cpral/Nex-N2-Pro-EXL3-5bpw API & Inference Endpoint

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.

Nex-N2-Pro: Hugging Face | ModelScope
Nex-N2-mini: Coming soon
Early Access: SiliconFlow

We welcome developers and enterprises to integrate and try Nex-N2 and share their feedback.

Performance

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 Benchmark Overview

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.

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

Usage

Local Deployment

We recommend sglang for serving Nex-series models locally:

bash
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: 0.7
top_p: 0.95
top_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:

bash
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:

bash
python -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder --reasoning-parser qwen3

Nex-N2-Pro-EXL3-5bpw

Get help setting up a custom Dedicated Endpoints.

README