A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone

MiniCPM-V 4.5 🤗 🤖 | MiniCPM-o 2.6 🤗 🤖 | 🍳 Cookbook | 📄 Technical Report (Coming Soon)

MiniCPM-V 4.5 is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters. It exhibits a significant performance improvement over previous MiniCPM-V and MiniCPM-o models, and introduces new useful features. Notable features of MiniCPM-V 4.5 include:

• 🔥 State-of-the-art Vision-Language Capability. MiniCPM-V 4.5 achieves an average score of 77.2 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-latest, Gemini-2.0 Pro, and strong open-source models like Qwen2.5-VL 72B for vision-language capabilities, making it the most performant MLLM under 30B parameters.

• 🎬 Efficient High Refresh Rate and Long Video Understanding. Powered by a new unified 3D-Resampler over images and videos, MiniCPM-V 4.5 can now achieve 96x compression rate for video tokens, where 6 448x448 video frames can be jointly compressed into 64 video tokens (normally 1,536 tokens for most MLLMs). This means that the model can percieve significantly more video frames without increasing the LLM inference cost. This brings state-of-the-art high refresh rate (up to 10FPS) video understanding and long video understanding capabilities on Video-MME, LVBench, MLVU, MotionBench, FavorBench, etc., efficiently.

• ⚙️ Controllable Hybrid Fast/Deep Thinking. MiniCPM-V 4.5 supports both fast thinking for efficient frequent usage with competitive performance, and deep thinking for more complex problem solving. To cover efficiency and performance trade-offs in different user scenarios, this fast/deep thinking mode can be switched in a highly controlled fashion.

• 💪 Strong OCR, Document Parsing and Others. Based on LLaVA-UHD architecture, MiniCPM-V 4.5 can process high-resolution images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), using 4x less visual tokens than most MLLMs. The model achieves leading performance on OCRBench, surpassing proprietary models such as GPT-4o-latest and Gemini 2.5. It also achieves state-of-the-art performance for PDF document parsing capability on OmniDocBench among general MLLMs. Based on the the latest RLAIF-V and VisCPM techniques, it features trustworthy behaviors, outperforming GPT-4o-latest on MMHal-Bench, and supports multilingual capabilities in more than 30 languages.

• 💫 Easy Usage. MiniCPM-V 4.5 can be easily used in various ways: (1) llama.cpp and ollama support for efficient CPU inference on local devices, (2) int4, GGUF and AWQ format quantized models in 16 sizes, (3) SGLang and vLLM support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with Transformers and LLaMA-Factory, (5) quick local WebUI demo, (6) optimized local iOS app on iPhone and iPad, and (7) online web demo on server. See our Cookbook for full usages!

Note: If you want to use local deployment, you can refer to this document.