// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "nvidia/nemotron-3-super-120b-a12b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "nvidia/nemotron-3-super-120b-a12b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="nvidia/nemotron-3-super-120b-a12b",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "nvidia/nemotron-3-super-120b-a12b",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Nemotron 3 Super is NVIDIA's open-weight 120B-parameter hybrid Mamba-Transformer MoE model with only 12B active parameters, designed for running complex multi-agent agentic AI systems at scale. It features a 1-million-token context window to prevent goal drift across long tasks and delivers up to 5x higher throughput than its predecessor. The model excels at reasoning, coding, and tool use.
Context Window 262K
tokens
Max Output N/A
tokens
Input Cost $0.1
per million tokens
Output Cost $0.5
per million tokens
Release Date Mar 11, 2026
Model Playground
Try Nemotron 3 Super instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From NVIDIA
Nemotron 3 Nano 30B A3B
Nemotron 3 Nano 30B A3B is a 31.6B total parameter (3.2B active) hybrid Mamba-Transformer MoE model trained from scratch by NVIDIA with a 1M token context window. It offers up to 3.3x higher throughput than comparable models and supports configurable reasoning traces for both agentic and conversational tasks.
ChatNemotron Nano 12B 2 VL
Nemotron Nano 12B V2 VL is a 12.6B parameter multimodal vision-language model built on a hybrid Mamba-Transformer architecture for document intelligence and video understanding. It processes multiple images, documents, and videos while achieving leading results on OCRBench v2 with up to 2.5x higher throughput using Efficient Video Sampling.
ChatNemotron Nano 9B V2
Nemotron Nano 9B V2 is a 9B parameter hybrid Mamba-Transformer model trained from scratch by NVIDIA with a 128K context window, achieving up to 6x higher inference throughput than similar models like Qwen3-8B. It features controllable reasoning budget allowing developers to balance accuracy and response time for edge deployment.
Frequently Asked Questions
You can access Nemotron 3 Super by NVIDIA through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Nemotron 3 Super to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.1 |
| Output | $0.5 |
Nemotron 3 Super was created by NVIDIA and released on Mar 11, 2026.
Nemotron 3 Super supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Yes — the Nemotron 3 Super API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add Nemotron 3 Super to your app without worrying about API keys or setup.
Read the Docs View Tutorials