Phi-4-multimodal and Phi-4-mini
Microsoft's Phi-4-multimodal (5.6B parameters) and Phi-4-mini (3.8B parameters) are new small language models that deliver powerful multimodal processing and efficient text-based capabilities while requiring minimal computational resources.
https://azure.microsoft.com/en-us/blog/empowering-innovation-the-next-generation-of-the-phi-family?ref=aipure
Product Information
Updated:Jun 16, 2025
Phi-4-multimodal and Phi-4-mini Monthly Traffic Trends
Phi-4-multimodal and Phi-4-mini saw a 3.5% decline in traffic with -245,633 visits in July. This slight decrease could be attributed to the competitive landscape, particularly with Microsoft Azure introducing 25 major announcements at Build 2025, including Azure AI Foundry and enhanced GitHub app for Teams, which may have drawn attention away from these products.
What is Phi-4-multimodal and Phi-4-mini
Phi-4-multimodal and Phi-4-mini are the newest additions to Microsoft's Phi family of small language models (SLMs), designed to empower developers with advanced AI capabilities while maintaining efficiency. Phi-4-multimodal is Microsoft's first multimodal language model that seamlessly integrates speech, vision, and text processing into a single unified architecture, while Phi-4-mini excels at text-based tasks like reasoning, math, coding, and instruction-following. Both models are now available through Azure AI Foundry, Hugging Face, and the NVIDIA API Catalog, making them accessible to developers for building innovative AI applications.
Key Features of Phi-4-multimodal and Phi-4-mini
Phi-4-multimodal (5.6B parameters) and Phi-4-mini (3.8B parameters) are Microsoft's latest small language models designed for efficient AI deployment. Phi-4-multimodal uniquely integrates speech, vision, and text processing in a single architecture, while Phi-4-mini excels at text-based tasks like reasoning, math, and coding. Both models are optimized for compute-constrained environments and can be deployed across cloud, edge, and mobile devices, offering high performance with lower computational requirements.
Unified Multimodal Processing: Phi-4-multimodal integrates speech, vision, and text processing in a single model using mixture-of-LoRAs technology, enabling simultaneous processing of multiple input types without performance degradation
Compact Yet Powerful: Despite their smaller size, both models maintain high performance levels, with Phi-4-mini outperforming larger models in text-based tasks and Phi-4-multimodal matching capabilities of more resource-intensive competitors
Cross-Platform Deployment: Both models can be optimized for various platforms using ONNX Runtime, enabling deployment on edge devices, mobile phones, and cloud environments with efficient resource utilization
Extended Context Processing: Supports processing of up to 128,000 tokens, enabling analysis of large documents and complex contexts while maintaining efficiency
Use Cases of Phi-4-multimodal and Phi-4-mini
Automotive Intelligence: Integration into vehicle systems for voice command processing, driver monitoring, gesture recognition, and real-time navigation assistance, functioning both online and offline
Healthcare Applications: Supporting medical diagnosis through visual analysis, patient history summarization, and rapid diagnostic support while maintaining data privacy in compute-constrained environments
Smart Device Integration: Embedding in smartphones and personal devices for real-time language translation, image analysis, and intelligent personal assistance with low latency
Financial Services: Automating complex financial calculations, generating multilingual reports, and translating financial documents while maintaining high accuracy in computational tasks
Pros
Efficient resource utilization with small model size while maintaining high performance
Versatile deployment options across different computing environments
Strong reasoning and multimodal processing capabilities in a compact form
Cons
Performance gap in speech QA tasks compared to larger models like Gemini-2.0-Flash
May be challenging for smaller businesses to implement and integrate
Limited knowledge retention capacity compared to larger language models
How to Use Phi-4-multimodal and Phi-4-mini
Install Required Dependencies: Install the necessary packages: pip install transformers==4.48.2 flash_attn==2.7.4.post1 torch==2.6.0 accelerate==1.3.0 soundfile==0.13.1 pillow==11.1.0 scipy==1.15.2 torchvision==0.21.0 backoff==2.2.1 peft==0.13.2
Import Required Libraries: Import the necessary Python libraries: import requests, torch, os, io, PIL, soundfile, transformers
Load the Model: Load the model and processor using: model_path = 'microsoft/Phi-4-multimodal-instruct'; processor = AutoProcessor.from_pretrained(model_path); model = AutoModelForCausalLM.from_pretrained(model_path)
Prepare Input: Format your input based on the type - text, image or audio. For text, use the chat format with system and user messages. For images/audio, ensure they are in supported formats
Generate Output: Use the pipeline to generate outputs: pipeline = transformers.pipeline('text-generation', model=model_path); outputs = pipeline(messages, max_new_tokens=128)
Access Through Platforms: Alternatively, access the models through Azure AI Foundry, Hugging Face, or NVIDIA API Catalog platforms which provide user interfaces for model interaction
Optional: Fine-tuning: For customization, use Azure Machine Learning or Azure AI Foundry's no-code fine-tuning capabilities to adapt the model for specific use cases
Deploy: Deploy the model using Azure AI services for production use, or use ONNX Runtime for edge/device deployment with Microsoft Olive for optimization
Phi-4-multimodal and Phi-4-mini FAQs
They are the newest models in Microsoft's Phi family of small language models (SLMs). Phi-4-multimodal is a 5.6B parameter multimodal model that can process speech, vision, and text simultaneously, while Phi-4-mini is a 3.8B parameter model that excels in text-based tasks.
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Analytics of Phi-4-multimodal and Phi-4-mini Website
Phi-4-multimodal and Phi-4-mini Traffic & Rankings
6.7M
Monthly Visits
-
Global Rank
-
Category Rank
Traffic Trends: Jul 2024-Jun 2025
Phi-4-multimodal and Phi-4-mini User Insights
00:01:47
Avg. Visit Duration
1.95
Pages Per Visit
60.86%
User Bounce Rate
Top Regions of Phi-4-multimodal and Phi-4-mini
US: 21.02%
IN: 11.59%
JP: 5.16%
BR: 4.8%
GB: 4.14%
Others: 53.29%
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