Agentic AI systems need models with the specialized depth to solve dense technical problems autonomously. They must excel at reasoning , coding, and long-context analysis, while remaining efficient enough to run continuously at scale.  Multi-agent systems generate up to 15x the tokens of standard chats, re-sending history, tool outputs, and reasoning steps at every turn. Over long tasks, this “context explosion” causes goal drift, where agents gradually lose alignment with the original objective. And using massive reasoning models for every sub-task—the “thinking tax”—makes multi-agent applications too expensive and sluggish for practical use. Today, we are releasing Nemotron 3 Super to address these limitations. The new Super model is a 120B total, 12B active-parameter model that delivers maximum compute efficiency and accuracy for complex multi-agent applications such as software development and cybersecurity triaging. This model follows the introduction of Nemotron 3 Nano in December.…