Ultra-efficient multimodal large language model for image and video understanding on mobile devices.
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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Exceptional efficiency for edge deployment, especially on mobile devices.
Biggest con
torchcodec (used for video decoding) may have CUDA compatibility issues with certain environments, requiring workarounds.
TL;DR - MiniCPM-V 4.6
- Ultra-efficient MLLM for image and video understanding.
- Optimized for mobile deployment across iOS, Android, and HarmonyOS.
- Achieves high performance with significantly reduced computational cost.
Pricing: Free plan available
Best for: Growing teams
What is MiniCPM-V 4.6?
MiniCPM-V 4.6 is a highly optimized multimodal large language model (MLLM) designed for efficient image and video understanding, particularly on edge devices like smartphones. Built upon SigLIP2-400M and the Qwen3.5-0.8B LLM, it offers strong capabilities in single-image, multi-image, and video analysis while significantly reducing computational overhead. This model is ideal for developers and organizations looking to integrate advanced visual understanding into mobile applications with high performance and low resource consumption.
The model boasts leading foundation and multimodal capabilities, outperforming larger models in benchmarks with significantly fewer token costs. Its ultra-efficient architecture, based on LLaVA-UHD v4, reduces visual encoding computation FLOPs by over 50%, leading to faster token throughput. MiniCPM-V 4.6 supports broad mobile platform coverage (iOS, Android, HarmonyOS) and is developer-friendly, with open-sourced edge adaptation code, support for popular inference frameworks like vLLM and llama.cpp, and fine-tuning ecosystems like SWIFT and LLaMA-Factory. It also provides multiple quantized variants for flexible deployment.
Available on: Web
Pros & Cons
Pros
- Exceptional efficiency for edge deployment, especially on mobile devices.
- Strong multimodal capabilities, outperforming larger models in various benchmarks.
- Broad compatibility with mainstream mobile operating systems.
- Developer-friendly with extensive framework and fine-tuning support.
- Open-sourced edge adaptation code for easy reproduction of on-device experience.
Cons
- torchcodec (used for video decoding) may have CUDA compatibility issues with certain environments, requiring workarounds.
- Requires technical expertise for deployment and fine-tuning.
Key Features
Single-image understandingMulti-image understandingVideo understandingMixed 4x/16x visual token compressionDeployment on iOS, Android, and HarmonyOSSupport for vLLM, SGLang, llama.cpp, Ollama inference frameworksSupport for SWIFT and LLaMA-Factory fine-tuning ecosystemsMultiple quantized variants (GGUF, BNB, AWQ, GPTQ)
Pricing Plans
Pricing checked Jun 21, 2026
Free
Free
- Explore, experiment, collaborate and build technology with Machine Learning
- Packed with ML features, like model eval, dataset viewer
- Git based and designed for collaboration
- Learn by experimenting and sharing with community
- Build your ML portfolio
- Share your work with the world and build your own ML profile
- Spaces Hardware: CPU Basic (2 vCPU, 16 GB Memory)
- ZeroGPU (dynamic, Nvidia H200, 70 GB VRAM)
PRO Account
$9 / month
- 10x private storage capacity
- 2x public storage capacity
- 20x included inference credits
- 8x ZeroGPU quota and highest queue priority
- Spaces Dev Mode & ZeroGPU Spaces hosting
- Personal blog publishing
- Dataset Viewer for private datasets
- Show your support with a PRO badge
Team
$20 / user per month
- Instant setup for growing teams
- SSO support (SAML & OIDC)
- Data location control with Storage Regions
- Detailed action reviews with Audit Logs
- Granular access control via Resource Groups
- Repository usage Analytics
- Advanced auth policies and repository visibility controls
- Centralized token control and approvals
Enterprise
Starting at $50 per user per month
- All benefits from the Team plan
- Highest storage, bandwidth, and API rate limits
- Automated user management with SCIM provisioning
- Advanced security and access controls
- Managed billing with annual commitments
- Legal and Compliance processes
- Dedicated support
Reviews
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MiniCPM-V 4.6 FAQ
How does MiniCPM-V 4.6 achieve its high efficiency compared to other models?
MiniCPM-V 4.6 achieves its high efficiency through an ultra-efficient architecture based on LLaVA-UHD v4, which reduces visual encoding computation FLOPs by over 50%. This allows it to achieve approximately 1.5 times greater token throughput compared to models like Qwen3.5-0.8B, even while maintaining strong performance.
What specific mobile platforms does MiniCPM-V 4.6 support for deployment?
MiniCPM-V 4.6 is designed for broad mobile platform coverage and can be deployed across all three mainstream mobile platforms: iOS, Android, and HarmonyOS. The edge adaptation code is open-sourced to facilitate this deployment.
Can MiniCPM-V 4.6 be fine-tuned for custom tasks, and what tools are supported for this?
Yes, MiniCPM-V 4.6 supports fine-tuning for new domains and tasks. It is compatible with popular fine-tuning ecosystems such as SWIFT and LLaMA-Factory, allowing developers to customize models using consumer-grade GPUs.
What are the options for handling CUDA compatibility issues with torchcodec during installation?
If you encounter CUDA compatibility issues with torchcodec, there are two main workarounds: either replace torchcodec with PyAV (which supports both image and video inference without CUDA version constraints) or pin the CUDA version when installing PyTorch to match your environment (e.g.,
pip install "transformers>=5.7.0" torchvision torchcodec --index-url https://download.pytorch.org/whl/cu128).How does MiniCPM-V 4.6's visual token compression work, and what are its benefits?
MiniCPM-V 4.6 introduces mixed 4x/16x visual token compression. This feature allows for flexible switching between accuracy and speed, enabling users to choose a compression rate that best suits their application's requirements for detail preservation versus processing efficiency.
Source: huggingface.co