Nanbeige4.1-3B is built upon Nanbeige4-3B-Base and represents an enhanced iteration of our previous reasoning model, Nanbeige4-3B-Thinking-2511, achieved through further post-training optimization with supervised fine-tuning (SFT) and reinforcement learning (RL). As a highly competitive open-source model at a small parameter scale, Nanbeige4.1-3B illustrates that compact models can simultaneously achieve robust reasoning, preference alignment, and effective agentic behaviors.
Specifically, Nanbeige4.1-3B exhibits the following key strengths:
Strong Reasoning: Nanbeige4.1-3B is capable of solving complex, multi-step problems through sustained and coherent reasoning within a single forward pass, and reliably produces correct final answers on challenging tasks such as LiveCodeBench-Pro, IMO-Answer-Bench, and AIME 2026 I.
Robust Preference Alignment: Nanbeige4.1-3B achieves solid alignment performance, outperforming not only same-scale models such as Qwen3-4B-2507 and Nanbeige4-3B-2511, but also substantially larger models including Qwen3-30B-A3B and Qwen3-32B on Arena-Hard-v2 and Multi-Challenge.
Agentic Capability: Nanbeige4.1-3B is the first general small model to natively support deep-search tasks and reliably sustain complex problem solving involving more than 500 rounds of tool invocations. It fills a long-standing gap in the small-model ecosystem where models are typically optimized for either general reasoning or agentic scenarios, but rarely excel at both.
We evaluate Nanbeige4.1-3B across a broad and diverse set of benchmarks covering general reasoning, and deep-search capabilities.
General Reasoning Tasks
On general reasoning tasks including code, math, science, alignment, and tool-use benchmarks, Nanbeige4.1-3B not only significantly outperforms same-scale models such as Qwen3-4B, but also demonstrates overall superior performance compared to larger models including Qwen3-30B-A3B-2507 and Qwen3-32B.
Benchmark
Qwen3-4B-2507
Qwen3-8B
Qwen3-14B
Qwen3-32B
Qwen3-30B-A3B-2507
Nanbeige4-3B-2511
Nanbeige4.1-3B
Code
Live-Code-Bench-V6
57.4
49.4
55.9
55.7
66.0
46.0
76.9
Live-Code-Bench-Pro-Easy
40.2
41.2
33.0
42.3
60.8
40.2
81.4
Live-Code-Bench-Pro-Mediium
5.3
3.5
1.8
3.5
3.5
5.3
28.1
Math
AIME 2026 I
81.46
70.42
76.46
75.83
87.30
84.1
87.40
HMMT Nov
68.33
48.33
56.67
57.08
71.25
66.67
77.92
IMO-Answer-Bench
48.00
36.56
41.81
43.94
54.34
38.25
53.38
Science
GPQA
65.8
62.0
63.38
68.4
73.4
82.2
83.8
HLE (Text-only)
6.72
5.28
7.00
9.31
11.77
10.98
12.60
Alignment
Arena-Hard-v2
34.9
26.3
36.9
56.0
60.2
60.0
73.2
Multi-Challenge
41.14
36.30
36.97
38.72
49.40
41.20
52.21
Tool Use
BFCL-V4
44.87
42.20
45.14
47.90
48.6
53.8
56.50
Tau2-Bench
45.9
42.06
44.96
45.26
47.70
41.77
48.57
Deep Search Tasks
As a general small model, Nanbeige4.1-3B achieves deep-search performance comparable to specialized agents under 10B parameters.
In contrast to existing small general models, which typically exhibit little to no deep-search capability, Nanbeige4.1-3B represents a substantial qualitative improvement over prior small general models.
Deep Search and Agent Benchmarks
Model
xBench-DeepSearch-2505
xBench-DeepSearch-2510
Browse-Comp
Browse-Comp-ZH
GAIA (Text-only)
HLE
SEAL-0
Search-Specialized Small Agents
MiroThinker-v1.0-8B
61
–
31.1
40.2
66.4
21.5
40.4
AgentCPM-Explore-4B
70
–
25.0
29.0
63.9
19.1
40.0
Large Foundation Models (with Tools)
GLM-4.6-357B
70
–
45.1
49.5
71.9
30.4
–
Minimax-M2-230B
72
–
44.0
48.5
75.7
31.8
–
DeepSeek-V3.2-671B
71
–
67.6
65.0
63.5
40.8
38.5
Small Foundation Models (with Tools)
Qwen3-4B-2507
34
5
1.57
7.92
28.33
11.13
15.74
Qwen3-8B
31
2
0.79
5.15
19.53
10.24
6.34
Qwen3-14B
34
9
2.36
7.11
30.23
10.17
12.64
Qwen3-32B
39
8
3.15
7.34
30.17
9.26
8.15
Qwen3-30B-A3B-2507
25
10
1.57
4.12
31.63
14.81
9.24
Ours (with Tools)
Nanbeige4-3B-2511
33
11
0.79
3.09
19.42
13.89
12.61
Nanbeige4.1-3B
75
39
19.12
31.83
69.90
22.29
41.44
Quickstart
For inference hyperparameters, we recommend the following settings:
Web scraping with LLM-based information extraction with Jina
scrape_and_extract_info
Summary model: Qwen3-14B-thinking
Temperature: 1.0
Note, access to HuggingFace has been explicitly disabled in these tools.
Limitations
While we place great emphasis on the safety of the model during the training process, striving to ensure that its outputs align with ethical and legal requirements, it may not completely avoid generating unexpected outputs due to the model's size and probabilistic nature. These outputs may include harmful content such as bias or discrimination. Please don't propagate such content. We do not assume any responsibility for the consequences resulting from the dissemination of inappropriate information.
Citation
If you find our model useful or want to use it in your projects, please cite as follows:
@misc{yang2026nanbeige413bsmallgeneralmodel,
title={Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts},
author={Chen Yang and Guangyue Peng and Jiaying Zhu and Ran Le and Ruixiang Feng and Tao Zhang and Xiyun Xu and Yang Song and Yiming Jia and Yuntao Wen and Yunzhi Xu and Zekai Wang and Zhenwei An and Zhicong Sun and Zongchao Chen},
year={2026},
eprint={2602.13367},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2602.13367},
}
Contact
If you have any questions, please raise an issue or contact us at nanbeige@kanzhun.com.
