Mini Minicpm O 2 6 The 8b Parameter Multimodal Llm Beating Gpt 4o By Samar Singh Jan 2025

By switzerlandersing On Sep 12, 2025

Mini MiniCPM-o 2.6: The 8B Parameter Multimodal LLM Beating GPT-4o | By Samar Singh | Medium

Mini MiniCPM-o 2.6: The 8B Parameter Multimodal LLM Beating GPT-4o | By Samar Singh | Medium In a groundbreaking development, mini cpm o has taken the world of multimodal large language models (llms) by storm. with its 8 billion parameter architecture, it not only outperforms. 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.

Mini MiniCPM-o 2.6: The 8B Parameter Multimodal LLM Beating GPT-4o | By Samar Singh | Medium

Mini MiniCPM-o 2.6: The 8B Parameter Multimodal LLM Beating GPT-4o | By Samar Singh | Medium 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 multi image and video understanding, and shows promising in context learning capability. Openbmb has released minicpm o 2.6, an 8 billion parameter multimodal large language model (llm) that reportedly outperforms competitors such as gpt 4o, gemini 1.5 pro, and sonnet in various tasks. 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. 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.

Mini MiniCPM-o 2.6: The 8B Parameter Multimodal LLM Beating GPT-4o | By Samar Singh | Medium

Mini MiniCPM-o 2.6: The 8B Parameter Multimodal LLM Beating GPT-4o | By Samar Singh | Medium 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. 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. 💥 introducing our minicpm o 2.6: an 8b size, gpt 4o level omni model runs on device highlights:match gpt 4o 202405 in vision, audio and multimodal live st. With a total of 8b parameters, minicpm o 2.6 achieves comparable performance to gpt 4o 202405 in vision, speech, and multimodal live streaming, making it one of the most versatile and performant models in the open source community. Openbmb's minicpm o 2.6 addresses these challenges with its 8 billion parameter architecture. this model offers complete multimodal capabilities, supporting vision, speech, and language processing while working well on peripheral devices such as smartphones, tablets, and ipads. Minicpm o 2.6 is the latest multimodal large language model (mllm) developed by the openbmb team, featuring 8 billion parameters and capable of high quality visual, voice, and multimodal interactions on edge devices like smartphones.

Mini MiniCPM-o 2.6: The 8B Parameter Multimodal LLM Beating GPT-4o | By Samar Singh | Medium

Mini MiniCPM-o 2.6: The 8B Parameter Multimodal LLM Beating GPT-4o | By Samar Singh | Medium 💥 introducing our minicpm o 2.6: an 8b size, gpt 4o level omni model runs on device highlights:match gpt 4o 202405 in vision, audio and multimodal live st. With a total of 8b parameters, minicpm o 2.6 achieves comparable performance to gpt 4o 202405 in vision, speech, and multimodal live streaming, making it one of the most versatile and performant models in the open source community. Openbmb's minicpm o 2.6 addresses these challenges with its 8 billion parameter architecture. this model offers complete multimodal capabilities, supporting vision, speech, and language processing while working well on peripheral devices such as smartphones, tablets, and ipads. Minicpm o 2.6 is the latest multimodal large language model (mllm) developed by the openbmb team, featuring 8 billion parameters and capable of high quality visual, voice, and multimodal interactions on edge devices like smartphones.