NVIDIA Nemotron 3 Nano Omni is an open, efficient reasoning model for enterprise agentic workflows, built on a 30B A3B hybrid Transformer-Mamba MoE architecture. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
निष्क्रिय
{
"output": "A woman with wavy brown hair, wearing a white sweater and blue denim with a belt, is holding a black cassette in her right hand and looking at it. She is in a room with two cream-colored couches on either side, a brown table in the middle, and a lamp on it, along with two photo frames. Behind her, there are three windows with white curtains and brown drapes on the sides."
}$0.006प्रति रन·~166 / $1
Describe the video.
NVIDIA Nemotron-3 Nano Omni Video is a multimodal video-language model for understanding and analyzing video content. Provide a video URL and an English prompt, and the model generates a text response for tasks such as video description, scene understanding, event summarization, and visual question answering over time-based media.
Video understanding with natural-language prompts Ask questions about a video or request summaries, descriptions, and structured analysis in plain English.
Temporal scene analysis Understand actions, events, transitions, and visual context across time instead of from a single frame only.
Flexible response control
Adjust max_tokens, temperature, and top_p to balance response length, determinism, and creativity.
Optional system steering
Use system_prompt to guide output style, response format, or task behavior for more controlled results.
Reasoning mode options
Choose between no_think and think depending on your preferred response mode and workflow.
Production-ready API Suitable for video analysis pipelines, multimodal assistants, content review systems, and automated media understanding workflows.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | English text prompt sent to the model. |
| video_url | Yes | URL of the video to analyze. |
| system_prompt | No | Optional system prompt used to steer behavior, tone, or response style. |
| reasoning_mode | No | Reasoning mode: no_think (default) or think. |
| max_tokens | No | Maximum number of tokens to generate. Default: 1024. |
| temperature | No | Sampling temperature. Lower values are more deterministic. Default: 0.7. |
| top_p | No | Nucleus sampling probability mass. Default: 0.95. |
no_think or think depending on your workflow.max_tokens, temperature, and top_p.Describe this video in detail, including the setting, key actions, important scene changes, visible subjects, and the overall mood.
Billed by configured max_tokens.
| Max Tokens | Cost |
|---|---|
| 1000 | $0.006 |
| 1024 | $0.0061 |
| 2000 | $0.012 |
| 4000 | $0.024 |
| 8000 | $0.048 |
max_tokens value.max_tokens increases cost linearly.prompt, video_url, system_prompt, reasoning_mode, temperature, and top_p do not change pricing directly.system_prompt when you need a consistent output format, such as bullet summaries, labeled sections, or structured JSON-like responses.temperature lower when you want more stable and deterministic answers.max_tokens only when you need longer outputs, since pricing is tied to that value.prompt and video_url are required.prompt must be written in English.reasoning_mode = no_think, max_tokens = 1024, temperature = 0.7, and top_p = 0.95.max_tokens, not on other generation settings.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/nvidia/nemotron-3-nano-omni/video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Nemotron 3 Nano Omni Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"video_url": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"reasoning_mode": "no_think",
"max_tokens": 1024,
"temperature": 0.7,
"top_p": 0.95
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/nvidia/nemotron-3-nano-omni/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/nvidia/nemotron-3-nano-omni/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"video_url": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"reasoning_mode": "no_think",
"max_tokens": 1024,
"temperature": 0.7,
"top_p": 0.95
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"video_url": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"reasoning_mode": "no_think",
"max_tokens": 1024,
"temperature": 0.7,
"top_p": 0.95
}
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/nvidia/nemotron-3-nano-omni/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)Nemotron 3 Nano Omni Video is a NVIDIA model for AI inference, exposed as a REST API on WaveSpeedAI. NVIDIA Nemotron 3 Nano Omni is an open, efficient reasoning model for enterprise agentic workflows, built on a 30B A3B hybrid Transformer-Mamba MoE architecture. 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/nvidia/nvidia-nemotron-3-nano-omni-video.
Nemotron 3 Nano Omni Video starts at $0.006 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`, `enable_sync_mode`, `max_tokens`, `reasoning_mode`, `system_prompt`, `temperature`. 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/nvidia/nvidia-nemotron-3-nano-omni-video.
Median end-to-end generation time on WaveSpeedAI is around 8 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 (NVIDIA). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.