Kling V3 Turbo Pro converts text prompts into high-quality 1080P videos with fast pro-tier inference, single-prompt generation, and multi-shot storyboard support. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
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$0.14cho mỗi lần chạy·~71 / $10
A foreign female mechanic repairing a giant humanoid mech inside an industrial hangar, sparks flying, oil stains, robotic parts, orange work lights, realistic sci-fi cinematic scene
Kling V3 Turbo Pro Text-to-Video generates high quality 1080p videos from text prompts. It supports both single-prompt generation and multi-shot storyboard generation with per-shot durations.
High quality 1080p video generation
Create polished pro-tier videos directly from text prompts.
Fast turbo inference
Use the turbo version for faster generation while keeping strong visual quality.
Single prompt or multi-shot storyboard
Generate a complete video from one prompt, or use multi_prompt to define multiple shots.
Per-shot duration control
Set each storyboard segment duration when using multi_prompt.
Standard video output
The generated video is returned as a URL in the standard WaveSpeed prediction response.
| Parameter | Required | Description |
|---|---|---|
| prompt | No | Text prompt describing the video to generate. Mutually exclusive with multi_prompt. |
| multi_prompt | No | Multi-shot storyboard. Each item includes a prompt and duration. Mutually exclusive with prompt. |
| aspect_ratio | No | Output aspect ratio. Options: 16:9, 9:16, 1:1. Default: 16:9. |
| duration | No | Video duration in seconds for single-prompt generation. Options: 3 to 15. Default: 5. |
multi_prompt to define multiple shots with individual durations.16:9, 9:16, or 1:1.multi_prompt.Returns generated video URL(s) in the standard WaveSpeed prediction response.
The generated video is returned as MP4 video.
Pricing is $0.14 per second.
WaveSpeed bills by the generated video duration. If multi_prompt is provided, billing uses the selected duration plus the sum of all segment durations.
| Generated Duration | Price |
|---|---|
| 3s | $0.42 |
| 5s | $0.70 |
| 10s | $1.40 |
| 15s | $2.10 |
duration parameter.multi_prompt requests use duration plus the sum of all segment durations.multi_prompt when you need multiple shots instead of one continuous prompt.multi_prompt duration within the model limit.prompt or multi_prompt.prompt and multi_prompt are mutually exclusive.duration is used for single-prompt generation.multi_prompt supports per-shot durations.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-v3-turbo-pro/text-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Kling v3 Turbo Pro Text To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"aspect_ratio": "16:9",
"duration": "5"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-v3-turbo-pro/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-v3-turbo-pro/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"aspect_ratio": "16:9",
"duration": "5"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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 = {
"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-v3-turbo-pro/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 v3 Turbo Pro Text To Video is a Kuaishou model for video generation, exposed as a REST API on WaveSpeedAI. Kling V3 Turbo Pro converts text prompts into high-quality 1080P videos with fast pro-tier inference, single-prompt generation, and multi-shot storyboard support. Ready-to-use REST inference 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-v3-turbo-pro-text-to-video.
Kling v3 Turbo Pro Text To Video starts at $0.14 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`, `multi_prompt`. 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-v3-turbo-pro-text-to-video.
Median end-to-end generation time on WaveSpeedAI is around 118 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.