Skip to content
No models found
OpenRouter
© 2026 OpenRouter, Inc

Product

  • Chat
  • Rankings
  • Apps
  • Models
  • Providers
  • Pricing
  • Enterprise
  • Labs

Company

  • About
  • Announcements
  • CareersHiring
  • Privacy
  • Terms of Service
  • Support
  • State of AI
  • Works With OR
  • Data

Developer

  • Documentation
  • API Reference
  • SDK
  • Status

Connect

  • Discord
  • GitHub
  • LinkedIn
  • X
  • YouTube
Favicon for nvidia

NVIDIA: Nemotron 3 Super (free)

nvidia/nemotron-3-super-120b-a12b:free

Compare

NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid MoE model, activating just 12B parameters for maximum compute efficiency and accuracy in complex multi-agent applications. Built on a hybrid Mamba-Transformer Mixture-of-Experts architecture with multi-token prediction (MTP), it delivers over 50% higher token generation compared to leading open models.

The model features a 1M token context window for long-term agent coherence, cross-document reasoning, and multi-step task planning. Latent MoE enables calling 4 experts for the inference cost of only one, improving intelligence and generalization. Multi-environment RL training across 10+ environments delivers leading accuracy on benchmarks including AIME 2025, TerminalBench, and SWE-Bench Verified.

Fully open with weights, datasets, and recipes under the NVIDIA Open License, Nemotron 3 Super allows easy customization and secure deployment anywhere — from workstation to cloud.

Modalities

Price

Free

Context

1M

Weekly Tokens

719B

Released

Mar 11, 2026

Overview
Providers
Performance
Pricing
Benchmarks
Apps
Activity
Uptime
API

Sample code and API for Nemotron 3 Super (free)

OpenRouter normalizes requests and responses across providers for you.

1

Get your API key

Create an API key from your OpenRouter dashboard and set it as an environment variable:

2

Make your first request

Use nvidia/nemotron-3-super-120b-a12b:free with the OpenRouter API:

OpenRouter supports reasoning-enabled models that can show their step-by-step thinking process. Use the reasoning parameter in your request to enable reasoning, and access the reasoning_details array in the response to see the model's internal reasoning before the final answer. When continuing a conversation, preserve the complete reasoning_details when passing messages back to the model so it can continue reasoning from where it left off. Learn more about reasoning tokens.

In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

Using third-party SDKs

For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

3

Enable streaming

Add "stream": true to your request body to receive responses as server-sent events:

Endpoint

POSThttps://openrouter.ai/api/v1/chat/completions
AuthorizationBearer $OPENROUTER_API_KEY
Content-Typeapplication/json
HTTP-Refereroptional — your site URL, for rankings
X-Titleoptional — your site name, for rankings
Modelnvidia/nemotron-3-super-120b-a12b:free

Parameters

NameTypeDefaultDescription
reasoningmap—Controls reasoning behavior for models that support thinking tokens, including whether reasoning is enabled, the reasoning effort, maximum reasoning tokens, and whether reasoning is excluded from the response.
include_reasoningboolean—Deprecated alias for reasoning.exclude.
temperaturefloat1This setting influences the variety in the model's responses.
max_tokensinteger—This sets the upper limit for the number of tokens the model can generate in response.
seedinteger—If specified, the inferencing will sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
top_pfloat1This setting limits the model's choices to a percentage of likely tokens: only the top tokens whose probabilities add up to P.
toolsarray—Tool calling parameter, following OpenAI's tool calling request shape.
tool_choicestring or object—Controls which (if any) tool is called by the model.
structured_outputsboolean—If the model can return structured outputs using response_format json_schema.
response_formatmap—Forces the model to produce specific output format.