NVIDIA Nemotron 3.5 Super Video
ᠵᡠᠸᡝᠪᡠᠮᠪᡳ
nvidia/nemotron-3-5-super/video
# 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/nvidia/nemotron-3-5-super/video?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",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}'
# 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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/nvidia/nemotron-3-5-super/video?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/nvidia/nemotron-3-5-super/video", 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/mnc/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": "nvidia/nemotron-3-5-super/video",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}
}
NVIDIA Nemotron 3.5 Super Video ᠮᠣᠳᡝᠯ (nvidia/nemotron-3-5-super/video) ᠪᡝ Doitong API ᠵᠠᠯᡳᠨᡩᡝ ᠠᠰᡠᠮᠪᡳ. ᠠᡤᡝᠨᡨᡳ ᡩᠠᡳᠯᠠᠨ: https://doitong.com/mnc/developers/models/nvidia/nemotron-3-5-super/video.md
POST https://api.doitong.ru/v1/run/nvidia/nemotron-3-5-super/video?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",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}
ᡝᡵᡝᠮᡝ ᠪᡠᠮᡝᠨᡳ ᠠᠨᠠᠮᡝ "queued" ᡳᠨᡠ "running" ᠪᡝ, GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20 ᠪᡝ ᠠᠨᠠᠮᡝ "succeeded" ᠪᡝᠴᡳ ᠠᠰᡠᠮᠪᡳ; ᡶᡳᠯᡝ ᠪᡝ outputs[].url ᠪᡝᠴᡳ ᠣᠨᡩᠣᠮᡝ. POST https://api.doitong.ru/v1/quote/nvidia/nemotron-3-5-super/video ᠪᡝ ᡝᠮᡠ ᠪᡠᠶᡝᠮᡝ ᠪᡝ ᠠᠰᡠᠮᠪᡳᡩᡝ ᠵᠠᠯᡤᠠᠨ ᠪᡝ ᠶᠠᠪᡠᠮᠪᡳ ᠰᡳᠨᡳ ᠠᠮᡤᠠ ᠠᠰᡠᠮᠪᡳ ᠣᠯᡥᠣᠮᡝ.
ᡴᡳᠴᡝ ᡩᡝ ᠵᡠᠯᡤᡝ ᡤᡡᠨᠠᠨ ᠪᡝ $0.322 ᠪᠠᠶᠠᠮᠪᡳ ᠵᠣᡵᡤᠣᠨᡳ ᡠᠰᡳᡥᠠᡩᡝ, ᠰᡠᠯᡝ ᠠᠮᡝᠪᡠᠮᠪᡳ ᡝᡵᡝ ᠵᠠᠯᡳᠨᡩᡝ ᠠᠰᡠᠮᠪᡳ
ᠰᡳᠨᡳ ᠵᠠᠮᡠᠰᡳᠨ API ᠵᡳᡵᡤᠠ. ᡝᠯᡝ ᡝᠨᡩᡝ ᡳᠯᡤᠠᠮᠪᡳ ᡝᡵᡝ ᠮᡠᠨᡩᡝ ᡴᠣᡩᡝ ᡩᠠᠮᡠ ᠮᡝᠨᡩᡝᡵᡝ ᠪᠠᡩᡝᠯᡝᠨ ᠠᠮᡪᡳ ᡳᠨᡠ API ᠵᡳᡵᡤᠠ ᡩᡝᠪᡨᡝᡵᡝ ᡩᡝ ᠠᡴᡠ᠉ ᡳᠨᡠ ᡝᡵᡝᠩᡤᡝ ᠠᠮᠪᠠᠨ ᠮᡠᠨᡩᡝ ᠪᡝ ᠠᠰᡠᡵᠠᠮᠪᡳ: ᠮᡠᡵᡝ ᡩᠠᠮᡠ ᠪᡠᠶᡝᡵᡝ ᠪᠠᡩᡝᠯᡝᠨ ᡩᡝ ᠰᡳᠨᡩᡝᠮᡝ ᠠᠪᡠᠮᡝ ᠰᠠᠮᡪᡳ᠉
ᡳᠨᡝ ᡝᠮᡠᠨᡳᠩᡤᡝ ᡝᡵᡝᠮᡝ ᡩᡝ
ᡝᡵᡝ ᠵᡠᠯᡝᠨ ᡩᡝ ᡤᡝᡪᡳᠯᡝᠨ ᠪᠠᠶᠠᠨᠠᠮᡝ ᠠᠮᡪᠠᠯᠠ ᡶᡳᠯᡝᠮᡝ ᠪᡝᠶᡝᠮᡝ᠃ API ᠵᡳᡵᡤᠠᡳ ᡤᠠᡳᡥᠠᡩᡝ ᡤᡝᡪᡳᠯᡝᠨ ᠵᠠᠯᠠᠨᠠᠮᡝ ᠪᡠᠶᡝᠮᡝ ᠮᡠᠰᡝᡳ ᠰᡠᠯᡠᠨᡩᡝ ᠪᠠᠶᠠᠨᠠᠮᡝ᠂ ᠮᡤᡳ ᠮᡠᠰᡝ ᠠᠶᠠᠨ 7 ᡥᠠᠨ ᠰᠠᡳᠨᠠᠮᡝ᠃
ᡤᡳᠰᡠᠨ ᠪᡠᠶᡝᠮᡝ
ᠵᠠᡴᡠᠨᡳ ᠵᡠᠯᡝ ᡩᡝᡵᡤᡳ ᡩᡝᠪᡝᠰᡠᠮᡝ ᠵᡠᠯᡤᡝ ᠪᡠᠶᡝᠮᡝ᠈ ᡳᠨᡳ ᡤᡳᠰᡠᠨ ᠪᠠᡳ ᡝᠨᡝᠩᡤᡳ ᠠᠮᠪᡳ
/run/nvidia/nemotron-3-5-super/video?wait=20
ᠵᠠᠯᡳᠨ → $0.322
# 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/nvidia/nemotron-3-5-super/video?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",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}'
# 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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/nvidia/nemotron-3-5-super/video?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/nvidia/nemotron-3-5-super/video", 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": "nvidia/nemotron-3-5-super/video",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
},
"wait_seconds": 20
}
}
}
ᡝᠨᡳ ᠵᠠᠯᡳᠨ ᠪᡝ ᠵᠠᠯᠠᠨᠠᠮᡝ
ᡳᠨᡝ ᠵᠠᠯᠠᠨ ᡝᡵᡝ ᠪᡠᡵᡝᠪᡝᡵᡳ ᡩᡝ ᠪᡳᠮᡝ᠈ Doitong API ᡩᡝ ᠵᡝᠯᡤᡳ ᡩᠠᡵᠠᠮᡝ᠈ Remember ᠵᡠᠯᡝᠮᡝ ᡤᡳᠯᠠᠮᡝ localStorage ᡩᡝ ᠰᡠᠯᡝᠮᡝ ᠪᡳᠮᡝ API ᠵᠠᠯᠠᠨ ᠠᠪᠠᠮᡝ
NVIDIA Nemotron 3.5 Super Video ᡠᡵᡝᠮᡝ
`video_url` ᡩᡝ ᡤᠠᠰᡳᡥᠠ mp4 ᠪᡝ ᠠᠪᡠᠮᡝ (2 ᠮᡳᠨᡠᡨᡝ ᡩᡝ ᠴᡳ ᠠᠮᠪᠠ ᠪᡝ᠂ ᠵᡠᠸᠠᠨ ᠵᡳᠨ 3840 ᡩᡝ ᠴᡳ ᠠᠮᠪᠠ) ᡝᠷᡝᠴᡳ ᡥᡡᠯᡝᠨ ᡳ `prompt` ᠪᡝ ᠠᠪᡠᠮᡝ᠂ ᠵᡳᠯᡤᠠᠨ ᡩᡝ ᡩᠠᠯᡵᡳᠨ ᡤᡝᠰᡝᠨ ᠪᡝ ᡩᠠᠪᠠᡵᠠᠮᡝ. ᠠᠰᡠᠯᠠᠮᡝ ᡤᠠᠰᡳᡥᠠ ᠪᡝ ᡥᡡᠯᡝᠨ ᡩᡝ ᠵᠠᠯᡠᠮᡝ ᡩᠠᠯᡵᡳᠨ ᡠᠵᡠᠩᡤᠠ ᠶᠠᠪᡠᠮᡝ. `reasoning_mode` ᡤᠠᠰᡳᡥᠠ ᡤᡝᠯᡳ ᡩᠠᠯᡵᡳᠨ ᡤᡝᠰᡝᠨ ᠪᡝ ᡩᠠᠪᠠᡵᠠᠮᡝ᠂ `max_tokens` 32000 ᡩᡝ ᠰᡠᠯᡥᡝᠮᡝ.
ᠵᠠᡴᡠᠨ
ᠵᠠᡴᡠᠨᡳ ᡤᡳᠰᡠᠨ ᠵᡝᠯᡤᡳ ᠠᠪᠠᠮᠪᡳ᠈ ᠨᡝᠮᡝ ᠮᡠᠰᡝ ᡠᠯᠠᠮᠪᡳ ᠵᠠᡴᡠᠨᡳ ᡤᡳᠰᡠᠨ
promptᡴᠠᠷᠠᡴᡡ
system_promptᠵᡠᠯᡝ
reasoning_modeᠵᡠᠯᡝ
max_tokensᠵᡠᠯᡝ
temperatureᠵᡠᠯᡝ
top_pᠵᡠᠯᡝ
enable_safety_checkerᠵᡠᠯᡝ
video_urlᡴᠠᠷᠠᡴᡡ
video
ᠪᡠᠮᠪᡳᠯᡝᠰᡝ ᡝᠮᡠ ᡤᡝᠮᡠᠨᡳ https:// URL᠈ 10 MB ᡩᡝ ᡩᡝᠷᡤᡳ data: URI ᠵᡝᡵᡤᡳ ᡝᠮᡝ᠈ ᡳᠨᡝ ᠪᡠᠮᠪᡳᠯᡝᠰᡝᠨᡳ file_url ᠠᠪᠠᠮᠪᡳ ᠪᡠᠮᠪᡳ
ᠪᡠᠶᡝᠮᡝ
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "nvidia/nemotron-3-5-super/video",
"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": 322,
"usd": "0.322",
"charged": 322,
"refunded": 0
},
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
},
"output": {
"output": "Hello! How can I help?",
"reasoning": "string",
"finish_reason": "stop",
"usage": {
"input_tokens": 1,
"output_tokens": 1
}
},
"outputs": [
{
"type": "file",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"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"
}
}
ᠪᡠᠶᡝᠮᡝᠨᡳ ᡤᡳᠰᡠᠨ
- output
- stringᠵᠠᠯᡳᠨᠠᡥᠠ ᠪᡠᠶᡝᠮᡝ ᡳ ᡤᡳᠰᡠᠨ᠉
- reasoning
- string | null`think` ᡳ ᠰᠣᠩᡤᠣᡥᠠ ᡩᡝ ᠠᡩᠠᠯᡳ ᠵᠠᠯᡳᠨᠠᡥᠠ ᠮᡝᡵᡤᡝᠨ ᡳ ᠵᠠᠯᡳᠨᠠᡥᠠ ᡤᡳᠰᡠᠨ᠉
- finish_reason
- stringᠪᡠᠶᡝᠮᡝ ᠪᡠᠯᡝᡥᡝ ᡳᠨᡠ ᡤᡝᠮᡠ ᡠᠯᡳᡥᠠᠩᡤᡝ᠉
- usage
- object {input_tokens, output_tokens}ᡩᡝᠪᡝᠯᡳ ᡳ ᠵᠠᠯᡳᠨᠠᡥᠠ ᡤᡝᠮᡠᠨᡳ ᠵᠠᠯᡳᠨᠠᠮᠪᡳ᠉
ᡝᠯᡤᡳ NVIDIA Nemotron 3.5 Super ᡠᠯᡤᡳᠨ
ᡝᠯᡤᡳ ᡩᠠᠯᡳᠨ: ᡤᠠᠳᠠᠨ ᡤᡝᠯᡳᠮᡝ
AI ᠠᡤᡝᠨᡨᡳ ᡠᠴᡠᠷᡳ᠈ ᡝᡵᡝ ᡩᡝᠪᡝᡵᡳ Markdown ᠰᡳᠨᡩᠠᡵᡳ https://doitong.com/mnc/developers/models/nvidia/nemotron-3-5-super/video.md. ᡝᡵᡝ ᠮᠣᡩᡝᠯ ᠵᠠᡴᡠᠨ ᠰᡠᠯᡠᠮᡝ https://doitong.com/developers/llms.txt