MiniCPM-o-2_6

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News

• [2025.06.20] ⭐️⭐️⭐️ Our official ollama repository is released. Try our latest models with one click！

• [2025.03.01] 🚀🚀🚀 RLAIF-V, which is the alignment technique of MiniCPM-o, is accepted by CVPR 2025！The code, dataset, paper are open-sourced!

• [2025.01.24] 📢📢📢 MiniCPM-o 2.6 technical report is released! See Here.

• [2025.01.19] ⭐️⭐️⭐️ MiniCPM-o tops GitHub Trending and reaches top-2 on Hugging Face Trending!

MiniCPM-o 2.6

MiniCPM-o 2.6 is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.6, and introduces new features for real-time speech conversation and multimodal live streaming. Notable features of MiniCPM-o 2.6 include:

• 🔥 Leading Visual Capability. MiniCPM-o 2.6 achieves an average score of 70.2 on OpenCompass, a comprehensive evaluation over 8 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-202405, Gemini 1.5 Pro, and Claude 3.5 Sonnet for single image understanding. It also outperforms GPT-4V and Claude 3.5 Sonnet in mutli-image and video understanding, and shows promising in-context learning capability.

• 🎙 State-of-the-art Speech Capability. MiniCPM-o 2.6 supports bilingual real-time speech conversation with configurable voices in English and Chinese. It outperforms GPT-4o-realtime on audio understanding tasks such as ASR and STT translation, and shows state-of-the-art performance on speech conversation in both semantic and acoustic evaluations in the open-source community. It also allows for fun features such as emotion/speed/style control, end-to-end voice cloning, role play, etc.

• 🎬 Strong Multimodal Live Streaming Capability. As a new feature, MiniCPM-o 2.6 can accept continous video and audio streams independent of user queries, and support real-time speech interaction. It outperforms GPT-4o-202408 and Claude 3.5 Sonnet and shows state-of-art performance in open-source community on StreamingBench, a comprehensive benchmark for real-time video understanding, omni-source (video & audio) understanding, and multimodal contextual understanding.

• 💪 Strong OCR Capability and Others. Advancing popular visual capabilites from MiniCPM-V series, MiniCPM-o 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344). It achieves state-of-the-art performance on OCRBench for models under 25B, surpassing proprietary models such as GPT-4o-202405. Based on the the latest RLAIF-V and VisCPM techniques, it features trustworthy behaviors, outperforming GPT-4o and Claude 3.5 Sonnet on MMHal-Bench, and supports multilingual capabilities on more than 30 languages.

• 🚀 Superior Efficiency. In addition to its friendly size, MiniCPM-o 2.6 also shows state-of-the-art token density (i.e., number of pixels encoded into each visual token). It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-o 2.6 can efficiently support multimodal live streaming on end-side devices such as iPad.

• 💫 Easy Usage. MiniCPM-o 2.6 can be easily used in various ways: (1) llama.cpp support for efficient CPU inference on local devices, (2) int4 and GGUF format quantized models in 16 sizes, (3) vLLM support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with LLaMA-Factory, (5) quick local WebUI demo setup with Gradio, and (6) online web demo on server.

Model Architecture.

• End-to-end Omni-modal Architecture. Different modality encoder/decoders are connected and trained in an end-to-end fashion to fully exploit rich multimodal knowledge.
• Omni-modal Live Streaming Mechanism. (1) We change the offline modality encoder/decoders into online ones for streaminig inputs/outputs. (2) We devise a time-division multiplexing (TDM) mechanism for omni-modality streaminig processing in the LLM backbone. It divides parallel omni-modality streams into sequential info within small periodic time slices.
• Configurable Speech Modeling Design. We devise a multimodal system prompt, including traditional text system prompt, and a new audio system prompt to determine the assistant voice. This enables flexible voice configurations in inference t