---

license: apache-2.0

base_model:

• nex-agi/Nex-N2-Pro

pipeline_tag: text-generation

library_name: transformers

tags:

• qwen3_5_moe
• image-text-to-text
• conversational
• gguf
• moe
• agent

---

Disclaimer:

If you find that the Nex N2 model cannot be loaded, llama.cpp is attempting to locate a non-existent block 60 (MTP) layer. I am currently running tests and seeking a donor.

• Q2_K : Already injected✅
• BF16 : ❌

Nex-N2-Pro-GGUF

Overview

This repository contains the GGUF quantized files for nex-agi/Nex-N2-Pro.

• Original Model: nex-agi/Nex-N2-Pro
• Architecture: Qwen3.5-397B-A17B
• License: Apache 2.0

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<div align="left">

<img src="./figures/NEX_logo.svg" width="20%"/>

</div>

An agentic model with Agentic Thinking.

Today, we are officially releasing and open-sourcing our next-generation model, Nex-N2 — an agent model built for real-world productivity scenarios. With first-tier coding and agentic capabilities, Nex-N2 keeps driving complex, long-horizon tasks forward in real environments to deliver stable, end-to-end results.

Over the past year, a paradigm shift led by Vibe Coding and Harness Engineering has been redefining the limits of LLM agents. From dialogue, to reasoning, to agents that execute long-horizon tasks with environmental feedback, the tasks models must handle keep growing harder, the contexts longer, and the environments more realistic. The core of next-generation model competition is no longer whether a model can think, but whether it can reliably and efficiently turn thinking into actions that are executable, verifiable, and iterable.

Rather than treating reasoning, tool use, and environment execution as separate capabilities, Nex-N2 unifies them through an Agentic Thinking framework that connects requirement understanding, task planning, code implementation, environmental feedback, evaluation and debugging, and continuous iteration into a single closed loop. The framework has two parts:

• Adaptive Thinking lets the model decide on its own when to think and how deeply — executing simple actions quickly while reasoning thoroughly on critical decisions.
• Coherent Thinking carries one consistent reasoning paradigm across general reasoning and diverse agentic tasks, staying consistent across tasks and modalities to enable stable capability transfer.

Across real agentic workflows — agentic coding, deep research, tool calling, and terminal execution — Nex-N2 reaches first-tier performance, with substantial gains over the previous-generation Nex-N1 on multiple authoritative benchmarks. In real productivity scenarios such as OpenClaw one-person-company workflows, end-to-end game development, and web and multimodal generation, it likewise demonstrates outstanding usability, robustness, and stability.

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Performance

| Benchmark | Nex-N2-mini | Nex-N2-Pro | GPT-5.5 | Opus 4.7 | Kimi-K2.6 | GLM-5.1 | MiniMax M3 | DeepSeek-V4-Pro |

| --- | --- | --- | --- | --- | --- | --- | --- | --- |

| Agent | | | | | | | | |

| BrowseComp | 74.1 | 83.7 | 84.4 | 79.8 | 83.2 | 79.3 | 83.5 | 83.4 |

| GDPval | 1402 | 1585 | 1769 | 1753 | 1481 | 1535 | - | 1554 |

| Toolathlon | 33.3 | 51.9 | 55.6 | 52.8 | 50.0 | 40.7 | - | 51.8 |

| WildClawBench | 47.7 | 53.5 | 58.2 | 62.2 | - | 48.2 | - | 43.7 |

| WideSearch | 62.0 | 75.6 | - | - | 80.8 | - | - | - |

| TAU3 | 65.9 | 71.1 | - | - | - | 70.6 | - | - |

| Coding & SWE | | | | | | | | |

| SWE-Bench Pro | 50.2 | 58.8 | 58.6 | 64.3 | 58.6 | 58.4 | 59.0 | 55.4 |

| Terminal-Bench 2.1 | 60.7 | 75.3 | 83.4 | 69.7 | - | 58.7 | 66.0 | 72.0 |

| DeepSWE | 8.0 | 33.6 | 70 | 54 | 24 | 18 | - | 8 |

| SWE-Bench Verified | 74.4 | 80.8 | 82.9 | 87.6 | 80.2 | - | 80.5 | 80.6 |

| SWE Atlas QnA | 31.5 | 37.9 | 45.4 | 45.2 | - | - | 37.9 | - |

| SWE Atlas RF | 30.0 | 32.9 | 44.8 | 48.6 | - | - | - | - |

| SWE Atlas TW | 23.3 | 40.0 | 42.6 | 38.2 | - | - | 30.8 | - |

| General & Reasoning | | | | | | | | |

| GPQA Diamond | 82.6 | 90.7 | 93.6 | 94.2 | 90.5 | 86.2 | - | 90.1 |

| IFEval | 89.1 | 94.0 | - | - | 94.5 | 94.5 | - | 91.9 |

| Apex | 9.4 | 36.5 | - | - | 24.0 | 11.5 | - | 38.3 |

!Nex-N2 Benchmark Overview

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How to Use

These GGUF files are fully compatible with llama.cpp and popular graphical interfaces like LM Studio, Ollama.

Example using llama.cpp CLI:

./llama-cli -m nex-n2-pro-Q2_K-00001-of-00023.gguf \
  -p "Hello, how are you?" \
  -sys "You are a helpful AI" \
  -n 4096 \
  -c 8192