Overview

Nemotron 3 Super delivers enterprise-grade performance designed for complex reasoning and extended context handling. With a massive 128,000-token context window, this model excels at processing lengthy documents, codebases, and multi-turn conversations without losing coherence. Built on NVIDIA's advanced architecture, it offers FP16 precision for high-fidelity outputs, making it ideal for research pipelines requiring detailed analysis. Developers can integrate it immediately via our standard API endpoints, ensuring your first call happens within minutes of reading the documentation. We provide full API reference examples to accelerate your build process.

This proprietary model is production-ready, featuring robust bilingual capabilities in English and Arabic to serve diverse user bases. It operates on a starter tier with a 3.5x credit multiplier, providing transparent costing structures for scalable deployment. Whether building customer-facing chatbots or internal analysis tools, Nemotron 3 Super ensures reliable latency and accuracy. Our platform provides comprehensive benchmark data comparing performance across linguistic tasks, allowing technical leads to validate suitability before commitment. Decision makers can access clear pricing tables and Arabic support confirmation directly within the dashboard, eliminating the need for sales inquiries. The proprietary license guarantees long-term stability for commercial applications.

Specifications

Display Name Nemotron 3 Super

Family Nemotron

Category Chat

Parameters unknown

Context Window 128,000 tokens

Quantization FP16

License PROPRIETARY

Min Tier Starter

Status Available

Pricing

3.5×

credits per token

1K 3,500 Credits

10K 35,000 Credits

100K 350,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="nemotron-3-super",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

import anthropic

client = anthropic.Anthropic(
    base_url="https://llmapi.resayil.io/v1",
    api_key="YOUR_API_KEY"
)

message = client.messages.create(
    model="nemotron-3-super",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(message.content[0].text)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "nemotron-3-super",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "nemotron-3-super",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing extensive legal documents for key clauses

Enterprise customer support automation and response generation

Software code generation and debugging assistance tools

Summarizing lengthy technical reports and meeting transcripts

Extracting insights from large-scale system log files

In-Depth Guide

Full Guide

Complete Guide to Nemotron 3 Super — LLM Resayil

Nemotron 3 Super