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Cataloged from Nanbeige/Nanbeige4.1-3B
All models by Nanbeige →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:
Technical Report: Link
We evaluate Nanbeige4.1-3B across a broad and diverse set of benchmarks covering general reasoning, and deep-search capabilities.
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.
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.
For inference hyperparameters, we recommend the following settings:
For the chat scenario:
For the tool use scenario:
For the deep-search scenario:
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.
If you find our model useful or want to use it in your projects, please cite as follows:
If you have any questions, please raise an issue or contact us at nanbeige@kanzhun.com.
from transformers import AutoModelForCausalLM, AutoTokenizertokenizer = AutoTokenizer.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', torch_dtype='auto', device_map='auto', trust_remote_code=True)messages = [ {'role': 'user', 'content': 'Which number is bigger, 9.11 or 9.8?'}]prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False)input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_idsoutput_ids = model.generate(input_ids.to('cuda'), eos_token_id=166101)resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)print(resp)from transformers import AutoModelForCausalLM, AutoTokenizertokenizer = AutoTokenizer.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( 'Nanbeige/Nanbeige4.1-3B', torch_dtype='auto', device_map='auto', trust_remote_code=True)messages = [ {'role': 'user', 'content': 'Help me check the weather in Beijing now'}]tools = [{'type': 'function', 'function': {'name': 'SearchWeather', 'description': 'Find out the current weather in a place on a certain day.', 'parameters': {'type': 'dict', 'properties': {'location': {'type': 'string', 'description': 'A city in China.'}, 'required': ['location']}}}}]prompt = tokenizer.apply_chat_template( messages, tools, add_generation_prompt=True, tokenize=False)input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_idsoutput_ids = model.generate(input_ids.to('cuda'), max_new_tokens=512, eos_token_id=166101)resp = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)print(resp)@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}, }