Overview

Nemotron 3 Super delivers enterprise-grade performance designed for complex reasoning and extended context understanding. With a massive 128,000 token context window, this model excels at processing lengthy documents and maintaining coherence across multi-turn conversations. Built on FP16 quantization, it ensures high precision output suitable for demanding technical workflows. Developers can integrate this proprietary model immediately through our standardized API endpoints, requiring minimal configuration to start building production-ready applications. The architecture supports robust chat capabilities, making it an ideal choice for sophisticated assistants and data analysis pipelines where accuracy is paramount.

Operational efficiency is streamlined with transparent pricing structures based on a 3.5x credit multiplier relative to base rates, allowing for accurate cost forecasting during scaling. This tier is accessible from the starter level, ensuring teams can validate performance before full deployment. The model is production-ready, offering the stability required for critical business operations while supporting diverse linguistic tasks including Arabic. Our platform provides immediate access to billing in major regional currencies, removing friction for international teams. Whether optimizing for research benchmarks or commercial deployment, Nemotron 3 Super balances advanced capability with predictable operational costs.

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

Enterprise customer support automation and routing

Analyzing extensive legal contracts for compliance issues

Complex software code generation and debugging

Technical knowledge base querying and retrieval

Multi-turn conversational agent for enterprise workflows

In-Depth Guide

Full Guide

Complete Guide to Nemotron 3 Super — LLM Resayil

Nemotron 3 Super