MiniCPM-V 4.6 (1.3B) is now live 🚀🚀

High-res visual processing, optimized for consumer-grade and mobile hardware. We’ve leveraged the latest LLaVA-UHD v4 technique to cut vision encoding costs by 55%, enabling native edge deployment with extreme efficiency.
🔥 Beats Gemma4-E2B-it and Qwen3.5-0.8B across key multimodal and Artificial Analysis benchmarks — scoring higher than Qwen3.5-0.8B using just 2.5% of its token budget.
⚡ TTFT (75.7ms) 2.2x Faster than Qwen3.5-0.8B even with 3136² high-res images.
🏗️ ~1.5x Token Throughput compared with Qwen3.5-0.8B on a single RTX 4090.

Try the model here:
🤗 Hugging Face: https://huggingface.co/openbmb/MiniCPM-V-4.6
💻 GitHub: https://github.com/OpenBMB/MiniCPM-V
🔭 Modelscope: https://modelscope.cn/models/OpenBMB/MiniCPM-V-4.6
🌐 Web Demo: https://huggingface.co/spaces/openbmb/MiniCPM-V-4.6-Demo
📱 App Demo : GitHub - OpenBMB/MiniCPM-V-Apps
#openbmb #edge #minicpm

Видео MiniCPM-V 4.6 (1.3B) is now live 🚀🚀 канала OpenBMB