Project Title

MiniCPM-V — A High-Performance, On-Device Multimodal LLM for Image, Video, and Text Understanding

Overview

MiniCPM-V is a series of efficient end-side multimodal LLMs (MLLMs) that accept images, videos, and text as inputs and deliver high-quality text outputs. The project stands out for its strong performance and efficient deployment, particularly with the latest MiniCPM-V 4.5 model, which outperforms several industry-leading models in vision-language capabilities and introduces new features like high-FPS video understanding and complex document parsing.

Key Features

High-FPS and long video understanding with up to 96x compression rate for video tokens
Controllable hybrid fast/deep thinking for efficient processing
Strong handwritten OCR and complex table/document parsing capabilities
Multilingual support and end-side deployability

Use Cases

Mobile developers looking to integrate advanced image and video understanding into their apps
Enterprises needing on-device multimodal models for data privacy and efficiency
Researchers and developers in the field of multimodal AI for cutting-edge applications

Advantages

Outperforms GPT-4o-latest, Gemini-2.0 Pro, and Qwen2.5-VL 72B in vision-language capabilities
Supports real-time speech conversation with configurable voices and voice cloning
Enables multimodal live streaming on end-side devices like iPad

Limitations / Considerations

The project's license is currently unknown, which may affect its use in commercial applications
As with any AI model, there may be limitations in understanding complex or nuanced content

GPT-4o: A large-scale language model that serves as a benchmark for MiniCPM-V's performance. GPT-4o is known for its capabilities in natural language understanding but does not focus on multimodal inputs.
Gemini-2.0 Pro: Another multimodal model that MiniCPM-V outperforms. Gemini-2.0 Pro is recognized for its multimodal capabilities but may not match MiniCPM-V's efficiency and on-device performance.
Qwen2.5-VL 72B: A competitor model with a large parameter count that MiniCPM-V surpasses in performance. Qwen2.5-VL 72B is part of the growing field of very large language models but may not offer the same level of optimization for on-device use.

Basic Information

GitHub: https://github.com/OpenBMB/MiniCPM-V
Stars: 21,447
License: Unknown
Last Commit: 2025-09-07

📊 Project Information

Project Name: MiniCPM-V
GitHub URL: https://github.com/OpenBMB/MiniCPM-V
Programming Language: Python
⭐ Stars: 21,447
🍴 Forks: 1,594
📅 Created: 2024-01-29
🔄 Last Updated: 2025-09-07

🏷️ Project Topics

Topics: [, ", m, i, n, i, c, p, m, ", ,, , ", m, i, n, i, c, p, m, -, v, ", ,, , ", m, u, l, t, i, -, m, o, d, a, l, ", ]

🎮 Online Demos

here

📚 Documentation

Docs Site

This article is automatically generated by AI based on GitHub project information and README content analysis

MiniCPM-V

Project Description