# Nanbeige4.3 33B

URL: https://interfaze.ai/models/nanbeigenanbeige43-33b

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Nanbeige4.3 33B by Nanbeige, a text-generation model. Understand and compare features, benchmarks, and capabilities.

## Comparison

| Feature | Nanbeige4.3 33B | Interfaze |
| --- | --- | --- |
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 524.3K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Nanbeige4.3 33B | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

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View model card on [Hugging Face](https://huggingface.co/Nanbeige/Nanbeige4.3-33B)

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.

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.
-   Passing `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:

| Scenario | Temperature | Top\_p | Max New Tokens |
| --- | --- | --- | --- |
| Agentic and tool-use tasks | 1.0 | 0.95 | 65,536 |
| Reasoning and chat tasks | 0.6 | 0.95 | 262,144 |

You can run the model with Transformers, SGLang, or vLLM:

## Transformers

```
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)
```

## SGLang

```
MODEL_PATH=/path/to/your/Nanbeige4.3-33B
python -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_coder
```

## vLLM

```
MODEL_PATH=/path/to/your/Nanbeige4.3-33B
vllm 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
```

If you have any questions, please open an issue in this repository or contact us at [nanbeige@kanzhun.com](mailto: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:

```
@misc{nanbeige43chat2026,
  title        = {Nanbeige4.3-33B},
  author       = {Nanbeige Team},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Nanbeige/Nanbeige4.3-33B}}
}
```

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