Nemotron 3 Nano Omni Text | Fast LLM API

{ "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." }

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

Parameter	Required	Description
prompt	Yes	English text prompt sent to 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

Write your prompt — describe the task, question, or output you want the model to generate.
Add a system prompt (optional) — guide the model’s role, format, or tone.
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

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

Pricing

Billed by configured max_tokens.

Max Tokens	Cost
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

NVIDIA Nemotron-3 Nano Omni Vision — Analyze images with the same Omni model family.
NVIDIA Nemotron-3 Nano Omni Video — Analyze video content with temporal understanding.
NVIDIA Nemotron-3 Nano Omni Audio — Process and understand audio inputs for multimodal workflows.

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 URLs from data.outputs. Examples for Nemotron 3 Nano Omni Text below.

HTTP example

# Submit the prediction
curl --fail-with-body --connect-timeout 10 --max-time 60 \
  -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 '{
    "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
}'

# Wait at least 2 seconds, then poll. Safe GET requests may be retried.
curl --fail-with-body --connect-timeout 10 --max-time 30 \
  --retry 4 --retry-all-errors --retry-delay 1 \
  -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# Start at 2 seconds and increase the interval for long-running tasks.
# Stop on completed, failed, cancelled, or timeout.

Node.js example

// npm install wavespeed
const { Client } = require('wavespeed');

const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
const client = new Client(apiKey, {
  maxConnectionRetries: 5,
  retryInterval: 1.0,
});

try {
  const result = await client.run("nvidia/nemotron-3-nano-omni/text", {
        "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
}, {
    timeout: 3600,
    pollInterval: 2.0,
  });
  console.log(result.outputs);
} catch (error) {
  console.error('Generation failed:', error);
  process.exitCode = 1;
}

Python example

# pip install wavespeed
import os
from wavespeed import Client

client = Client(
    api_key=os.environ["WAVESPEED_API_KEY"],
    max_connection_retries=5,
    retry_interval=1.0,
)

try:
    output = client.run(
        "nvidia/nemotron-3-nano-omni/text",
        {
    "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
},
        timeout=3600.0,
        poll_interval=2.0,
    )
    print(output["outputs"])
except Exception as error:
    raise SystemExit(f"Generation failed: {error}") from error

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 do I get started with the Nemotron 3 Nano Omni Text 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 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.

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