9B-parameter multimodal model
The model is built end to end from SigLip2, Whisper-medium, CosyVoice2, and Qwen3-8B, with a total of 9B parameters, and is presented as the latest model in the MiniCPM-o series.
openbmb/MiniCPM-o-4_5
MiniCPM-o 4.5 is a 9B-parameter multimodal model on Hugging Face for vision, speech, OCR, and full-duplex live-streaming workflows. It supports instruct and thinking modes, bilingual English and Chinese speech, and multiple deployment paths including PyTorch, llama.cpp, Ollama, vLLM, and SGLang.
Overview
MiniCPM-o 4.5 is a multimodal model from openbmb on Hugging Face that combines vision, speech, and live-streaming capabilities in a single end-to-end system. The model card describes it as the latest and most capable model in the MiniCPM-o series, built from SigLip2, Whisper-medium, CosyVoice2, and Qwen3-8B with 9B parameters.
The source highlights four main jobs for the model: visual understanding, real-time speech conversation, full-duplex multimodal live streaming, and OCR/document parsing. It also notes that the model supports bilingual English and Chinese speech, more than 30 languages overall, and several deployment paths ranging from PyTorch on Nvidia GPUs to llama.cpp, Ollama, vLLM, SGLang, quantized formats, and FlagOS.
Key features
Instruct and thinking modes
MiniCPM-o 4.5 supports both instruct and thinking modes in a single model, which lets users choose between faster responses and deeper reasoning behavior without switching models.
Speech conversation features
The model supports bilingual real-time speech conversation in English and Chinese, configurable assistant voices, voice cloning, and role play from a short reference audio clip.
Full-duplex live streaming
It can process continuous audio and video streams at the same time while generating text and speech outputs concurrently, enabling full-duplex live interaction without blocking between modalities.
High-resolution visual input
The model handles high-resolution images up to 1.8 million pixels and high-FPS video up to 10 fps, and it is described as strong on OCR and document parsing tasks.
Multiple inference and deployment options
The page lists multiple deployment paths, including PyTorch on Nvidia GPU, llama.cpp, Ollama, quantized int4 and GGUF models, vLLM, SGLang, and FlagOS support.
Common use cases
Multimodal assistant workflows
Use the model when you need one system that can answer questions about images, document pages, or other visual inputs while also handling speech-related interaction.
Real-time voice assistants
Use it for live conversational agents that must listen and respond in real time, including bilingual English and Chinese speech interactions with configurable voices.
Full-duplex live streaming
Use it for applications that combine camera input and audio with continuous output, such as demo experiences, embodied interfaces, or live scene commentary.
Document and OCR processing
Use it when OCR or document parsing is part of the job, especially for high-resolution pages and image-heavy workflows.
Local and optimized deployment
Use the deployable variants and runtime support when you need local or optimized inference through PyTorch, llama.cpp, Ollama, vLLM, SGLang, or quantized formats.
Pros and Cons
Pros
- Combines vision, speech, and streaming behavior in one model instead of requiring separate systems.
- Supports both instruct and thinking modes, which gives users a choice between efficiency and deeper reasoning.
- Offers multiple deployment options, including CPU-oriented and quantized paths as well as higher-throughput runtimes.
- Handles multilingual speech and more than 30 languages, which broadens practical use cases.
- Includes OCR and document parsing capabilities alongside general multimodal understanding.
Cons
- The source describes basic usage as PyTorch inference with Nvidia GPU, so the most direct setup is not lightweight for all environments.
- Some capabilities, such as full-duplex live streaming and proactive interaction, are specialized and may require more complex integration than standard single-turn inference.
- The collected pricing information is about Hugging Face services in general, not model-specific pricing for MiniCPM-o 4.5.
FAQ
What kind of model is MiniCPM-o 4.5?
MiniCPM-o 4.5 is positioned as a multimodal model for vision, speech, and full-duplex live streaming. It is presented as a single model that supports both instruct and thinking modes.
How can MiniCPM-o 4.5 be run?
The source says the model can be used with PyTorch inference on Nvidia GPU for basic usage, and also supports llama.cpp and Ollama for CPU inference, quantized int4 and GGUF formats, vLLM and SGLang for higher-throughput inference, and FlagOS for a unified multi-chip backend plugin.
What does the model support beyond text and image understanding?
The model is described as supporting real-time speech conversation in English and Chinese, plus full-duplex multimodal live streaming where video and audio inputs can be processed while text and speech outputs are generated concurrently.
What input types and language coverage are mentioned?
The model card says MiniCPM-o 4.5 can process high-resolution images up to 1.8 million pixels, high-FPS video up to 10 fps, and supports multilingual capabilities in more than 30 languages.
Is there public pricing for using this model on Hugging Face?
The Hugging Face pricing page confirms that Hugging Face offers paid infrastructure and inference services, but the collected source does not provide model-specific pricing for MiniCPM-o 4.5.
Quick Facts
- Provider
- openbmb
- Platform
- Hugging Face
- Model type
- Multimodal model
- Modalities
- Vision, speech, audio, video, text
- Parameter count
- 9B
- Source domain
- huggingface.co
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