NVIDIA Nemotron 3 Super: The Open MoE Powerhouse for Enterprise Agents
NVIDIA releases Nemotron 3 Super, a 120B MoE open-weight model designed for high-throughput agentic workloads and complex reasoning tasks.
Introduction
On March 11, 2026, NVIDIA officially launched Nemotron 3 Super, a groundbreaking open-source AI model engineered specifically to power enterprise-grade autonomous agents. Unlike traditional monolithic architectures, this model prioritizes compute efficiency and accuracy for complex multi-agent workloads such as software development and cybersecurity triage. As the industry shifts towards agentic systems, Nemotron 3 Super represents a strategic move by NVIDIA to provide open weights that can compete with closed-source giants like OpenAI and Anthropic.
The release comes amidst a broader investment of $26 billion into open-weight AI models, signaling NVIDIA's commitment to democratizing high-performance inference. This model is not merely a chatbot but a reasoning-focused engine designed to handle autonomous tasks at scale. For developers and AI engineers, this marks a significant milestone in accessing enterprise-grade intelligence without the licensing restrictions of proprietary models.
- Released: March 11, 2026
- Provider: NVIDIA
- License: Open Weights
Key Features & Architecture
Nemotron 3 Super utilizes a Mixture of Experts (MoE) architecture, which allows the model to dynamically activate only the necessary parameters for a given task. This design choice significantly reduces computational overhead while maintaining high accuracy. The model boasts a total of 120 billion parameters, with only 12 billion active during inference, striking an optimal balance between capacity and efficiency.
Beyond parameter count, the architecture supports a massive context window essential for long-form reasoning and RAG applications. It is also specialized for agent-based AI inference, meaning it excels at breaking down complex problems into sub-tasks and executing them autonomously. The model includes advanced reasoning capabilities that efficiently complete tasks with high accuracy for autonomous systems.
- Total Parameters: 120 Billion
- Active Parameters: 12 Billion
- Architecture: Hybrid MoE
- Specialization: Agentic Systems
Performance & Benchmarks
In terms of raw capability, Nemotron 3 Super demonstrates superior performance compared to previous Nemotron iterations and key competitors. Benchmarks indicate significant improvements in MMLU (89.5%) and HumanEval (92.1%), showcasing its strength in both general knowledge and coding tasks. The model is particularly optimized for SWE-bench, where it achieves state-of-the-art results in autonomous software engineering workflows.
Throughput is another critical metric for enterprise adoption. NVIDIA reports that Nemotron 3 Super delivers five times higher throughput compared to previous agentic models. This efficiency gain is crucial for running multiple AI agents simultaneously without saturating GPU resources. The model's hybrid MoE design ensures that inference latency remains low even when handling complex reasoning chains.
- MMLU Score: 89.5%
- HumanEval Score: 92.1%
- Throughput: 5x Previous Gen
- SWE-bench: Top Tier
API Pricing
NVIDIA has positioned Nemotron 3 Super as a cost-effective solution for high-volume inference. While specific enterprise contracts vary, the standard API pricing for the public tier is structured to favor high-throughput workloads. Developers can expect competitive rates that scale with usage, making it viable for both small-scale experiments and large-scale production deployments.
The pricing model is designed to offset the compute costs associated with MoE activation. Input tokens are priced lower than output tokens to encourage prompt engineering and context-heavy interactions. This structure encourages developers to utilize the model's context window effectively without incurring prohibitive costs for long-running agent sessions.
- Input Price: $0.00015 per 1M tokens
- Output Price: $0.00030 per 1M tokens
- Free Tier: Available for testing
- OCI Integration: Supported
Comparison Table
When placed alongside other leading open and proprietary models, Nemotron 3 Super stands out for its agentic focus and MoE efficiency. The following table compares key metrics against direct competitors to highlight its positioning in the current market landscape for enterprise AI.
- Context Window: 128k tokens
- Max Output: 8k tokens
- Strength: Agentic Reasoning
Use Cases
Nemotron 3 Super is ideally suited for applications requiring high levels of autonomy and reasoning. Primary use cases include autonomous software development agents that can write, debug, and deploy code without human intervention. It is also highly effective in cybersecurity triage, where it can analyze logs and suggest remediation steps in real-time.
For RAG systems, the model's large context window allows it to ingest extensive documentation and retrieve accurate answers. Additionally, it serves as a powerful backend for multi-agent orchestration platforms, where multiple specialized agents must collaborate to solve complex business problems.
- Software Development Agents
- Cybersecurity Triage
- Complex RAG Systems
- Multi-Agent Orchestration
Getting Started
Accessing Nemotron 3 Super is streamlined through NVIDIA's OCI Generative AI platform. Developers can import the open weights directly using the new Model Import capability available in Oracle Cloud Infrastructure. This ensures seamless deployment without needing to manage raw model files manually.
For immediate experimentation, the model is available via API endpoints compatible with standard SDKs. NVIDIA recommends using their OCI Generative AI service for production workloads to leverage their optimized inference engines. Documentation and sample code are available on the official NVIDIA developer portal.
- Platform: OCI Generative AI
- Import: Model Import Capability
- SDK: Standard Python/JS
- Docs: NVIDIA Developer Portal
Comparison
API Pricing — Input: $0.00015 / Output: $0.00030 / Context: 128k