LongCat Video · Text to Video · 480p
Integrieren
longcat-video/text-to-video/480p
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/longcat-video/text-to-video/480p?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/longcat-video/text-to-video/480p?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/longcat-video/text-to-video/480p", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/de/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.ru/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "longcat-video/text-to-video/480p",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
}
Rufe das Modell LongCat Video · Text to Video · 480p (longcat-video/text-to-video/480p) über die Doitong API auf. Dokumentation für Agenten: https://doitong.com/de/developers/models/longcat-video/text-to-video/480p.md
POST https://api.doitong.ru/v1/run/longcat-video/text-to-video/480p?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
Wenn der Antwortstatus "queued" oder "running" lautet, frage GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20 ab, bis er "succeeded" lautet; die Dateien befinden sich in outputs[].url. POST https://api.doitong.ru/v1/quote/longcat-video/text-to-video/480p mit demselben Body gibt den exakten Preis vor der Ausführung zurück.
Dateien wie das Beispiel: 0,27 $ 270 API-Credits · ≈ 29,05 ₽ Deine Dateien werden vor der Ausführung gemessen Auf dieser Seite ausführen
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Erstelle unten eine Anfrage und führe sie mit deinem API-Schlüssel aus. Ausgaben werden in unseren Speicher kopiert und dauerhaft oder 7 Tage kostenlos aufbewahrt.
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Die Steuerelemente werden aus dem Modellschema generiert. Code und Preis werden aktualisiert, während du sie änderst.
/run/longcat-video/text-to-video/480p?wait=20
Preis → 0,27 $
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/longcat-video/text-to-video/480p?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/longcat-video/text-to-video/480p?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/longcat-video/text-to-video/480p", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "longcat-video/text-to-video/480p",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
},
"wait_seconds": 20
}
}
}
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Über LongCat Video · Text to Video · 480p
Erzeugt ein Video ausschließlich aus einem Text-`prompt`. `aspect_ratio` kann 16:9, 9:16 oder 1:1 sein, und `num_frames` reicht bei 1 bis 60 `fps` von 17 bis 961 (Standard 162). Verfügbar sind `num_inference_steps` (8 bis 50), `guidance_scale` (1 bis 10) und ein negativer Prompt. Das Ergebnis kann MP4, WebM, ProRes MOV oder GIF sein.
Parameter
prompterforderlich
negative_promptoptional
num_framesoptional
num_inference_stepsoptional
guidance_scaleoptional
aspect_ratiooptional
fpsoptional
seedoptional
enable_prompt_expansionoptional
enable_safety_checkeroptional
video_output_typeoptional
video_qualityoptional
video_write_modeoptional
accelerationoptional
Ausgabe
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "longcat-video/text-to-video/480p",
"status": "succeeded",
"created_at": "2026-10-20T12:00:00Z",
"started_at": "2026-10-20T12:00:02Z",
"completed_at": "2026-10-20T12:00:09Z",
"queue_position": null,
"cost": {
"credits": 270,
"usd": "0.27",
"charged": 270,
"refunded": 0
},
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
},
"output": {
"prompt": "realistic filming style, a person wearing a dark helmet, a deep-colored jacket, blue jeans, and bright yellow shoes rides a skateboard along a winding mountain road. The skateboarder starts in a standing position, then gradually lowers into a crouch, extending one hand to touch the road surface while maintaining a low center of gravity to navigate a sharp curve. After completing the turn, the skateboarder rises back to a standing position and continues gliding forward. The background features lush green hills flanking both sides of the road, with distant snow-capped mountain peaks rising against a clear, bright blue sky. The camera follows closely from behind, smoothly tracking the skateboarder’s movements and capturing the dynamic scenery along the route. The scene is shot in natural daylight, highlighting the vivid outdoor environment and the skateboarder’s fluid actions.",
"seed": 42,
"video": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
"content_type": "video/mp4",
"file_name": "0-output.mp4",
"file_size": 1843200
}
},
"outputs": [
{
"type": "video",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
"content_type": "video/mp4",
"file_size": 1843200,
"width": 1280,
"height": 720,
"duration": 5,
"stored": true
}
],
"error": null,
"metrics": {
"inference_seconds": 6.8
},
"urls": {
"self": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Ausgabefelder
- prompt
- stringZur Generierung der Ausgabe verwendeter Prompt.
- seed
- integerWährend der Generierung angewendeter Seed-Wert.
- video
- object {url, content_type, file_name, file_size}Durch die Generierung erzeugte Videodatei.
Weitere LongCat Video-Varianten
Weitere Modelle: Text zu Video
Für KI-Agenten: Diese Seite ist als Markdown verfügbar unter https://doitong.com/de/developers/models/longcat-video/text-to-video/480p.md. Alle Modelle und Anleitungen: https://doitong.com/developers/llms.txt