MiniCPM-V 4.0 是MiniCPM-V系列的最新高效模型。该模型基于SigLIP2-400M和MiniCPM4-3B构建，总参数量为41亿。它继承了MiniCPM-V 2.6强大的单图、多图和视频理解能力，并大幅提升了运行效率。MiniCPM-V 4.0的显著特点包括：
🔥 领先的视觉能力 尽管仅拥有41亿参数，MiniCPM-V 4.0在OpenCompass的8项主流基准综合评测中平均得分为69.0，性能超越GPT-4.1-mini-20250414、MiniCPM-V 2.6（81亿参数，OpenCompass得分65.2）和Qwen2.5-VL-3B-Instruct（38亿参数，OpenCompass得分64.5）。同时，它在多图理解和视频理解任务上也表现出色。
🚀 卓越的效率 MiniCPM-V 4.0专为端侧部署设计，可在终端设备上流畅运行。例如，在iPhone 16 Pro Max上，它实现了首token延迟低于2秒，解码速度超过17 token/s，且无发热问题。在并发请求下，其吞吐量也表现优异。
💫 易用性 MiniCPM-V 4.0可通过多种方式轻松使用，包括llama.cpp、Ollama、vLLM、SGLang、LLaMA-Factory和本地网页演示等。我们还开源了可在iPhone和iPad上运行的iOS应用。通过结构清晰的Cookbook，您可以轻松上手，其中包含详细的使用说明和实用示例。
| model | Size | Opencompass | OCRBench | MathVista | HallusionBench | MMMU | MMVet | MMBench V1.1 | MMStar | AI2D |
|---|---|---|---|---|---|---|---|---|---|---|
| 闭源模型 | ||||||||||
| GPT-4v-20240409 | - | 63.5 | 656 | 55.2 | 43.9 | 61.7 | 67.5 | 79.8 | 56.0 | 78.6 |
| Gemini-1.5-Pro | - | 64.5 | 754 | 58.3 | 45.6 | 60.6 | 64.0 | 73.9 | 59.1 | 79.1 |
| GPT-4.1-mini-20250414 | - | 68.9 | 840 | 70.9 | 49.3 | 55.0 | 74.3 | 80.9 | 60.9 | 76.0 |
| Claude 3.5 Sonnet-20241022 | - | 70.6 | 798 | 65.3 | 55.5 | 66.4 | 70.1 | 81.7 | 65.1 | 81.2 |
| 开源模型 | ||||||||||
| Qwen2.5-VL-3B-Instruct | 3.8B | 64.5 | 828 | 61.2 | 46.6 | 51.2 | 60.0 | 76.8 | 56.3 | 81.4 |
| InternVL2.5-4B | 3.7B | 65.1 | 820 | 60.8 | 46.6 | 51.8 | 61.5 | 78.2 | 58.7 | 81.4 |
| Qwen2.5-VL-7B-Instruct | 8.3B | 70.9 | 888 | 68.1 | 51.9 | 58.0 | 69.7 | 82.2 | 64.1 | 84.3 |
| InternVL2.5-8B | 8.1B | 68.1 | 821 | 64.5 | 49.0 | 56.2 | 62.8 | 82.5 | 63.2 | 84.6 |
| MiniCPM-V-2.6 | 8.1B | 65.2 | 852 | 60.8 | 48.1 | 49.8 | 60.0 | 78.0 | 57.5 | 82.1 |
| MiniCPM-o-2.6 | 8.7B | 70.2 | 889 | 73.3 | 51.1 | 50.9 | 67.2 | 80.6 | 63.3 | 86.1 |
| MiniCPM-V-4.0 | 4.1B | 69.0 | 894 | 66.9 | 50.8 | 51.2 | 68.0 | 79.7 | 62.8 | 82.9 |
| model | Size | ChartQA | MME | RealWorldQA | TextVQA | DocVQA | MathVision | DynaMath | WeMath | Obj Hal | MM Hal | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CHAIRs↓ | CHAIRi↓ | score avg@3↑ | hall rate avg@3↓ | ||||||||||
| 闭源模型 | |||||||||||||
| GPT-4v-20240409 | - | 78.5 | 1927 | 61.4 | 78.0 | 88.4 | - | - | - | - | - | - | - |
| Gemini-1.5-Pro | - | 87.2 | - | 67.5 | 78.8 | 93.1 | 41.0 | 31.5 | 50.5 | - | - | - | - |
| GPT-4.1-mini-20250414 | - | - | - | - | - | - | 45.3 | 47.7 | - | - | - | - | - |
| Claude 3.5 Sonnet-20241022 | - | 90.8 | - | 60.1 | 74.1 | 95.2 | 35.6 | 35.7 | 44.0 | - | - | - | - |
| 开源模型 | |||||||||||||
| Qwen2.5-VL-3B-Instruct | 3.8B | 84.0 | 2157 | 65.4 | 79.3 | 93.9 | 21.9 | 13.2 | 22.9 | 18.3 | 10.8 | 3.9 | 33.3 |
| InternVL2.5-4B | 3.7B | 84.0 | 2338 | 64.3 | 76.8 | 91.6 | 18.4 | 15.2 | 21.2 | 13.7 | 8.7 | 3.2 | 46.5 |
| Qwen2.5-VL-7B-Instruct | 8.3B | 87.3 | 2347 | 68.5 | 84.9 | 95.7 | 25.4 | 21.8 | 36.2 | 13.3 | 7.9 | 4.1 | 31.6 |
| InternVL2.5-8B | 8.1B | 84.8 | 2344 | 70.1 | 79.1 | 93.0 | 17.0 | 9.4 | 23.5 | 18.3 | 11.6 | 3.6 | 37.2 |
| MiniCPM-V-2.6 | 8.1B | 79.4 | 2348 | 65.0 | 80.1 | 90.8 | 17.5 | 9.0 | 20.4 | 7.3 | 4.7 | 4.0 | 29.9 |
| MiniCPM-o-2.6 | 8.7B | 86.9 | 2372 | 68.1 | 82.0 | 93.5 | 21.7 | 10.4 | 25.2 | 6.3 | 3.4 | 4.1 | 31.3 |
| MiniCPM-V-4.0 | 4.1B | 84.4 | 2298 | 68.5 | 80.8 | 92.9 | 20.7 | 14.2 | 32.7 | 6.3 | 3.5 | 4.1 | 29.2 |
| model | Size | Mantis | Blink | Video-MME | |
|---|---|---|---|---|---|
| wo subs | w subs | ||||
| 闭源模型 | |||||
| GPT-4v-20240409 | - | 62.7 | 54.6 | 59.9 | 63.3 |
| Gemini-1.5-Pro | - | - | 59.1 | 75.0 | 81.3 |
| GPT-4o-20240513 | - | - | 68.0 | 71.9 | 77.2 |
| 开源模型 | |||||
| Qwen2.5-VL-3B-Instruct | 3.8B | - | 47.6 | 61.5 | 67.6 |
| InternVL2.5-4B | 3.7B | 62.7 | 50.8 | 62.3 | 63.6 |
| Qwen2.5-VL-7B-Instruct | 8.3B | - | 56.4 | 65.1 | 71.6 |
| InternVL2.5-8B | 8.1B | 67.7 | 54.8 | 64.2 | 66.9 |
| MiniCPM-V-2.6 | 8.1B | 69.1 | 53.0 | 60.9 | 63.6 |
| MiniCPM-o-2.6 | 8.7B | 71.9 | 56.7 | 63.9 | 69.6 |
| MiniCPM-V-4.0 | 4.1B | 71.4 | 54.0 | 61.2 | 65.8 |
可通过iOS demo在iPhone 16 Pro Max上本地运行。
from PIL import Image
import torch
from transformers import AutoModel, AutoTokenizer
model_path = 'openbmb/MiniCPM-V-4'
model = AutoModel.from_pretrained(model_path, trust_remote_code=True,
# sdpa or flash_attention_2, no eager
attn_implementation='sdpa', torch_dtype=torch.bfloat16)
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(
model_path, trust_remote_code=True)
image = Image.open('./assets/single.png').convert('RGB')
# First round chat
question = "What is the landform in the picture?"
msgs = [{'role': 'user', 'content': [image, question]}]
answer = model.chat(
msgs=msgs,
image=image,
tokenizer=tokenizer
)
print(answer)
# Second round chat, pass history context of multi-turn conversation
msgs.append({"role": "assistant", "content": [answer]})
msgs.append({"role": "user", "content": [
"What should I pay attention to when traveling here?"]})
answer = model.chat(
msgs=msgs,
image=None,
tokenizer=tokenizer
)
print(answer)👏 欢迎探索 MiniCPM-V 2.6 的核心技术以及我们团队的其他多模态项目：
VisCPM | RLHF-V | LLaVA-UHD | RLAIF-V
如果您觉得我们的工作对您有所帮助，请考虑引用我们的论文 📝 并为该项目点赞 ❤️！
@article{yao2024minicpm,
title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others},
journal={Nat Commun 16, 5509 (2025)},
year={2025}
}