---
title: NVIDIA: Nemotron 3 Super — Agentic LLM | ModelsLab
description: Access NVIDIA: Nemotron 3 Super API to run 120B MoE model with 1M context for agentic AI. Generate efficient reasoning now.
url: https://modelslab.com/nvidia-nemotron-3-super
canonical: https://modelslab.com/nvidia-nemotron-3-super
type: website
component: Seo/ModelPage
generated_at: 2026-04-15T02:02:47.708129Z
---

Available now on ModelsLab · Language Model

NVIDIA: Nemotron 3 Super
Agentic AI Maximum Efficiency
---

[Try NVIDIA: Nemotron 3 Super](/models/open_router/nvidia-nemotron-3-super-120b-a12b) [API Documentation](https://docs.modelslab.com)

Run Nemotron 3 Super
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Hybrid MoE

### 120B Total 12B Active

Activates 12B of 120B parameters via Latent MoE for 5x throughput.

1M Context

### Persistent Agent Memory

Handles million-token workflows without goal drift in NVIDIA: Nemotron 3 Super API.

Multi-Token Prediction

### 3x Faster Inference

Predicts multiple tokens per pass with Mamba-Transformer hybrid backbone.

Examples

See what NVIDIA: Nemotron 3 Super can create
---

Copy any prompt below and try it yourself in the [playground](/models/open_router/nvidia-nemotron-3-super-120b-a12b).

Code Review

“Review this Python function for bugs and optimize for performance: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2). Suggest improvements using memoization.”

Data Analysis

“Analyze sales data trends from this CSV snippet: date,sales;2025-01,1000;2025-02,1200;2025-03,900. Forecast Q2 and identify anomalies.”

Tech Summary

“Summarize key innovations in hybrid MoE architectures for LLMs, including throughput gains and context handling up to 1M tokens.”

Workflow Plan

“Plan a multi-step agent workflow for IT ticket triage: classify issue, query database, suggest resolution, escalate if needed.”

For Developers

A few lines of code.
Agentic reasoning. One call.
---

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

- **Serverless:** scales to zero, scales to millions
- **Pay per token,** no minimums
- **Python and JavaScript SDKs,** plus REST API

[API Documentation ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())</code>
```

FAQ

Common questions about NVIDIA: Nemotron 3 Super
---

[Read the docs ](https://docs.modelslab.com)

### What is NVIDIA: Nemotron 3 Super?

### How does NVIDIA: Nemotron 3 Super API work?

### What makes nvidia nemotron 3 super model efficient?

### Is NVIDIA: Nemotron 3 Super alternative to closed models?

### What context length supports nvidia: nemotron 3 super api?

### Where to access nvidia nemotron 3 super model?

Ready to create?
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Start generating with NVIDIA: Nemotron 3 Super on ModelsLab.

[Try NVIDIA: Nemotron 3 Super](/models/open_router/nvidia-nemotron-3-super-120b-a12b) [API Documentation](https://docs.modelslab.com)

---

*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-04-15*