Kling 3.0 Standard delivers high-quality text-to-video generation with smooth motion, cinematic visuals, accurate prompt adherence, and native audio for ready-to-share clips. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.
就绪
$0.42每次运行·~23 / $10
Wide-angle shot. A lone figure with a large backpack walking through a dust storm-swept post-apocalyptic desert ruin. Wind blowing their cloak. Slow camera dolly out to reveal the desolate environment. Grainy film look.
A young woman with long curly brown hair sits in a dark movie theater on a deep red velvet seat, holding a red paper cup in both hands. She gazes up at the screen with a soft, amused smile. Her expression shifts subtly — her eyes widen slightly with interest, then she lets out a gentle laugh, her shoulders rising briefly. Her curly hair sways ever so slightly as she tilts her head. The flickering light from the movie screen casts warm, shifting reflections across her face, alternating between soft highlights and shadow. She lifts the red cup slowly to take a sip, then lowers it back down. The dark cinema background with rows of empty red seats remains still and moody. Cinematic lighting, shallow depth of field, warm color grading. 4K, 24fps.
Kling V3.0 Standard Text-to-Video is Kuaishou's cost-efficient text-to-video model, generating smooth, cinematic video directly from natural language prompts at accessible pricing. With flexible duration, multiple aspect ratios, optional synchronized sound, and precise prompt control, it delivers strong visual quality for a wide range of production workflows.
V3.0 quality at Standard pricing
Strong visual quality and motion coherence at a fraction of Pro tier cost.
Flexible duration
Generate videos from 3 to 15 seconds to match your scene needs.
Multiple aspect ratios
Support for 16:9, 9:16, and 1:1 to fit any platform or format.
Sound generation
Optional synchronized sound effects generated alongside the video.
Negative prompt support
Specify what you don't want in the video for more precise control over the output.
Multi-prompt support
Chain prompt segments to guide scene transitions and progression.
Prompt Enhancer
Built-in tool to automatically improve your video descriptions.
| Parameter | Required | Description |
|---|---|---|
| prompt | No | Text description of the scene, motion, camera style, and atmosphere. |
| negative_prompt | No | Elements to exclude from the video. |
| duration | No | Video length in seconds. Range: 3–15. Default: 5. |
| aspect_ratio | No | Output ratio: 16:9 (default), 9:16, 1:1. |
| cfg_scale | No | Prompt guidance strength. Default: 0.5. |
| sound | No | Generate synchronized sound alongside the video. Default: disabled. |
| shot_type | No | Editing mode: intelligent (default, auto-determines scope) or customize. |
| multi_prompt | No | Additional prompt segments to guide scene transitions and progressions. |
| Duration | Without Sound | With Sound |
|---|---|---|
| 3s | $0.252 | $0.378 |
| 5s | $0.420 | $0.630 |
| 10s | $0.840 | $1.260 |
| 15s | $1.260 | $1.890 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/kwaivgi/kling-v3.0-std/text-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Kling v3.0 Std Text To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"duration": 5,
"aspect_ratio": "16:9",
"cfg_scale": 0.5,
"shot_type": "customize"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-v3.0-std/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-v3.0-std/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"duration": 5,
"aspect_ratio": "16:9",
"cfg_scale": 0.5,
"shot_type": "customize"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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 = {
"duration": 5,
"aspect_ratio": "16:9",
"cfg_scale": 0.5,
"shot_type": "customize"
}
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-v3.0-std/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 v3.0 Std Text To Video is a Kuaishou model for video generation, exposed as a REST API on WaveSpeedAI. Kling 3.0 Standard delivers high-quality text-to-video generation with smooth motion, cinematic visuals, accurate prompt adherence, and native audio for ready-to-share clips. Ready-to-use REST inference API, best performance, no cold starts, 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-v3.0-std-text-to-video.
Kling v3.0 Std Text To Video starts at $0.42 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`, `negative_prompt`, `cfg_scale`, `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-v3.0-std-text-to-video.
Median end-to-end generation time on WaveSpeedAI is around 88 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.