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Nemotron 3 Nano Omni Vision

nvidia /

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.

image-to-text
इनपुट

निष्क्रिय

{
  "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."
}

$0.006प्रति रन·~166 / $1

आगे:

उदाहरणसभी देखें

Describe the image.

संबंधित मॉडल

README

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

ParameterRequiredDescription
promptYesEnglish text prompt sent to the model.
imageYesImage URL to analyze with the model.
system_promptNoOptional system prompt used to steer behavior, tone, or response style.
reasoning_modeNoReasoning mode: no_think (default) or think.
max_tokensNoMaximum number of tokens to generate. Default: 1024.
temperatureNoSampling temperature. Lower values are more deterministic. Default: 0.7.
top_pNoNucleus sampling probability mass. Default: 0.95.

How to Use

  1. Upload or link your image — provide the image you want the model to analyze.
  2. Write your prompt — ask the model to describe, explain, compare, classify, or answer questions about the image.
  3. Add a system prompt (optional) — guide the response style, output format, or task framing.
  4. Choose reasoning mode (optional) — use no_think or think depending on your workflow.
  5. Set generation controls (optional) — adjust max_tokens, temperature, and top_p.
  6. 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 TokensCost
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

नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है।

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. 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 Vision below.

HTTP example
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",
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "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/vision" \
  -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
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/nvidia/nemotron-3-nano-omni/vision";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "reasoning_mode": "no_think",
        "max_tokens": 1024,
        "temperature": 0.7,
        "top_p": 0.95
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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));
}
Python example
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",
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "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/vision", 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 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 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-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 long does Nemotron 3 Nano Omni Vision take to generate?

Median end-to-end generation time on WaveSpeedAI is around 6 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

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.

Nemotron 3 Nano Omni Vision | AI Image Understanding API | WaveSpeedAI