Nemotron 3 Nano Omni Vision | AI Image Understanding API

{ "output": "This is a black and white photograph capturing a bustling city street scene, likely from the mid-20th century. The image is taken from an elevated perspective, looking down a wide, straight road that recedes into the distance.\n\nThe street is filled with numerous vintage automobiles, characteristic of the 1940s or 1950s, including sedans and a few convertibles. The cars are in motion and parked along both sides of the road. On the left side, a building with a sign that appears to read \"COSCO\" is visible, and a crowd of pedestrians can be seen on the sidewalk. On the right, a prominent corner building with a clock on its facade stands out. The overall atmosphere is one of a busy, active urban environment." }

NVIDIA Nemotron-3 Nano Omni Vision

NVIDIA Nemotron-3 Nano Omni Vision is a multimodal vision-language model for image understanding and analysis. Upload an image, provide an English prompt, and the model generates a text response for tasks such as image description, visual question answering, scene understanding, and structured visual analysis.

Why Choose This?

Image understanding with natural-language prompts Ask questions about an image or request a description in plain English.
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, role, or response constraints for more controlled behavior.
Reasoning mode options Choose between no_think and think depending on your preferred response mode.
Production-ready API Suitable for image analysis workflows, multimodal assistants, automated review pipelines, and visual understanding tools.

Parameters

Parameter	Required	Description
prompt	Yes	English text prompt sent to the model.
image	Yes	Image URL to analyze with the model.
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

Upload or link your image — provide the image you want the model to analyze.
Write your prompt — ask the model to describe, explain, compare, classify, or answer questions about the image.
Add a system prompt (optional) — guide the response style, output format, 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 image in detail, including the setting, visible objects, mood, and any notable historical or architectural details.

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, image, system_prompt, reasoning_mode, temperature, and top_p do not change pricing directly.

Best Use Cases

Image description — Generate clear descriptions of scenes, objects, and visual content.
Visual question answering — Ask targeted questions about what appears in an image.
Document and screenshot analysis — Extract meaning from UI screenshots, charts, diagrams, or other visual references.
Content moderation and review workflows — Use text prompts to guide structured inspection of uploaded images.
Multimodal assistants — Add image-aware understanding to support bots, tools, and internal workflows.
Research and annotation tasks — Use guided prompts to summarize or analyze visual inputs consistently.

Pro Tips

Write prompts in English for best compatibility.
Be specific about what you want, such as description, object listing, comparison, or focused analysis.
Use system_prompt when you need a consistent format, such as bullet summaries, JSON-style output, or domain-specific tone.
Keep temperature lower when you want more stable and deterministic responses.
Increase max_tokens only when you need longer outputs, since pricing is tied to that value.
Use top_p and temperature together carefully to balance diversity and control.

Notes

Both prompt and image are required.
prompt is English only.
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 Video — Analyze and understand video content 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 Vision API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/nvidia/nemotron-3-nano-omni/vision 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 Vision below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/nvidia/nemotron-3-nano-omni/vision" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://example.com/your-input.jpg",
    "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/vision", {
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://example.com/your-input.jpg",
        "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/vision",
    {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://example.com/your-input.jpg",
    "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 Vision API — Frequently asked questions

What is the Nemotron 3 Nano Omni Vision API?

Nemotron 3 Nano Omni Vision 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 Vision 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-vision.

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

Nemotron 3 Nano Omni Vision 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 Vision accept?

Key inputs: `prompt`, `image`, `enable_sync_mode`, `max_tokens`, `reasoning_mode`, `system_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/nvidia/nvidia-nemotron-3-nano-omni-vision.

How do I get started with the Nemotron 3 Nano Omni Vision 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 Vision 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 Vision

Why Choose This?

Parameters

How to Use

Example Prompt

Pricing

Billing Rules

Best Use Cases

Pro Tips

Notes

Related Models

Nemotron 3 Nano Omni Vision API — Quick start

Nemotron 3 Nano Omni Vision API — Frequently asked questions