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.
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
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:
bash
1./llama-cli -m nex-n2-pro-Q2_K-00001-of-00023.gguf \2 -p "Hello, how are you?"\3 -sys "You are a helpful AI"\4 -n 4096\5 -c 8192
