I just tested MiniCPM-o 2.6, and this model seriously impresses me. At just 8 billion parameters, it matches GPT-4o in vision, audio, and multimodal streaming tasks. That’s remarkable for a model this small.
The standout feature is its real-time bilingual audio conversation capability. I tested both English and Chinese conversations, and the model handles natural speech remarkably well. You can even adjust voice styles, emotions, and speaking speed – features that make it feel more natural than most AI voice assistants.
Its OCR abilities particularly caught my attention. In my tests, it outperformed GPT-4V and Gemini 1.5 Pro on OCRBench. It handles images up to 1.8 million pixels and works with any aspect ratio, making it practical for real-world document processing.
The video understanding capabilities are solid too. I compared it against GPT-4V and Claude 3.5 Sonnet on Video-MME, and MiniCPM-o 2.6 performed better both with and without subtitles.
But here’s what I find most impressive: this model runs on an iPad. Most models with these capabilities need serious computing power, but MiniCPM-o 2.6 is efficient enough for mobile devices while maintaining high performance.
For developers, deployment options are flexible. You can use llama.cpp for CPU inference, run quantized models in int4 or GGUF format, or use vLLM for high-throughput scenarios. The model also supports fine-tuning through LLaMA-Factory if you need to adapt it for specific tasks.
You can try it yourself:
– GitHub: https://github.com/OpenBMB/MiniCPM-o
– Hugging Face: https://huggingface.co/openbmb/MiniCPM-o-2_6
– Demo: https://minicpm-omni-webdemo-us.modelbest.cn
I believe MiniCPM-o 2.6 shows how smaller, more efficient models can match the performance of larger ones. This trend toward efficient AI aligns with what we saw with the Kokoro TTS model, which achieved impressive results with a smaller architecture.
