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Nemotron 3 Nano Omni Text

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.

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En attente

{
  "output": "Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to imitate how humans learn, gradually improving its accuracy."
}

$0.01par exécution·~100 / $1

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Describe what is machine learning?

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README

NVIDIA Nemotron-3 Nano Omni Text

NVIDIA Nemotron-3 Nano Omni Text is a lightweight text-generation model for prompt-based language understanding and response generation. Provide an English prompt, and the model can generate answers, summaries, structured outputs, explanations, and other text-based responses with controllable length and sampling behavior.

Why Choose This?

  • Fast text generation Generate responses quickly for chat, automation, summarization, and general language tasks.

  • 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 tone, structure, formatting, or task behavior for more controlled outputs.

  • Reasoning mode options Choose between no_think and think depending on your preferred response mode and workflow.

  • Production-ready API Suitable for assistants, content tools, automation pipelines, internal workflows, and structured text generation tasks.

Parameters

ParameterRequiredDescription
promptYesEnglish text prompt sent to 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. Write your prompt — describe the task, question, or output you want the model to generate.
  2. Add a system prompt (optional) — guide the model’s role, format, or tone.
  3. Choose reasoning mode (optional) — use no_think or think depending on your workflow.
  4. Set generation controls (optional) — adjust max_tokens, temperature, and top_p.
  5. Submit — run the model and review the generated response.

Example Prompt

Summarize the following product requirements into a concise executive brief with key goals, risks, and next steps.

Pricing

Billed by configured max_tokens.

Max TokensCost
1000$0.01
1024$0.01024
2000$0.02
4000$0.04
8000$0.08

Billing Rules

  • Pricing is based on the configured max_tokens value.
  • Cost is $0.01 per 1,000 max tokens.
  • Increasing max_tokens increases cost linearly.
  • prompt, system_prompt, reasoning_mode, temperature, and top_p do not change pricing directly.

Best Use Cases

  • Question answering — Generate direct answers to prompts and tasks.
  • Summarization — Condense long text into concise takeaways or structured briefs.
  • Content drafting — Produce outlines, rewrites, explanations, and short-form written content.
  • Structured generation — Generate bullet points, labeled sections, or formatted outputs with system guidance.
  • Internal automation — Support workflow tools, copilots, and prompt-driven backend tasks.
  • General language tasks — Handle classification, transformation, extraction, and text reasoning workflows.

Pro Tips

  • Write prompts in English for best compatibility.
  • Be explicit about the desired output format, such as summary, bullets, JSON-style structure, or step-by-step explanation.
  • Use system_prompt when you need consistent tone, role behavior, or formatting rules.
  • Keep temperature lower when you want more stable and deterministic results.
  • Increase max_tokens only when you need longer outputs, since pricing is tied to that value.
  • Use top_p and temperature carefully together to balance creativity and control.

Notes

  • prompt is the only required field.
  • 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

Remarque :Ce site utilise des modèles d'IA fournis par des tiers.

Nemotron 3 Nano Omni Text API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/nvidia/nemotron-3-nano-omni/text 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 Text 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",
    "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/text" \
  -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/text";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "reasoning_mode": "no_think",
        "max_tokens": 1024,
        "temperature": 0.7,
        "top_p": 0.95
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "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/text", 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 Text API — Frequently asked questions

What is the Nemotron 3 Nano Omni Text API?

Nemotron 3 Nano Omni Text 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 Text 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-text.

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

Nemotron 3 Nano Omni Text starts at $0.010 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 Text 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-text.

How long does Nemotron 3 Nano Omni Text take to generate?

Median end-to-end generation time on WaveSpeedAI is around 4 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 Text 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 Text | Fast LLM API | WaveSpeedAI