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Cataloged from Nanbeige/Nanbeige4.3-33B
All models by Nanbeige →In Nanbeige4.3, we take a foundational step toward pushing the boundaries of lightweight agents. Our strategy centers on building a high-capacity teacher agent first, which will subsequently anchor the distillation of advanced agentic capabilities into smaller models.
To this end, we first introduce Nanbeige4.3-33B, designed as the flagship teacher model for the Nanbeige4.3 series. Delivering state-of-the-art agentic performance among open models in the ~30B parameter class, Nanbeige4.3-33B excels at complex coding and professional cowork tasks. It not only offers a powerful, cost-effective option for immediate real-world deployment, but also lays the necessary foundation to power our upcoming small model distillation pipeline (e.g., for Nanbeige4.3-3B).
We compare Nanbeige4.3-33B with recently released frontier models at around the 30B-parameter scale: Qwen3.8-27B, Muse-Glimmer-30B, and Gemma4-31B. All models are evaluated at their highest thinking effort. For each benchmark, we use the same inference hyperparameters and harness across models.
1 Nanbeige4.3-33B is evaluated withpreserve_thinking=true in the chat template.Nanbeige4.3-33B leads on most of the benchmarks, showing strong performance in both agentic coding and cowork tasks. Beyond its breadth across benchmarks, Nanbeige4.3-33B shows strong performance on agent tasks across a wide range of professions and industries. For example, on JobBench, it outperforms the baseline in most of the 35 occupations evaluated.
The tokenizer provides a configurable chat template for reasoning and tool-use scenarios:
enable_thinking controls whether the model generates reasoning for the current response. It is enabled by default; set it to False for non-thinking mode.preserve_thinking controls whether reasoning from previous assistant turns is retained in a multi-turn conversation. We recommend False for general chat and question answering, and True for multi-turn tool use, office tasks, and code-agent workflows.tools enables the tool-use template. We recommend tool_call_format="xml" for the best tool-calling performance; json is also supported for compatibility.The model supports a context length of up to 524,288 tokens (512K).
We recommend adjusting the inference settings according to the target scenario:
You can run the model with Transformers, SGLang, or vLLM:
If you have any questions, please open an issue in this repository or contact us at nanbeige@kanzhun.com.
We also welcome collaborations to build domain-specific and industry-focused agents, including joint work on data and training.
If you find our model useful or would like to use it in your own work, please cite this Hugging Face project:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = 'Nanbeige/Nanbeige4.3-33B'tokenizer = AutoTokenizer.from_pretrained( model_id, use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype='auto', device_map='auto', trust_remote_code=True)
messages = [{'role': 'user', 'content': 'Which number is bigger, 9.11 or 9.8?'}]inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors='pt').to(model.device)output_ids = model.generate(inputs, max_new_tokens=1024)response = tokenizer.decode(output_ids[0][inputs.shape[-1]:], skip_special_tokens=True)print(response)MODEL_PATH=/path/to/your/Nanbeige4.3-33Bpython -m sglang.launch_server \ --model-path ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tp-size 4 \ --mem-fraction-static 0.8 \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coderMODEL_PATH=/path/to/your/Nanbeige4.3-33Bvllm serve ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tensor-parallel-size 4 \ --gpu-memory-utilization 0.8 \ --enable-auto-tool-choice \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coder@misc{nanbeige43chat2026, title = {Nanbeige4.3-33B}, author = {Nanbeige Team}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Nanbeige/Nanbeige4.3-33B}}}