MiniMax M2.7 vs Nemotron 3 Super (free)

MiniMax-M2.7 is a next-generation large language model built for autonomous, real-world productivity and continuous improvement. Designed to take an active role in its own development, M2.7 incorporates advanced agent capabilities through multi-agent collaboration, allowing it to plan, execute, and improve complex tasks across dynamic environments. Trained for production-level performance, M2.7 supports workflows such as live debugging, root cause analysis, financial modeling, and full document creation across Word, Excel, and PowerPoint. It delivers strong benchmark results, including 56.2% on SWE-Pro and 57.0% on Terminal Bench 2, while reaching 1495 ELO on GDPval-AA, setting a new benchmark for multi-agent systems in real-world digital workflows.

NVIDIA Nemotron 3 Super is an open hybrid MoE model with 120B parameters, using only 12B active parameters to achieve high computational efficiency and strong accuracy in complex multi-agent scenarios. Based on a hybrid Mamba-Transformer Mixture-of-Experts architecture with multi-token prediction (MTP), it offers more than 50% faster token generation than leading open models. The model includes a 1M-token context window, enabling long-term agent consistency, cross-document reasoning, and multi-step task planning. Latent MoE makes it possible to engage 4 experts at the inference cost of just one, enhancing both intelligence and generalization. Reinforcement learning across more than 10 environments provides top-tier benchmark performance, including AIME 2025, TerminalBench, and SWE-Bench Verified. Released fully open with weights, datasets, and recipes under the NVIDIA Open License, Nemotron 3 Super supports simple customization and secure deployment in any environment — from local workstations to the cloud.