MiniCPM-o 4.5: 9B Multimodal AI Model Release

• Architecture: Qwen3-8B based
• Vision: SigLip2
• Audio: Whisper-medium
• Voice: CosyVoice2
• Latency: Low for on-device inference

Performance & Benchmarks

In terms of raw performance, MiniCPM-o 4.5 achieves an average score of 78.2 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. This score places it at Gemini 2.5 Flash level performance, which is a remarkable feat for a model with only 9 billion parameters. The model has been rigorously tested against competitors like GPT-4o, Qwen3-VL-8B, and InternVL-3.5-8B, consistently outperforming or matching them in specific vision and reasoning tasks.

The model excels in image understanding and video comprehension, areas where many smaller models typically struggle. Its ability to process video streams in real-time allows for dynamic content analysis, making it suitable for surveillance, education, and interactive media applications. The benchmark results indicate that the model maintains high accuracy even when processing complex, multi-step reasoning tasks that require integrating visual and textual context.

HumanEval and SWE-bench scores further validate its coding and problem-solving capabilities, demonstrating that size does not equate to intelligence in this architecture. The model's efficiency means it can run inference at a fraction of the cost of larger models while delivering comparable results. This balance of speed and accuracy is what makes MiniCPM-o 4.5 a preferred choice for developers building resource-constrained applications.

• OpenCompass Score: 78.2
• Comparison: Gemini 2.5 Flash level
• Strengths: Vision, Video, Reasoning
• Efficiency: High throughput on edge devices

API Pricing & Access

As an open-source model, MiniCPM-o 4.5 does not carry an official API subscription fee from OpenBMB. Developers can access the model weights directly for free, allowing for self-hosted deployment without per-token costs. However, if utilizing a third-party inference platform that hosts the model, pricing will depend on the provider's specific infrastructure costs. This open-access model encourages community contributions and fine-tuning, fostering a robust ecosystem around the technology.

For enterprise users, the value proposition lies in the elimination of data egress fees and latency costs associated with cloud APIs. By running MiniCPM-o 4.5 locally, companies can save significantly on operational expenditures (OpEx) while maintaining strict data sovereignty. The model's lightweight nature means it can be deployed on existing hardware without the need for expensive GPU clusters, further reducing capital expenditures.

• Model Type: Open Source
• Official API Cost: N/A
• Deployment: Self-hosted or Edge
• Privacy: Local Inference

Comparison Table

When comparing MiniCPM-o 4.5 against current market leaders, the differences in efficiency and capability become clear. While larger models offer higher raw context windows, MiniCPM-o 4.5 matches performance in critical multimodal tasks while using significantly fewer resources. The table below highlights the key distinctions between MiniCPM-o 4.5 and its primary competitors in the multimodal space.

• MiniCPM-o 4.5 offers the best balance of speed and cost.
• Competitors often require more hardware for similar tasks.

Use Cases

The versatility of MiniCPM-o 4.5 makes it suitable for a wide range of applications. In coding, it can assist developers by analyzing screenshots of errors and suggesting fixes in real-time. For reasoning tasks, it can process diagrams and charts to explain complex data visually, enhancing its utility in education and business intelligence.

In the realm of chat and agents, the full-duplex capability allows for natural conversation flows where the agent can observe the user's environment while speaking. RAG (Retrieval-Augmented Generation) systems can benefit from the model's ability to index and retrieve visual documents alongside text, creating a more comprehensive knowledge base for specialized queries.

• Coding Assistance
• Visual Reasoning
• Real-time Chat Agents
• RAG Systems

Getting Started

Accessing MiniCPM-o 4.5 is straightforward for developers. The model weights are available on Hugging Face under the openbmb organization, where you can find the AWQ quantized versions for faster inference. Additionally, the Ollama community has integrated the model, allowing users to run it locally with a single command via their command line interface.

For those interested in the full capabilities, the OpenBMB demo page provides a web-based interface to test the model's multimodal features directly in the browser. Developers should clone the GitHub repository to access the source code and fine-tuning scripts, enabling customization for specific verticals. Documentation is available on the official website to guide integration into existing pipelines.

• Hugging Face: openbmb/MiniCPM-o-4_5-awq
• Ollama: openbmb/minicpm-o4.5
• GitHub: OpenBMB/MiniCPM-o
• Demo: openbmb.github.io/MiniCPM-o-Demo

Comparison

API Pricing — Input: 0.00 / Output: 0.00

Sources