Open Source
In keeping with our commitment to open source, we are releasing Nex-N2-Pro starting today. Nex-N2-mini is not open-sourced at this time and will be released in a future update.
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.
Table with columns: Benchmark, Nex-N2-mini, Nex-N2-Pro, GPT-5.5, Opus 4.7, Kimi-K2.6, GLM-5.1, MiniMax M3, DeepSeek-V4-Pro| 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 |
Usage
Local Deployment
We recommend sglang for serving Nex-series models locally:
python -m sglang.launch_server --model-path /path/to/your/model
Recommended Sampling Parameters
For the best generation quality, we recommend the following sampling parameters:
temperature: 0.7top_p: 0.95top_k: 40
Function Calling
Nex-series models support robust function-calling capabilities. To enable function calling, add the --tool-call-parser qwen3_coder flag when launching the server:
python -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder
Reasoning Parser
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:
python -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder --reasoning-parser qwen3