Nex-N2: China's Open-Source AI That Fights GPT-5.5

Links & Resources

• Website: nex-agi.com
• Hugging Face: Nex-N2-Pro
• GitHub: nex-agi/Nex-N2
• OpenRouter: Nex-N2-Pro (free)
• SiliconFlow: cloud.siliconflow.cn
• ModelScope: Nex-N2-Pro
• License: Apache 2.0

What Makes Nex-N2 Different

Most reasoning models overthink everything. Ask a simple question and they still burn through tokens like they're solving a math olympiad. Nex-N2 doesn't do that.

It uses something called Adaptive Thinking - the model decides on its own when to think hard and when to just answer. Simple action? Execute immediately. Complex multi-step decision? Reason thoroughly. The result: 30-50% fewer thinking tokens compared to always-on reasoning, with equal or better task completion.

This isn't just a nice-to-have. Fewer tokens = lower cost = faster responses. At scale, that's the difference between affordable and unusable.

The Architecture

Nex-N2 is a Mixture-of-Experts (MoE) model post-trained on Alibaba's Qwen3.5 base. Two variants:

Only 17B parameters fire per token on the Pro model. That's how a 397B-parameter model runs at a fraction of the cost of a dense model its size.

Context window: 262K tokens input, 256K tokens output. It accepts both text and images.

Benchmarks - The Real Numbers

Here's how Nex-N2-Pro stacks up against the top models on agentic, coding, and reasoning benchmarks:

Agentic Tasks

Nex-N2-Pro beats DeepSeek-V4-Pro on every agentic benchmark and stays within striking distance of GPT-5.5.

Coding & Software Engineering

Nex-N2-Pro beats GPT-5.5 on SWE-Bench Pro (58.8 vs 58.6), beats Opus 4.7 on Terminal-Bench (75.3 vs 69.7), and absolutely destroys DeepSeek-V4-Pro on DeepSWE (33.6 vs 8).

General Reasoning

Agentic Thinking - How It Actually Works

Nex-N2's thinking system has two parts:

Adaptive Thinking

The model autonomously decides whether to engage its chain-of-thought and how deeply. Three reasoning profiles emerge based on task type:

• Search tasks - heavy reasoning upfront to break down search strategy, then concentrated synthesis at the end
• SWE tasks - reasoning peaks during bug localization and fix verification
• Long-horizon tasks - reasoning deepens progressively, peaking at result integration

Reasoning is always concentrated where uncertainty is highest. No wasted compute on easy steps.

Coherent Thinking

Whether the model is searching, coding, or calling tools, its chain of thought follows the same structural pattern: goal decomposition → state tracking → strategy adjustment → self-verification. This consistency is what makes it reliable across mixed tasks - like a coding job that interleaves search queries and tool calls mid-stream.

How to Use Nex-N2

Option 1: API Access (Easiest)

OpenRouter (Free tier available):

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nex-agi/Nex-N2-Pro:free",
    "messages": [{"role": "user", "content": "Your prompt here"}]
  }'

SiliconFlow: Available at cloud.siliconflow.cn

Option 2: Use with Claude Code / Cursor

Nex-N2 is plug-and-play with Claude Code, Cursor, OpenClaw, and other agentic harnesses. It supports function calling out of the box and emits explicit reasoning traces. Point your tool at the OpenRouter or SiliconFlow endpoint and it works.

Option 3: Run Locally with vLLM or SGLang

Install and serve:

pip install sglang

python -m sglang.launch_server \
  --model-path nex-agi/Nex-N2-Pro \
  --host 0.0.0.0 \
  --port 30000 \
  --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3

Query it (OpenAI-compatible API):

curl -X POST "http://localhost:30000/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nex-agi/Nex-N2-Pro",
    "messages": [{"role": "user", "content": "Your prompt here"}]
  }'

Docker:

docker model run hf.co/nex-agi/Nex-N2-Pro

Option 4: Python (Transformers)

from transformers import pipeline

pipe = pipeline("text-generation", model="nex-agi/Nex-N2-Pro")
result = pipe("Explain quantum computing in simple terms")
print(result)

Recommended Sampling Parameters

temperature: 0.7
top_p: 0.95
top_k: 40
max_tokens: 64K
context: 256K

Where to Get It

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