, aerial view gliding swiftly over a snow-capped mountain range during golden hour. The camera pushes forward through volumetric clouds to reveal a hidden valley with a glowing ancient temple. Realistic lighting, 4k, smooth motion, high fidelity.
First-person view (FPV) of an astronaut walking through a dark, abandoned spaceship corridor with a flashlight. The beam cuts through floating dust particles. Suddenly, the red emergency lights flicker on one by one down the hallway, revealing a massive alien structure at the end. Handheld camera movement, heavy atmosphere, cinematic lighting.
Extreme close-up of a texture that looks like rock. Suddenly, the 'rock' moves and an enormous dragon eye opens, the pupil contracting in the light. The camera quickly zooms out to reveal the dragon's head emerging from a misty mountain range. Debris falling, epic scale, hyper-realistic texture.
GoPro POV shot, fisheye lens. Skiing down a steep, vertical snowy mountain peak at extreme speed. Powder snow sprays into the camera lens. The sun is setting in the distance creating a lens flare. Intense speed, camera shake, wind visual effects, adrenaline rush.
Kling Omni Video O1 is Kuaishou's groundbreaking unified multi-modal video model, representing the world's first AI system that seamlessly integrates text, images, videos, and subject references into a single creative engine. The Text-to-Video mode transforms natural language prompts into stunning, cinematic video content.
Unlike traditional single-task models, Video O1 unifies multiple video generation capabilities:
The model interprets your instructions through a revolutionary MVL system that understands:
Maintains stable character, prop, and scene features across varying shots — similar to professional directing techniques used in film production.
Example: "A young woman walking through a neon-lit Tokyo street at night, rain reflecting city lights, cinematic tracking shot"
Set Parameters Choose your preferred duration, resolution, and aspect ratio.
Generate Submit your request and receive high-quality video output.
| Item | Price |
|---|---|
| Per Second | $0.112 |
Billed per second of output video duration.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-video-o1/text-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Kling Video O1 Text To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"duration": 5
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-video-o1/text-to-video" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/kwaivgi/kling-video-o1/text-to-video";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"duration": 5
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "16:9",
"duration": 5
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/kwaivgi/kling-video-o1/text-to-video", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Kling Video O1 Text To Video is a Kuaishou model for video generation, exposed as a REST API on WaveSpeedAI. Kling Omni Video O1 is Kuaishou's first unified multi-modal video model with MVL (Multi-modal Visual Language) technology. Text-to-Video mode generates cinematic videos from text prompts with subject consistency, natural physics simulation, and precise semantic understanding. Ready-to-use REST API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/kwaivgi/kwaivgi-kling-video-o1-text-to-video.
Kling Video O1 Text To Video starts at $0.56 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `aspect_ratio`, `duration`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/kwaivgi/kwaivgi-kling-video-o1-text-to-video.
Median end-to-end generation time on WaveSpeedAI is around 124 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (Kuaishou). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.