OpenBMBOpen Source

MiniCPM-V 4.6

Name: MiniCPM-V 4.6
Author: OpenBMB

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A multimodal model developed by OpenBMB, excelling at integrated processing of text and images.

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Parameters

Context Window

262K

License

Apache 2.0

Release Date

2026-05-11

Japanese Language Capability

🌐Multilingual

General multilingual model. Basic Japanese processing is possible, but inferior to specialized models.

API Pricing

API pricing for this model is not yet available

Strengths

Weaknesses

Use Cases

Deep Analysis

Parameters

1.3B

Base model: SigLIP2-400M (vision) + Qwen3.5-0.8B (LLM)

Intelligence Index

Surpasses Qwen3.5-0.8B (10) with 19x lower token cost

VRAM Requirement

4 GB

GGUF variant: 2 GB CPU memory

Context Window

262,144 tokens

262K tokens

Mobile Deployment

iOS, Android, HarmonyOS

Full edge adaptation code open-sourced

License

Apache 2.0

Commercial use permitted

Strengths

・Ultra-efficient architecture with >50% reduction in visual encoding FLOPs enabling real on-device inference
・Flexible mixed 4x/16x visual token compression allows trading accuracy for speed per task
・Broad mobile platform coverage (iOS, Android, HarmonyOS) with fully open-sourced edge code

Weaknesses

・Performance ceiling due to 1.3B parameter size limits complex multi-step reasoning tasks
・Real-world on-device latency and stability can vary significantly by device hardware
・Most benchmark results are self-reported by the OpenBMB team without independent verification

Competitor Comparison

Model	Arena	SWE	GPQA	Price
Qwen3.5-0.8B	N/A	N/A	N/A	$0.00 (open weights)
Gemma4-E2B	N/A	N/A	N/A	$0.00 (open weights)
Mistral Medium 3.5	N/A	N/A	74.8%	$1.50/$7.50

Overview

MiniCPM-V 4.6 is a 1.3-billion parameter multimodal model specifically engineered by OpenBMB for ultra-efficient deployment on edge devices, particularly smartphones. Its core innovation lies in a highly optimized architecture based on LLaVA-UHD v4, which reduces visual encoding computation by over 50%, and a novel mixed 4x/16x visual token compression mechanism. This allows it to achieve superior performance-to-size ratios, scoring 13 on the Artificial Analysis Intelligence Index—outperforming the larger Qwen3.5-0.8B with drastically lower token costs. Positioned as a 'pocket-sized MLLM', the model prioritizes practical on-device capabilities over raw cloud-scale intelligence. It supports single-image, multi-image, and video understanding, with strong results in OCR and document parsing (e.g., 84.6 on OmniDocBench). Its design philosophy is efficiency-first, enabling deployment on mainstream mobile platforms (iOS, Android, HarmonyOS) with low VRAM requirements (4GB GPU, 2GB CPU via GGUF). This makes it a compelling open-source option for developers building vision-language features directly into consumer applications. While it cannot match the absolute performance of frontier cloud models like GPT-5 or Gemini 2.5 Flash, MiniCPM-V 4.6 carves a unique niche by bringing credible multimodal understanding to resource-constrained environments. It represents a significant step towards practical, privacy-preserving AI on personal devices, backed by a comprehensive developer ecosystem including multiple quantization formats and support for major inference frameworks like vLLM, llama.cpp, and Ollama.

Benchmarks & Performance

### Benchmark Performance (Instruct Model) | Benchmark | MiniCPM-V 4.6 | Qwen3.5-0.8B | Gemma4-E2B | Context | | :--- | :--- | :--- | :--- | :--- | | **Intelligence Index** | **13** | 10 | N/A | Composite benchmark. Outperforms larger 0.8B model with 19x lower token cost. | | **MMStar** | **68.0** | 55.9 | 55.7 | General multimodal reasoning. | | **MathVista** | **75.5** | 58.6 | 58.5 | Math with visual context. | | **OmniDocBench** | **84.6** | 70.6 | 47.0 | Document understanding; extremely large performance gap. | | **HallusionBench** | **58.1** | 46.7 | 44.1 | Hallucination resistance (Hallucination Rate: 30.6% vs 41.7% for Qwen). | | **Video-MME** | **59.7** | 48.9 | 56.2 | Video understanding. | | **MUIRBench** | **60.2** | 41.8 | 46.6 | Multi-image reasoning. | *Source: Official Hugging Face model card (https://huggingface.co/openbmb/MiniCPM-V-4.6)* ### Key Efficiency Metrics - **Token Throughput**: ~1.5x faster than Qwen3.5-0.8B. - **Visual Encoding FLOPs**: Reduced by >50% via LLaVA-UHD v4 technique. - **Mixed Compression**: Supports 4x (high detail) and 16x (high efficiency) modes.

Detailed Comparison

Community Feedback

The developer and research community reaction has been notably positive, particularly within the efficient AI and edge-computing spheres. The model's GitHub repository (`OpenBMB/MiniCPM-V`) has garnered significant interest (25,887 stars), and its integration into frameworks like Ollama's official library is cited as a major milestone for accessibility. A standout community example is its integration into **StarVLA**, a Vision-Language-Action framework for robot manipulation. A developer (`shaohua-pan`) shared results using MiniCPM-V 4.6 as a backbone, achieving a 95.6% success rate on the LIBERO benchmark, demonstrating its potential as a lightweight brain for embodied AI. The OpenBMB team actively engages with such feedback, highlighting a focus on real-world applications. On platforms like Hugging Face Spaces, numerous demos (99 listed) showcase its capabilities. The release of a free public API key further lowered the barrier to experimentation. The sentiment is that while it's not a frontier model, it delivers remarkable 'punch-for-its-size,' making advanced multimodal AI accessible for on-device innovation beyond just research labs.

Use Cases

### 1. Mobile Document & Receipt Scanning **Use Case:** Building an app that lets users photograph a restaurant receipt, menu, or complex form to automatically extract and digitize information (prices, item names, totals). **Why MiniCPM-V 4.6?** Its exceptionally high score on OmniDocBench (84.6) indicates strong OCR and document structure understanding. It can run locally on the phone, preserving user privacy, with low latency in 16x compression mode for quick scans. ### 2. Real-Time On-Device Visual Q&A for Accessibility **Use Case:** A wearable or phone app for visually impaired users that can describe scenes, read signs, or identify objects in real-time via the camera feed. **Why MiniCPM-V 4.6?** The mixed compression modes are key: 16x for quick, general descriptions, and 4x for detailed text reading. Its support for video understanding enables continuous processing, and its edge deployment ensures functionality without an internet connection. ### 3. Lightweight Robotics & Embodied AI **Use Case:** Serving as the vision-language 'brain' in a small robot or drone for tasks like object sorting, navigation, or simple manipulation based on verbal commands. **Why MiniCPM-V 4.6?** As demonstrated by the StarVLA community project, its compact size and efficiency allow it to run on robot-embedded GPUs (like NVIDIA Jetson). It can interpret visual scenes and follow instructions without offloading to the cloud, reducing latency and dependency. ### 4. Privacy-Preserving Photo Album Analysis **Use Case:** A desktop or mobile app that organizes a user's personal photo library by automatically tagging people, locations, objects, and even generating captions—all processed locally. **Why MiniCPM-V 4.6?** Its multi-image understanding capability is perfect for analyzing photo batches. Running entirely on-device ensures sensitive personal photos never leave the user's hardware, addressing a major privacy concern.

Latest News

Recent developments are centered around expanded ecosystem integration and accessibility: - **[June 25, 2026]** MiniCPM-V 4.6 was merged into **Ollama's official model library**, making local installation and experimentation via a simple `ollama run` command trivially easy for any user. - **[May 17, 2026]** The team released an official **API service** for MiniCPM-V 4.6, along with a **public free API key**, significantly lowering the barrier for developers to test its capabilities in cloud applications without self-hosting. - **[May 11, 2026]** Initial release of the model with its core innovations: mixed 4x/16x visual token compression, LLaVA-UHD v4 architecture, and full open-sourcing of edge deployment code for iOS, Android, and HarmonyOS. The overarching trend is making this highly efficient model accessible through multiple channels: open weights for self-hosting, quantized variants for different hardware, API for easy cloud integration, and integration with popular local tools like Ollama.

Positioned as a 'pocket-sized MLLM', the model prioritizes practical on-device capabilities over raw cloud-scale intelligence. It supports single-image, multi-image, and video understanding, with strong results in OCR and document parsing (e.g., 84.6 on OmniDocBench). Its design philosophy is efficiency-first, enabling deployment on mainstream mobile platforms (iOS, Android, HarmonyOS) with low VRAM requirements (4GB GPU, 2GB CPU via GGUF). This makes it a compelling open-source option for developers building vision-language features directly into consumer applications.

While it cannot match the absolute performance of frontier cloud models like GPT-5 or Gemini 2.5 Flash, MiniCPM-V 4.6 carves a unique niche by bringing credible multimodal understanding to resource-constrained environments. It represents a significant step towards practical, privacy-preserving AI on personal devices, backed by a comprehensive developer ecosystem including multiple quantization formats and support for major inference frameworks like vLLM, llama.cpp, and Ollama.

Sources

Analysis generated: 2026-07-17