Nemotron 3 Nano Omni Video | AI Video Understanding API

{ "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." }

NVIDIA Nemotron-3 Nano Omni 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.

Why Choose This?

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.

Parameters

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`.

How to Use

Provide your video URL — upload or link the video you want the model to analyze.
Write your prompt — ask the model to describe, summarize, explain, compare, classify, or answer questions about the video.
Add a system prompt (optional) — guide the output style, response structure, or task framing.
Choose reasoning mode (optional) — use no_think or think depending on your workflow.
Set generation controls (optional) — adjust max_tokens, temperature, and top_p.
Submit — run the model and review the generated response.

Example Prompt

Describe this video in detail, including the setting, key actions, important scene changes, visible subjects, and the overall mood.

Pricing

Billed by configured max_tokens.

Max Tokens	Cost
1000	$0.006
1024	$0.0061
2000	$0.012
4000	$0.024
8000	$0.048

Billing Rules

Pricing is based on the configured max_tokens value.
Cost is $0.006 per 1,000 max tokens.
Increasing max_tokens increases cost linearly.
prompt, video_url, system_prompt, reasoning_mode, temperature, and top_p do not change pricing directly.

Best Use Cases

Video summarization — Generate concise or detailed summaries of video content.
Scene and event understanding — Identify key actions, transitions, and important moments over time.
Video question answering — Ask targeted questions about what happens in the video.
Content review workflows — Inspect uploaded videos for structured analysis, moderation, or categorization tasks.
Multimodal assistants — Add video-aware understanding to internal tools, bots, and applications.
Research and annotation tasks — Use guided prompts to consistently analyze and label video content.

Pro Tips

Write prompts in English for best compatibility.
Be specific about the task, such as summarization, scene breakdown, action recognition, or focused question answering.
Use system_prompt when you need a consistent output format, such as bullet summaries, labeled sections, or structured JSON-like responses.
Keep temperature lower when you want more stable and deterministic answers.
Increase max_tokens only when you need longer outputs, since pricing is tied to that value.
Ask focused temporal questions like “what happens before and after” or “summarize the sequence of events” for better video-specific results.

Notes

Both prompt and video_url are required.
prompt must be written in English.
Default settings include reasoning_mode = no_think, max_tokens = 1024, temperature = 0.7, and top_p = 0.95.
Pricing depends on configured max_tokens, not on other generation settings.

Related Models

NVIDIA Nemotron-3 Nano Omni Vision — Analyze images with the same Omni model family.
NVIDIA Nemotron-3 Nano Omni Audio — Process and understand audio inputs for multimodal workflows.
NVIDIA Nemotron-3 Nano Omni Text — Use the text-only variant for language generation and text-based tasks.

Nemotron 3 Nano Omni Video API — Quick start

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; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Nemotron 3 Nano Omni Video below.

HTTP example

# Submit the prediction
curl -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 '{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "reasoning_mode": "no_think",
    "max_tokens": 1024,
    "temperature": 0.7,
    "top_p": 0.95,
    "enable_sync_mode": false
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("nvidia/nemotron-3-nano-omni/video", {
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "reasoning_mode": "no_think",
        "max_tokens": 1024,
        "temperature": 0.7,
        "top_p": 0.95,
        "enable_sync_mode": false
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "nvidia/nemotron-3-nano-omni/video",
    {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "reasoning_mode": "no_think",
    "max_tokens": 1024,
    "temperature": 0.7,
    "top_p": 0.95,
    "enable_sync_mode": false
}
)

print(output["outputs"][0])  # → URL of the generated output

Nemotron 3 Nano Omni Video API — Frequently asked questions

What is the Nemotron 3 Nano Omni Video API?

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.

How do I call the Nemotron 3 Nano Omni Video API?

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 prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/nvidia/nvidia-nemotron-3-nano-omni-video.

How much does Nemotron 3 Nano Omni Video cost per run?

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.

What inputs does Nemotron 3 Nano Omni Video accept?

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.

How do I get started with the Nemotron 3 Nano Omni Video API?

Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.

Can I use Nemotron 3 Nano Omni Video outputs commercially?

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.

ExamplesView all

Related Models

README

NVIDIA Nemotron-3 Nano Omni Video

Why Choose This?

Parameters

How to Use

Example Prompt

Pricing

Billing Rules

Best Use Cases

Pro Tips

Notes

Related Models

Nemotron 3 Nano Omni Video API — Quick start

Nemotron 3 Nano Omni Video API — Frequently asked questions