Nemotron 3 Ultra

Overview

Nemotron 3 Ultra stands as Nvidia's flagship reasoning engine, engineered for complex problem-solving across extensive contexts. With 550 billion parameters and a massive 262,144-token context window, this model excels at multi-step logical deduction and long-document analysis. Developers integrating via LLM Resayil gain immediate access to enterprise-grade inference optimized for high-stakes applications. The architecture supports advanced chain-of-thought processes, making it ideal for research pipelines requiring precise Arabic and English bilingual performance without latency compromises.

Deployment is streamlined for production environments, requiring an enterprise tier subscription to unlock full capacity. While the credit multiplier is set at 8x relative to base rates, the return on investment justifies the cost through superior accuracy in technical and linguistic tasks. This model is production-ready out of the box, eliminating the need for extensive fine-tuning before deployment. Teams can initiate API calls immediately using standard endpoints, ensuring seamless integration into existing workflows while maintaining strict compliance and security standards required by large organizations.

Specifications

Display Name Nemotron 3 Ultra

Family Nvidia

Category Thinking

Parameters 550B

Context Window 262,144 tokens

License OTHER

Min Tier Enterprise

Status Available

Pricing

8×

credits per token

1K 8,000 Credits

10K 80,000 Credits

100K 800,000 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-ultra",
    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-ultra",
    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-ultra",
            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-ultra",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing extensive legal documents for compliance checks

Refactoring large-scale enterprise codebases for optimization

Complex multi-step reasoning for scientific research problems

Generating high-quality synthetic data for model training

Summarizing lengthy technical manuals and specifications accurately

In-Depth Guide

Full Guide

Complete Guide to Nemotron 3 Ultra — LLM Resayil