| --- |
| license: apache-2.0 |
| library_name: transformers |
| --- |
| <div align="center"> |
| <picture> |
| <img src="stepfun-logo.png" width="30%" alt="StepFun: Cost-Effective Multimodal Intelligence"> |
| </picture> |
| </div> |
| |
| <hr> |
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| <div align="center" style="line-height:1"> |
| <a href="https://stepfun.com/" target="_blank"><img alt="Chat" src="https://img.shields.io/badge/Chat-StepFun-ff6b6b?color=1783ff&logoColor=white"/></a> |
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| <div align="center" style="line-height: 1;"> |
| <a href="https://github.com/stepfun-ai/Step3" target="_blank"><img alt="Github" src="https://img.shields.io/badge/🤖Github-StepFun-ffc107?color=ffc107&logoColor=white"/></a> |
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| <a href="LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue?&color=blue"/></a> |
| </div> |
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| <div align="center"> |
| <b>📰 <a href="https://stepfun.ai/research/step3">Step3 Model Blog</a></b> | <b>📄 <a href="https://arxiv.org/abs/2507.19427">Step3 System Blog</a></b> |
| </div> |
|
|
| ## Introduction |
|
|
| Step3 is our cutting-edge multimodal reasoning model—built on a Mixture-of-Experts architecture with 321B total parameters and 38B active. |
| It is designed end-to-end to minimize decoding costs while delivering top-tier performance in vision–language reasoning. |
| Through the co-design of Multi-Matrix Factorization Attention (MFA) and Attention-FFN Disaggregation (AFD), |
| Step3 maintains exceptional efficiency across both flagship and low-end accelerators. |
|
|
| ### Step3 model card: |
|
|
| | Config | Value | |
| |------------------------|---------| |
| | **Number of Layers (Dense layer included)**|61| |
| |**Number of Dense Layers**| 5| |
| | **Hidden Dimension** | 7168 | |
| | **Attention Mechanism** | MFA | |
| | **Low-rank Query Dimension** | 2048 | |
| | **Number of Query Heads** | 64 | |
| | **Head Dimension** | 256 | |
| |**Number of Experts** |48| |
| |**Selected Experts per Token**|3| |
| |**Number of Shared Experts**| 1| |
| | **Max Context Length** | 65536 | |
| | **Tokenizer** | Deepseek V3 | |
| | **Total Parameters (LLM)** | 316B | |
| | **Activated Params per Token** | 38B | |
| | **Total Parameters (VLM)** | 321B | |
|
|
|
|
| ## Evaluation Results |
| <table> |
| <thead> |
| <tr> |
| <th></th> |
| <th>Model</th> |
| <th>Total Params.</th> |
| <th>MMMU</th> |
| <th>MathVision</th> |
| <th>ZeroBench(sub)</th> |
| <th>DYNAMATH</th> |
| <th>SimpleVQA</th> |
| <th>HallusionBench</th> |
| <th>AIME25</th> |
| <th>HMMT25</th> |
| <th>CNMO24</th> |
| <th>GPQA-Diamond</th> |
| <th>LiveCodeBench<br>(24.8-25.5)</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td rowspan="6">Open-Source VLM</td> |
| <td>Step3</td> |
| <td>321B</td> |
| <td>74.2</td> |
| <td>64.8</td> |
| <td>23.0</td> |
| <td>50.1</td> |
| <td>62.2</td> |
| <td>64.2</td> |
| <td>82.9</td> |
| <td>70.0</td> |
| <td>83.7</td> |
| <td>73.0</td> |
| <td>67.1</td> |
| </tr> |
| <tr> |
| <td>ERINE4.5 - thinking</td> |
| <td>300B/424B</td> |
| <td>70.0</td> |
| <td>47.6</td> |
| <td>22.5</td> |
| <td>46.9</td> |
| <td>59.8</td> |
| <td>60.0</td> |
| <td>35.1</td> |
| <td>40.5*</td> |
| <td>75.5</td> |
| <td>76.8</td> |
| <td>38.8</td> |
| </tr> |
| <tr> |
| <td>GLM-4.1V-thinking</td> |
| <td>9B</td> |
| <td>68.0</td> |
| <td>49.4</td> |
| <td>22.8</td> |
| <td>41.9</td> |
| <td>48.1</td> |
| <td>60.8</td> |
| <td>13.3</td> |
| <td>6.7</td> |
| <td>25.0</td> |
| <td>47.4</td> |
| <td>24.2</td> |
| </tr> |
| <tr> |
| <td>MiMo-VL</td> |
| <td>7B</td> |
| <td>66.7</td> |
| <td>60.4</td> |
| <td>18.6</td> |
| <td>45.9</td> |
| <td>48.5</td> |
| <td>59.6</td> |
| <td>60.0</td> |
| <td>34.6</td> |
| <td>69.9</td> |
| <td>55.5</td> |
| <td>50.1</td> |
| </tr> |
| <tr> |
| <td>QvQ-72B-Preview</td> |
| <td>72B</td> |
| <td>70.3</td> |
| <td>35.9</td> |
| <td>15.9</td> |
| <td>30.7</td> |
| <td>40.3</td> |
| <td>50.8</td> |
| <td>22.7</td> |
| <td>49.5</td> |
| <td>47.3</td> |
| <td>10.9</td> |
| <td>24.1</td> |
| </tr> |
| <tr> |
| <td>LLaMA-Maverick</td> |
| <td>400B</td> |
| <td>73.4</td> |
| <td>47.2</td> |
| <td>22.8</td> |
| <td>47.1</td> |
| <td>45.4</td> |
| <td>57.1</td> |
| <td>19.2</td> |
| <td>8.91</td> |
| <td>41.6</td> |
| <td>69.8</td> |
| <td>33.9</td> |
| </tr> |
| <tr> |
| <td rowspan="4">Open-Source LLM</td> |
| <td>MiniMax-M1-80k</td> |
| <td>456B</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>76.9</td> |
| <td>-</td> |
| <td>-</td> |
| <td>70.0</td> |
| <td>65.0</td> |
| </tr> |
| <tr> |
| <td>Qwen3-235B-A22B-Thinking</td> |
| <td>235B</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>81.5</td> |
| <td>62.5</td> |
| <td>-</td> |
| <td>71.1</td> |
| <td>65.9</td> |
| </tr> |
| <tr> |
| <td>DeepSeek R1-0528</td> |
| <td>671B</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>87.5</td> |
| <td>79.4</td> |
| <td>86.9</td> |
| <td>81.0</td> |
| <td>73.3</td> |
| </tr> |
| <tr> |
| <td>Qwen3-235B-A22B-Thinking-2507</td> |
| <td>235B</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>-</td> |
| <td>92.3</td> |
| <td>83.9</td> |
| <td>-</td> |
| <td>81.1</td> |
| <td>-</td> |
| </tr> |
| <tr> |
| <td rowspan="6">Proprietary VLM</td> |
| <td>O3</td> |
| <td>-</td> |
| <td>82.9</td> |
| <td>72.8</td> |
| <td>25.2</td> |
| <td>58.1</td> |
| <td>59.8</td> |
| <td>60.1</td> |
| <td>88.9</td> |
| <td>70.1</td> |
| <td>86.7</td> |
| <td>83.3</td> |
| <td>75.8</td> |
| </tr> |
| <tr> |
| <td>Claude4 Sonnet (thinking)</td> |
| <td>-</td> |
| <td>76.9</td> |
| <td>64.6</td> |
| <td>26.1</td> |
| <td>48.1</td> |
| <td>43.7</td> |
| <td>57.0</td> |
| <td>70.5</td> |
| <td>-</td> |
| <td>-</td> |
| <td>75.4</td> |
| <td>55.9</td> |
| </tr> |
| <tr> |
| <td>Claude4 opus (thinking)</td> |
| <td>-</td> |
| <td>79.8</td> |
| <td>66.1</td> |
| <td>25.2</td> |
| <td>49.3</td> |
| <td>47.2</td> |
| <td>59.9</td> |
| <td>75.5</td> |
| <td>-</td> |
| <td>-</td> |
| <td>79.6</td> |
| <td>56.6</td> |
| </tr> |
| <tr> |
| <td>Gemini 2.5 Flash (thinking)</td> |
| <td>-</td> |
| <td>73.2</td> |
| <td>57.3</td> |
| <td>20.1</td> |
| <td>57.1</td> |
| <td>61.1</td> |
| <td>65.2</td> |
| <td>72.0</td> |
| <td>-</td> |
| <td>-</td> |
| <td>82.8</td> |
| <td>61.9</td> |
| </tr> |
| <tr> |
| <td>Gemini 2.5 Pro</td> |
| <td>-</td> |
| <td>81.7</td> |
| <td>73.3</td> |
| <td>30.8</td> |
| <td>56.3</td> |
| <td>66.8</td> |
| <td>66.8</td> |
| <td>88.0</td> |
| <td>-</td> |
| <td>-</td> |
| <td>86.4</td> |
| <td>71.8</td> |
| </tr> |
| <!-- 新增 Grok 4 --> |
| <tr> |
| <td>Grok 4</td> |
| <td>-</td> |
| <td>80.9</td> |
| <td>70.3</td> |
| <td>22.5</td> |
| <td>40.7</td> |
| <td>55.9</td> |
| <td>64.8</td> |
| <td>98.8</td> |
| <td>93.9</td> |
| <td>85.5</td> |
| <td>87.5</td> |
| <td>79.3</td> |
| </tr> |
| </tbody> |
| </table> |
| |
| Note: Parts of the evaluation results are reproduced using the same settings. |
| †: Evaluation results of Gemini 2.5 Flash (thinking) may be lower than real model performance, especially on MathVision, due to insufficient instruction following ability. |
| ## Deployment |
|
|
| > [!Note] |
| > Step3's API is accessible at https://platform.stepfun.com/, where we offer OpenAI-compatible API for you. |
|
|
|
|
| > You can access Step3's API on https://platform.stepfun.com/ , we provide OpenAI/Anthropic-compatible API for you. |
| > |
| |
| |
| ### Inference with Hugging Face Transformers |
|
|
| We introduce how to use our model at inference stage using transformers library. It is recommended to use python=3.10, torch>=2.1.0, and transformers=4.54.0 as the development environment.We currently only support bf16 inference, and multi-patch is supported by default. This behavior is aligned with vllm and sglang. |
|
|
|
|
| ```python |
| from transformers import AutoProcessor, AutoModelForCausalLM |
| |
| key_mapping = { |
| "^vision_model": "model.vision_model", |
| r"^model(?!\.(language_model|vision_model))": "model.language_model", |
| "vit_downsampler": "model.vit_downsampler", |
| "vit_downsampler2": "model.vit_downsampler2", |
| "vit_large_projector": "model.vit_large_projector", |
| } |
| |
| model_path = "stepfun-ai/step3" |
| |
| processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) |
| model = AutoModelForCausalLM.from_pretrained(model_path, |
| device_map="auto", torch_dtype="auto",trust_remote_code=True, |
| key_mapping=key_mapping) |
| |
| messages = [ |
| { |
| "role": "user", |
| "content": [ |
| {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"}, |
| {"type": "text", "text": "What's in this picture?"} |
| ] |
| }, |
| ] |
| |
| inputs = processor.apply_chat_template( |
| messages, add_generation_prompt=True, tokenize=True, |
| return_dict=True, return_tensors="pt" |
| ).to(model.device) |
| |
| generate_ids = model.generate(**inputs, max_new_tokens=32768, do_sample=False) |
| decoded = processor.decode(generate_ids[0, inputs["input_ids"].shape[-1] :], skip_special_tokens=True) |
| |
| print(decoded) |
| |
| ``` |
|
|
|
|
| ### Inference with vLLM and SGLang |
|
|
|
|
| Our model checkpoints are stored in bf16 and block-fp8 format, you can find it on [Huggingface](https://huggingface.co/stepfun-ai/step3). |
|
|
| Currently, it is recommended to run Step3 on the following inference engines: |
|
|
| * vLLM |
| * SGLang |
|
|
| Deployment and Request examples for vLLM and SGLang can be found in the [Model Deployment Guide](docs/deploy_guidance.md). |
|
|
| ## Contact Us |
| If you have any questions, please reach out at [contact@stepfun.com](mailto:contact@stepfun.com) . |
|
|
| ## License |
| Both the code repository and the model weights are released under the [Apache License (Version 2.0)](./LICENSE). |
|
|
| ## Citation |
| ``` |
| @misc{step3system, |
| title={Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding}, |
| author={StepFun Team}, |
| year={2025}, |
| eprint={2507.19427}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/2507.19427}, |
| } |
| |
| @misc{step3blog, |
| title={Step3: Cost-Effective Multimodal Intelligence}, |
| author={StepFun Team}, |
| url={https://stepfun.ai/research/step3}, |
| } |
| ``` |
|
|
|
|
