Name: Nemotron 3 Ultra API
Brand: NVIDIA

Overview

NVIDIA Nemotron 3 Ultra is a next-generation reasoning and orchestration model based on a hybrid Transformer-Mamba Mixture-of-Experts architecture with 550 billion total parameters and 55 billion active parameters per forward pass. Designed for enterprise-grade agentic workflows, it delivers strong performance on complex reasoning tasks, multi-step analysis, and long-document understanding — with support for contexts up to 1 million tokens.

Technical Specifications

Performance Benchmarks

550B total parameters, 55B active per forward pass (MoE architecture).
Context window: up to 1,000,000 tokens.
Strong performance on reasoning, coding, and instruction-following benchmarks.
Optimized for multi-step and chain-of-thought reasoning scenarios.
Suitable for both interactive chat and batch inference workloads.

Architecture Breakdown

Nemotron 3 Ultra uses a hybrid Transformer-Mamba architecture combined with Mixture-of-Experts (MoE) routing. The MoE design activates only 55B of 550B parameters per token, enabling efficient inference at scale. The Mamba layers provide linear-complexity sequence modeling for long-context tasks, while Transformer attention layers handle high-precision reasoning over complex inputs.

API Pricing

Input: $0.65 / 1M tokens
Output: $3.25 / 1M tokens
Cache Read: $0.195 / 1M tokens

Core Features & Capabilities

- Extended Context: Supports up to 1M token context for long-document analysis and retrieval.
- Complex Reasoning: Optimized for chain-of-thought, multi-step problem solving, and logical inference.
- Tool Use: Supports function calling for agentic and orchestration workflows.
- Agent Orchestration: Designed as both an orchestrator and sub-agent in multi-agent pipelines.
- Instruction Following: Strong performance on precise instruction adherence across diverse tasks.
- Code Generation: Capable of generating, reviewing, and debugging complex code across languages.
- Long-Context Summarization: Processes and summarizes large documents, codebases, and transcripts.

Comparison with Other Models

VS DeepSeek R1: Nemotron 3 Ultra offers a 1M token context window vs. DeepSeek R1's shorter context; both target advanced reasoning but Nemotron uses a hybrid MoE architecture optimized for enterprise deployment.
VS Claude Sonnet 4: Claude Sonnet 4 focuses on balanced performance and speed; Nemotron 3 Ultra prioritizes extended context and MoE-driven reasoning efficiency at scale.
VS GPT-4o: GPT-4o delivers multimodal capabilities; Nemotron 3 Ultra specializes in long-context text reasoning and agentic orchestration with a larger parameter footprint.

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Uptime and performance

Uptime (24h): 100%
Uptime (7d): 100%
Availability (24h): 100%
Error rate (24h): 0%
End-to-end latency (24h median): 10.3s
Throughput (24h median): 178.0 tok/s

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

Customer service automation is a documented use case for Nemotron 3 Ultra 550B A55B, which NVIDIA describes as ideal for multi-agent enterprise workflows requiring highest accuracy.

Supply chain management is another documented use case, listed alongside customer service automation among the model’s enterprise workflow applications.

IT security is also explicitly named as a use case for the model in NVIDIA’s Nemotron overview, again in the context of high-accuracy multi-agent enterprise workflows.

The model is also described as suitable for deep research and complex reasoning tasks, including orchestration across coding and enterprise workflows.

How to use Nemotron 3 Ultra API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to nvidia/nemotron-3-ultra-550b-a55b.

import requests

r = requests.post(
    "https://api.aimlapi.com/v1/chat/completions",
    headers={"Authorization": "Bearer " + AIMLAPI_KEY},
    json={
      "model": "nvidia/nemotron-3-ultra-550b-a55b",
      "messages": [
        {
          "role": "user",
          "content": "Hello!"
        }
      ]
    },
)
print(r.json())

const r = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "nvidia/nemotron-3-ultra-550b-a55b",
    "messages": [
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }),
});
console.log(await r.json());

curl -X POST https://api.aimlapi.com/v1/chat/completions \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"nvidia/nemotron-3-ultra-550b-a55b","messages":[{"role":"user","content":"Hello!"}]}'

OpenAI-compatible — swap the base URL and it works with your existing SDK.