MiniCPM-V 4.0 and MiniCPM-o 2.6: Bringing GPT-4o-Level Multimodal AI to Your Smartphone

In today’s rapidly evolving AI landscape, multimodal models are transforming how we interact with technology. These sophisticated systems can understand and process multiple forms of information—text, images, audio, and video—creating more natural and intuitive user experiences. However, the most powerful multimodal models typically require substantial computational resources, limiting their practical application on everyday devices.

What if you could run a state-of-the-art multimodal AI directly on your smartphone, without relying on cloud services? This is precisely what MiniCPM-V 4.0 and MiniCPM-o 2.6 deliver—a breakthrough in on-device multimodal AI that brings GPT-4o-level capabilities to your pocket.

Why On-Device Multimodal AI Matters

Before diving into the technical details, let’s address a fundamental question: why should we care about running multimodal models directly on smartphones and other edge devices?

While cloud-based AI services have dominated the market, they come with several significant limitations:

Privacy concerns: Uploading personal photos, videos, or voice recordings to remote servers creates potential privacy risks
Network dependency: Without reliable internet connectivity, cloud-based AI becomes unusable
Latency issues: The round-trip to cloud servers introduces noticeable delays in response times
Cost considerations: Frequent API calls to commercial services can become expensive at scale

On-device AI solves these problems elegantly. Imagine being able to instantly identify a plant during a hike, translate street signs while traveling abroad, or have a private conversation with your AI assistant—all without sending your data to external servers. This is the promise of MiniCPM-V 4.0 and MiniCPM-o 2.6.

MiniCPM-V 4.0: The Efficient Vision Understanding Specialist

MiniCPM-V 4.0 represents the latest advancement in the MiniCPM-V series, built on SigLIP2-400M and MiniCPM4-3B with a total parameter count of just 4.1 billion. Despite its relatively compact size, this model delivers exceptional performance that rivals—and sometimes surpasses—larger models.

Why MiniCPM-V 4.0 Stands Out

1. Exceptional visual capabilities

MiniCPM-V 4.0 achieves an impressive average score of 69.0 on the OpenCompass benchmark, outperforming both the larger MiniCPM-V 2.6 (8.1B parameters, score of 65.2) and Qwen2.5-VL-3B-Instruct (3.8B parameters, score of 64.5). Remarkably, it even surpasses the widely-used closed-source model GPT-4.1-mini-20250414 in several key areas.

Model	Parameters	OpenCompass	OCRBench	MathVista	HallusionBench	MMMU
MiniCPM-V 4.0	4.1B	69.0	894	66.9	50.8	51.2
Qwen2.5-VL-3B-Instruct	3.8B	64.5	828	61.2	46.6	51.2
GPT-4.1-mini-20250414	–	68.9	840	70.9	49.3	55.0

2. Optimized for real-world performance

MiniCPM-V 4.0 is specifically engineered for edge devices. Real-world testing shows it runs smoothly on an iPhone 16 Pro Max with a first-token latency of just 2 seconds and a decoding speed of 17.9 tokens per second—without causing the device to overheat. This level of performance is exceptional for a model capable of handling complex visual tasks.

3. Flexible deployment options

Unlike many specialized AI models, MiniCPM-V 4.0 supports multiple inference frameworks, making it accessible to developers and users with varying technical expertise:

llama.cpp
Ollama
vLLM
SGLang
LLaMA-Factory
Local Web Demo

This versatility ensures that whether you’re a developer building applications or a curious user wanting to experiment, there’s an approach that works for your needs.

MiniCPM-o 2.6: The Complete Multimodal Powerhouse

If MiniCPM-V 4.0 excels at visual understanding, MiniCPM-o 2.6 is the comprehensive solution for full multimodal interaction. As the latest model in the MiniCPM-o series, it processes images, video, text, and audio inputs while generating both text and speech outputs—all through an end-to-end architecture.

Key Advantages of MiniCPM-o 2.6

1. Industry-leading visual understanding

MiniCPM-o 2.6 achieves an OpenCompass average score of 70.2, outperforming major commercial models like GPT-4o-202405, Gemini 1.5 Pro, and Claude 3.5 Sonnet in single-image understanding tasks—despite having only 8.7 billion parameters.

Model	Parameters	OpenCompass	OCRBench	MathVista	Video-MME (w subs)
MiniCPM-o 2.6	8.7B	70.2	889	73.3	69.6
GPT-4o-20240513	–	–	–	–	77.2
Gemini-1.5-Pro	–	64.5	754	58.3	81.3

2. Advanced speech capabilities

MiniCPM-o 2.6 supports configurable voice output for both English and Chinese, featuring:

Control over emotion, speaking rate, and style
End-to-end voice cloning capabilities
Character role-playing functionality
Speech recognition and translation performance exceeding GPT-4o-realtime

3. Revolutionary multimodal streaming interaction

One of MiniCPM-o 2.6’s most innovative features is its ability to accept continuous video and audio streams while engaging in real-time voice conversations. In StreamingBench evaluations, it achieves the best results in the open-source community and surpasses both GPT-4o-202408 and Claude 3.5 Sonnet.

Model	Real-Time Video Understanding	Omni-Source Understanding	Contextual Understanding	Overall Score
MiniCPM-o 2.6	79.9	53.4	38.5	66.0
GPT-4o-202408	74.5	51.0	48.0	64.1
Gemini 1.5 Pro	77.4	67.8	51.1	70.3

4. Enhanced OCR capabilities

MiniCPM-o 2.6 can process images of any aspect ratio with up to 1.8 million pixels (such as 1344×1344). It achieves the best results among models under 25 billion parameters on OCRBench, outperforming commercial models like GPT-4o-202405.

Practical Implementation: Getting Started with MiniCPM

Now that we’ve covered the theoretical advantages, let’s explore how you can actually use these models in real-world scenarios.

1. Quick Local Setup (For Beginners)

If you’re new to AI models, the simplest way to get started is through the Gradio Web Demo:

# Install required dependencies
pip install -r requirements.txt

# Launch the web interface
python web_demo.py

Once running, navigate to localhost:7860 in your browser to interact with the model. You can upload images, videos, or audio files directly through the intuitive interface.

2. Mobile Device Deployment (iOS)

For iPhone and iPad users, both MiniCPM-V 4.0 and MiniCPM-o 2.6 can run directly on your device:

# Using llama.cpp for deployment
git clone https://github.com/OpenBMB/llama.cpp
cd llama.cpp
git checkout minicpmv-main
make
./main -m ./models/minicpm-v-4.0.Q4_K_M.gguf -p "Describe this image" -i

Testing on an iPad Pro (M4) shows MiniCPM-V 4.0 achieves 16-18 tokens per second, enabling smooth performance across various multimodal tasks without draining your battery.

3. High-Performance Server Deployment

For applications requiring high concurrency, vLLM offers an excellent deployment solution:

from vllm import LLM, SamplingParams

# Initialize the model
llm = LLM(model="openbmb/MiniCPM-o-2.6", 
          image_token_id=151645,
          video_token_id=151646,
          audio_token_id=151647)

# Configure generation parameters
sampling_params = SamplingParams(temperature=0.7, max_tokens=512)

# Process multimodal input
messages = [
    {"role": "user", "content": [
        {"type": "image", "image": "path/to/image.jpg"},
        {"type": "text", "text": "Please describe this image"}
    ]}
]

# Generate response
outputs = llm.chat(messages, sampling_params=sampling_params)
print(outputs[0].text)

4. Voice Interaction Configuration

MiniCPM-o 2.6’s voice capabilities require specific setup:

# Initialize the model
model = MiniCPMModel.from_pretrained("openbmb/MiniCPM-o-2.6")

# Process audio input
audio_input, _ = librosa.load('user_audio.wav', sr=16000, mono=True)

# Construct message
msgs = [{'role': 'user', 'content': audio_input}]

# Generate response with audio output
res = model.chat(
    msgs=msgs,
    tokenizer=tokenizer,
    generate_audio=True,
    output_audio_path='response.wav',
)

Customization: Fine-Tuning for Specific Applications

While the pre-trained models offer impressive capabilities, you might need to adapt them for specialized use cases. Here are several fine-tuning approaches:

1. Using Transformers Library (For Hugging Face Users)

Ideal for developers familiar with the Hugging Face ecosystem:

from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM-o-2.6")
tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM-o-2.6")

# Configure training parameters
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    logging_dir="./logs",
)

# Create Trainer instance
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Start fine-tuning process
trainer.train()

2. Using LLaMA-Factory (No-Code Option)

LLaMA-Factory provides a user-friendly web interface for fine-tuning:

Install LLaMA-Factory: pip install llama-factory
Launch the web interface: llamafactory-cli webui
Access localhost:7860 through your browser
Select the MiniCPM-o 2.6 model
Configure training parameters and begin fine-tuning

3. Using Align-Anything Framework (Advanced Alignment)

For researchers focusing on model alignment:

# Clone the repository
git clone https://github.com/PKU-Alignment/align-anything

# Navigate to script directory
cd align-anything/scripts

# Run MiniCPM-o 2.6 fine-tuning script
bash minicpm-o-2.6/sft.sh

Understanding the Technical Architecture

To appreciate what makes MiniCPM models special, it’s helpful to understand their underlying architecture:

End-to-End Multimodal Framework

Unlike traditional multimodal models that process different modalities separately, MiniCPM-o 2.6 employs an end-to-end architecture that connects and trains various modality encoders/decoders together. This unified approach allows the model to leverage rich multimodal knowledge more effectively.

Streaming Multimodal Mechanism

The model transforms offline encoders/decoders into online modules suitable for streaming input/output. It implements a time-division multiplexing mechanism for multimodal information processing, breaking parallel data streams into periodic time slices that the language model can process sequentially.

Configurable Voice System

MiniCPM-o 2.6 introduces a novel multimodal system prompt that includes both traditional text instructions and voice-specific parameters. This enables flexible control over voice characteristics during inference, supporting advanced capabilities like end-to-end voice cloning and creation.

Performance Deep Dive: What the Numbers Really Mean

Let’s examine the evaluation results more closely to understand what these models can actually do in practical scenarios.

Visual Understanding Capabilities

MiniCPM-V 4.0 and MiniCPM-o 2.6 excel across multiple visual understanding benchmarks:

Task	Model	Size	ChartQA	MME	RealWorldQA	TextVQA	DocVQA	MathVision	DynaMath	WeMath	Obj Hal	MM Hal	Score Avg
Open-source	MiniCPM-o-2.6	8.7B	86.9	2372	68.1	82.0	93.5	21.7	10.4	25.2	6.3	3.4	31.3
Open-source	MiniCPM-V-4.0	4.1B	84.4	2298	68.5	80.8	92.9	20.7	14.2	32.7	6.3	3.5	29.2

These scores translate to real-world capabilities like accurately reading documents, solving mathematical problems presented visually, and understanding complex charts and graphs.

Speech Processing Performance

MiniCPM-o 2.6 sets new standards for open-source speech processing:

Task	Size	ASR (zh)	ASR (en)	AST	Emotion
Metric		CER↓	WER↓	BLEU↑	ACC↑
MiniCPM-o 2.6	8B	1.6	4.4	6.9	1.7

Compared to proprietary models:

Chinese speech recognition: 1.6% Character Error Rate (better than GPT-4o’s 7.3%)
English speech recognition: 4.4% Word Error Rate (better than GPT-4o’s 5.4%)
Speech translation: 6.9 BLEU score
Emotion recognition: 1.7 accuracy score

Speech Generation Quality

When it comes to generating speech, MiniCPM-o 2.6 delivers impressive results:

Model	Size	SIMO↑ (zh)	SIMO↑ (en)
F5-TTS	–	76	67
CosyVoice	–	75	64
MiniCPM-o 2.6	8B	57	47

While not matching the absolute top commercial systems, these scores represent state-of-the-art performance for open-source models, especially considering the model’s ability to run on consumer devices.

Practical Limitations: Setting Realistic Expectations

Despite their impressive capabilities, it’s important to acknowledge current limitations:

Speech output stability: Voice generation can be affected by background noise and meaningless sounds, leading to inconsistent performance
Repetitive responses: When faced with consecutive similar user requests, the model may generate repetitive responses
Web demo latency: Remote server deployments may experience higher latency compared to local execution
Complex scenario limitations: Highly specialized or domain-specific tasks may exceed the model’s current capabilities

These limitations reflect the current state of on-device multimodal AI rather than specific shortcomings of the MiniCPM models. As the technology evolves, we can expect these constraints to gradually diminish.

Frequently Asked Questions

What’s the difference between MiniCPM-V 4.0 and MiniCPM-o 2.6?

MiniCPM-V 4.0 focuses specifically on efficient single-image, multi-image, and video understanding with a compact 4.1B parameter count, making it ideal for resource-constrained edge devices. MiniCPM-o 2.6 (8.7B parameters) is a comprehensive multimodal model that adds speech input/output capabilities and real-time streaming interaction, offering broader functionality at the cost of higher resource requirements.

Can these models run on my smartphone?

Yes! MiniCPM-V 4.0 is specifically optimized for edge devices. Real-world testing shows it achieves first-token latency of just 2 seconds and decoding speeds of 17.9 tokens per second on an iPhone 16 Pro Max. For older devices, we recommend using quantized versions (like int4 or gguf formats) which have lower memory requirements.

How can I obtain the model weights?

The MiniCPM model weights are openly available for academic research. For commercial use, you’ll need to complete a registration form. You can find the models on Hugging Face Model Hub:

Which languages does the model support?

MiniCPM-o 2.6 supports English, Chinese, German, French, Italian, Korean, and over 30 additional languages. This multilingual capability makes it suitable for international applications without requiring separate language-specific models.

How can I improve unstable voice output?

To enhance speech generation stability:

Ensure clear audio input with minimal background noise
Adjust the temperature parameter (recommended range: 0.3-0.5)
Include explicit voice style specifications in system prompts
For critical applications, consider local deployment rather than remote services

Can these models process video content?

Absolutely! Both MiniCPM-V 2.6 and MiniCPM-o 2.6 support video understanding, with MiniCPM-o 2.6 additionally enabling real-time video stream processing for continuous visual conversations. Here’s a basic implementation:

# Video processing example
contents = get_video_chunk_content(video_path, flatten=False)
session_id = '123'

# Pre-fill system prompt
model.streaming_prefill(session_id=session_id, msgs=[sys_msg])

# Process video segments sequentially
for content in contents:
    msgs = [{"role": "user", "content": content}]
    model.streaming_prefill(session_id=session_id, msgs=msgs)

# Generate final response
res = model.streaming_generate(
    session_id=session_id,
    generate_audio=True
)

Real-World Applications and Implementation Strategies

The true value of MiniCPM models lies in their practical applications. Let’s explore how different user groups can leverage these technologies:

Individual Users: Personal AI Assistance

For everyday users, MiniCPM models enable private, offline AI assistance:

Document processing: Extract text from photos of documents without uploading to cloud services
Travel assistance: Real-time translation of signs and menus while traveling
Educational support: Visual problem-solving for math and science homework
Accessibility tools: Helping visually impaired users understand their surroundings

Enterprise Applications: Scalable Solutions

Businesses can implement MiniCPM models for:

Customer service: Privacy-preserving visual assistance without sending customer images to external servers
Document processing: Secure handling of sensitive documents with built-in OCR capabilities
Retail applications: In-store visual search and product information without relying on internet connectivity
Field service: Technical support for remote locations with limited network access

Research Applications: Building on Open Foundations

Researchers can use MiniCPM models as:

Baseline systems: For developing new multimodal techniques
Privacy-focused AI: Studying on-device processing without data leakage
Resource-efficient models: Exploring techniques for model compression and optimization
Multilingual studies: Investigating cross-lingual transfer in multimodal contexts

Getting Started with Your Own Implementation

To help you begin your journey with MiniCPM models, here’s a step-by-step guide for different scenarios:

For Web Developers

If you’re building web applications, consider these deployment options:

FastAPI integration: Create a RESTful API for multimodal processing
WebAssembly deployment: Run models directly in browsers using WebAssembly
Hybrid approach: Use on-device processing for basic tasks with cloud fallback for complex requests

For Mobile App Developers

For iOS and Android applications:

Native integration: Use platform-specific frameworks (Core ML for iOS, NNAPI for Android)
Cross-platform: Implement using Flutter or React Native with native modules
Progressive enhancement: Start with basic functionality, adding advanced features as device capabilities allow

For Enterprise Solutions

When building scalable business applications:

vLLM deployment: For high-throughput server environments
Quantization strategies: Balance performance and accuracy with different quantization levels
Load balancing: Distribute processing between edge devices and central servers

The Future of On-Device Multimodal AI

MiniCPM-V 4.0 and MiniCPM-o 2.6 represent a significant milestone in making advanced AI capabilities accessible on everyday devices. As these technologies evolve, we can expect:

Improved efficiency: Even better performance on lower-end devices
Broader modality support: Integration of additional sensory inputs
Enhanced personalization: Models that adapt to individual users over time
Tighter ecosystem integration: Seamless interaction with other device features

Most importantly, the open nature of these models ensures that innovation isn’t limited to well-funded organizations. Developers, researchers, and enthusiasts worldwide can contribute to and benefit from this technology, accelerating progress for everyone.

Conclusion: Bringing Advanced AI to Everyone

The development of MiniCPM-V 4.0 and MiniCPM-o 2.6 demonstrates that high-performance multimodal AI doesn’t require massive server farms—it can run efficiently on the devices we carry in our pockets every day. By prioritizing on-device processing, these models address critical concerns around privacy, latency, and accessibility that have limited the practical adoption of AI technologies.

What makes this particularly exciting is that these models are open source, inviting collaboration and innovation from the global developer community. Whether you’re a researcher pushing the boundaries of AI, a developer building practical applications, or simply someone curious about the technology, these models provide a powerful foundation to explore and build upon.

The true measure of AI’s value isn’t in benchmark scores or parameter counts—it’s in how effectively it solves real problems for real people. With MiniCPM models, we’re taking a significant step toward making advanced multimodal AI genuinely useful, accessible, and respectful of user privacy in everyday contexts.

MiniCPM-V 4.0 and MiniCPM-o 2.6: Revolutionizing On-Device Multimodal AI with GPT-4o-Level Capabilities