Nex AGI has released Nex-N2-Pro, a massive 397B MoE model that rivals GPT-5.5 and sets a new standard for open-source agentic coding and reasoning.
On June 2, 2026, the landscape of artificial intelligence shifted fundamentally. Nex AGI has officially released Nex-N2-Pro, a milestone model that bridges the gap between proprietary closed-source giants and the open-source community. This isn't just another incremental update; it is a declaration that state-of-the-art (SOTA) reasoning and agentic capabilities are now accessible to every developer, regardless of their budget or infrastructure.
For years, developers have had to choose between the raw power of models like GPT-5.5 or Claude Opus 4.7 and the flexibility of open-weights models. Nex-N2-Pro eliminates this compromise. By delivering top-tier performance under the Apache-2.0 license, Nex AGI is empowering a new generation of autonomous agents, deep research tools, and sophisticated coding environments.
At the heart of Nex-N2-Pro lies a sophisticated Mixture of Experts (MoE) architecture. While the model boasts a staggering 397B total parameters, it utilizes a highly efficient routing mechanism that activates only 17B parameters per token. This design allows the model to possess the vast knowledge base of a massive dense model while maintaining the inference speed and efficiency required for real-time applications.
The model is post-trained on the Qwen3.5-397B-A17B base, refining its ability to handle complex multimodal inputs. Nex-N2-Pro is natively multimodal, accepting both text and image inputs to produce highly coherent text outputs. Furthermore, its context handling is industry-leading, featuring a 262K token context window and the ability to generate up to 256K output tokens, making it ideal for analyzing entire codebases or massive technical documents.
The numbers speak for themselves. Nex-N2-Pro has achieved SOTA status across several critical developer-centric benchmarks. On the SWE-Verified, SWE-Pro, and DeepSWE benchmarks, it outperforms every other open-source model currently available. Its coding prowess is specifically highlighted by a score of 75.3 on Terminal-Bench 2.1, demonstrating an uncanny ability to navigate and execute complex CLI-based tasks.
Beyond simple coding, the model excels in long-horizon reasoning. It scored 1585 on the GDPval benchmark, a metric specifically designed to measure success in long-running, multi-step workflows. This performance places Nex-N2-Pro in the same league as the world's most advanced proprietary models, proving that open-source intelligence has finally caught up to the frontier.
What truly sets Nex-N2-Pro apart is its 'Agentic Thinking' capability. Unlike standard LLMs that simply predict the next token, Nex-N2-Pro operates in a unified closed loop: Comprehension → Planning → Implementation → Feedback → Debug → Iteration. This allows the model to act as a true autonomous agent, capable of using tools, executing code, observing the environment, and correcting its own mistakes without human intervention.
To optimize this process, Nex AGI implemented adaptive reasoning depth. Instead of utilizing heavy 'thinking' processes for every query, the model dynamically scales its computational effort. This intelligence reduces the number of thinking tokens by 30-50% compared to traditional 'always-on' reasoning models, significantly lowering latency and cost without sacrificing the quality of complex logical deductions.
Nex-N2-Pro is engineered for high-stakes technical environments. Its primary strength lies in agentic coding and deep research. With native integration for tools like Claude Code, Cursor, and OpenClaw, it can serve as the 'brain' for autonomous software engineers that can write, test, and deploy entire features.
For enterprises, the model is perfect for complex RAG (Retrieval-Augmented Generation) pipelines and long-context workflow automation. For individual developers, the availability of the Nex-N2-mini variant and the ability to run the Pro version locally via llama.cpp or Ollama means you can have a world-class AI assistant running entirely on your own hardware, ensuring data privacy and zero latency.
Getting started with Nex-N2-Pro is seamless. Developers can immediately download the weights from Hugging Face or ModelScope. For those looking for serverless, high-speed access, SiliconFlow offers early access to their serverless endpoints, and the model is also available via OpenRouter for easy API integration.
Whether you are building a local agentic harness or a massive cloud-based research platform, the ecosystem is ready. We recommend starting with the Hugging Face repository to explore the model cards and technical documentation provided by Nex AGI.