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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:
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.
In keeping with our commitment to open source, we are releasing both Nex-N2-Pro and Nex-N2-mini as open-source models starting today.
We welcome developers and enterprises to integrate and try Nex-N2 and share their feedback.
We evaluate Nex-N2 in real agentic workflows along three directions — agentic tasks, coding tasks, and general tasks — covering benchmarks across tool calling, search-based decision-making, software engineering, and terminal execution. Nex-N2-Pro delivers strong performance that keeps pace with top-tier models such as GPT-5.5 and Opus 4.7: it excels at coding (e.g., 75.3 on Terminal-Bench 2.1) and long-horizon tasks (1585 on GDPval), and shows especially strong generalization and competitiveness on newer benchmarks like SWE-Atlas and DeepSWE. On general capability and core reasoning, it stands on par with leading frontier models.
Nex-N2 ships in two variants, both post-trained on the Qwen3.5 series: Nex-N2-Pro (built on Qwen3.5-397B-A17B) and Nex-N2-mini (built on Qwen3.5-35B-A3B-Base), covering different latency and quality trade-offs. The table below reports their scores alongside leading proprietary and open models across our full evaluation suite.
Note: For the best performance with Nex-series models, we recommend serving them with our customized
sglangfork.
First, install our sglang fork:
Launch the server (example on two 8× H100 servers with CUDA 13.0):
Launch the server (example on one 2× H100 server with CUDA 13.0):
We also provide a prebuilt Docker image with our customized sglang fork preinstalled: nexagi/sglang:v0.5.12. The launch command is the same as above.
Single node with 2× H100:
For the best generation quality, we recommend the following sampling parameters:
temperature: 0.7top_p: 0.95top_k: 40Nex-series models support robust function-calling capabilities. To enable function calling, add the --tool-call-parser qwen3_coder flag when launching the server:
Nex-series models emit explicit reasoning traces. Add the --reasoning-parser qwen3 flag to parse the reasoning content separately from the final response. It can be combined with the function-calling parser above:
# Use the customized `sglang` forkgit clone https://github.com/nex-agi/sglang.gitcd sglang
# Install the python packagespip install --upgrade pippip install -e "python"# Multi-node (2 nodes). Run the same command on every node with:# <node-rank> = 0 on the head node, 1 on the other node# <node0-ip> = IP of the head node (reachable from all others)python -m sglang.launch_server \ --model-path /path/to/your/model \ --tp 16 \ --nnodes 2 \ --node-rank <node-rank> \ --dist-init-addr <node0-ip>:20000 \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coder \ --mamba-scheduler-strategy extra_bufferpython -m sglang.launch_server \ --model-path /path/to/your/model \ --tp 2 \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coder \ --mamba-scheduler-strategy extra_buffer# Multi-node (2 nodes). Run the same command on every node with:# <node-rank> = 0 on the head node, 1 on the other node# <node0-ip> = IP of the head node (reachable from all others)docker run --gpus all --shm-size 32g --network host \ -v /path/to/your/model:/model \ nexagi/sglang:v0.5.12 \ python3 -m sglang.launch_server \ --model-path /model \ --tp 16 \ --nnodes 2 \ --node-rank <node-rank> \ --dist-init-addr <node0-ip>:20000 \ --host 0.0.0.0 --port 30000 \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coder \ --mamba-scheduler-strategy extra_bufferdocker run --gpus all --shm-size 32g --ipc=host \ -p 30000:30000 \ -v /path/to/your/model:/model \ nexagi/sglang:v0.5.12 \ python3 -m sglang.launch_server \ --model-path /model \ --tp 2 \ --host 0.0.0.0 --port 30000 \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coder \ --mamba-scheduler-strategy extra_bufferpython -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coderpython -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder --reasoning-parser qwen3