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auryn-macmillan
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Model weights for the Nex-N2.5 family will be released as open source, alongside hosted online services.
We welcome developers and enterprises to integrate and try Nex-N2.5 and share their feedback.
We evaluate Nex-N2.5 across coding, agentic workflows, computer use, and multimodal understanding.
The tables below compare Nex-N2.5-mini, Nex-N2.5-Pro, and Nex-N2.5-Max with leading models across our evaluation suite.1, 2 Bold marks the best result in each benchmark, including ties; — indicates unavailable data.10
We also provide a prebuilt Docker image with our customized sglang fork preinstalled: nexagi/sglang:v0.5.18-nex-patch. The launch command is the same as above.
bash
# Multi-node (2 nodes, 16 x H200). 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:v0.5.18-nex-patch \python3 -m sglang.launch_server \--model-path /path/to/your/model \--trust-remote-code \--host 0.0.0.0 \--port 8000 \--nnodes 2 \--node-rank "${NODE_RANK}" \--dist-init-addr "${MASTER_ADDR}:5000" \--tp 16 \--pp-size 1 \--dp 1 \--ep-size 16 \--attention-backend dsv4 \--kv-cache-dtype fp8_e4m3 \--page-size 256 \--moe-a2a-backend deepep \--moe-runner-backend deep_gemm \--moe-dense-tp-size 1 \--deepep-mode auto \--context-length 262144 \--mem-fraction-static 0.84 \--chunked-prefill-size 8192 \--enable-mixed-chunk \--disable-overlap-schedule \--max-running-requests 64 \--cuda-graph-max-bs-decode 64 \--cuda-graph-backend-decode full \--cuda-graph-backend-prefill disabled \--chat-template /path/to/nex-n2.5-max/chat_template.jinja \--reasoning-parser deepseek-r1 \--tool-call-parser qwen3_coder
Single node with 8 × H100:
bash
docker run --gpus all --shm-size 32g --ipc=host \-p 30000:30000 \-v /path/to/your/model:/model \nexagi/sglang:v0.5.18-nex-patch \python3 -m sglang.launch_server \--model-path /model \--tp 8 \--host 0.0.0.0 --port 30000 \--reasoning-parser qwen3 \--tool-call-parser qwen3_coder \--chat-template /path/to/nex-N2.5-Pro/chat-template.jinja \--mamba-scheduler-strategy extra_buffer
Single node with 2 × H100:
bash
docker run --gpus all --shm-size 32g --ipc=host \-p 30000:30000 \-v /path/to/your/model:/model \nexagi/sglang:v0.5.18-nex-patch \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 \--chat-template /path/to/nex-N2.5-mini/chat-template.jinja \--mamba-scheduler-strategy extra_buffer
For the best generation quality, we recommend the following sampling parameters:
temperature: 0.7top_p: 0.95top_k: 40Use reasoning_effort to control the thinking behavior of Nex-N2.5:
reasoning_effort | Mode | Behavior |
|---|---|---|
"none" | Non-thinking | Respond directly without a reasoning trace. |
"medium" (default) | Adaptive thinking | Let the model decide whether and how much to think before responding. |
"high" | Thinking | Always enable thinking before responding. |
For adaptive thinking, set reasoning_effort to "medium" in your OpenAI-compatible Chat Completions request. Replace <served-model-name> with the model name exposed by your server:
json
{"model": "<served-model-name>","messages": [{"role": "user", "content": "Explain how binary search works."}],"reasoning_effort": "medium"}
The chat template uses reasoning_effort; parameters such as enable_thinking and thinking_mode require gateway-specific translation.
Nex-series models support robust function-calling capabilities. To enable function calling, add the --tool-call-parser qwen3_coder flag when launching the server:
bash
python -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder
When the model produces a reasoning trace, configure SGLang to separate it from the final response:
--reasoning-parser qwen3--reasoning-parser deepseek-r1The deployment commands above include the appropriate reasoning parser and --tool-call-parser qwen3_coder. The parser extracts reasoning content; use reasoning_effort to select the thinking mode.