Overview

nemotron 3 ultra stands as a flagship thinking model within the Nvidia family, engineered for complex reasoning and extended context processing. With 550 billion parameters and a massive 262,144 token context window, this architecture handles intricate codebases and long-document analysis without losing coherence. Developers can integrate this model immediately through our standardized API endpoints, enabling first calls within minutes of signup. The 2x credit multiplier reflects its advanced computational depth, ensuring you pay only for the enhanced reasoning capabilities required for production-grade applications.

For researchers and enterprise leaders, performance transparency is critical. This model delivers state-of-the-art results across bilingual tasks, demonstrating superior proficiency in Arabic and English compared to alternatives in its class. We provide clear pricing structures accessible directly from the dashboard, eliminating the need for sales consultations to understand costs in your preferred currency. Whether validating benchmarks or deploying customer-facing agents, nemotron 3 ultra offers the stability and linguistic nuance necessary for high-stakes environments. Detailed benchmark comparisons are available within the documentation to support your technical validation process. Our starter tier access ensures you can evaluate these capabilities immediately while scaling confidently as your workload grows.

Specifications

Display Name nemotron 3 ultra

Family Nvidia

Category Thinking

Parameters 550B

Context Window 262,144 tokens

Min Tier Starter

Status Available

Pricing

2×

credits per token

1K 2,000 Credits

10K 20,000 Credits

100K 200,000 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 contracts for compliance risks and clauses

Debugging and refactoring large legacy codebases across multiple files

Generating synthetic data for training smaller specialized AI models

Summarizing lengthy technical reports and extracting key action items

Complex reasoning tasks for scientific research and data analysis

In-Depth Guide

Full Guide

Complete Guide to nemotron 3 ultra — LLM Resayil

nemotron 3 ultra