Empowering innovation: The subsequent era of the Phi household

We’re excited to announce Phi-4-multimodal and Phi-4-mini, the most recent fashions in Microsoft’s Phi household of small language fashions (SLMs). These fashions are designed to empower builders with superior AI capabilities.

We’re excited to announce Phi-4-multimodal and Phi-4-mini, the most recent fashions in Microsoft’s Phi household of small language fashions (SLMs). These fashions are designed to empower builders with superior AI capabilities. Phi-4-multimodal, with its means to course of speech, imaginative and prescient, and textual content concurrently, opens new potentialities for creating modern and context-aware purposes. Phi-4-mini, then again, excels in text-based duties, offering excessive accuracy and scalability in a compact kind. Now obtainable in Azure AI Foundry, HuggingFace, and the NVIDIA API Catalog the place builders can discover the complete potential of Phi-4-multimodal on the NVIDIA API Catalog, enabling them to experiment and innovate with ease. 

What’s Phi-4-multimodal?

Phi-4-multimodal marks a brand new milestone in Microsoft’s AI growth as our first multimodal language mannequin. On the core of innovation lies steady enchancment, and that begins with listening to our prospects. In direct response to buyer suggestions, we’ve developed Phi-4-multimodal, a 5.6B parameter mannequin, that seamlessly integrates speech, imaginative and prescient, and textual content processing right into a single, unified structure.

By leveraging superior cross-modal studying methods, this mannequin permits extra pure and context-aware interactions, permitting units to grasp and motive throughout a number of enter modalities concurrently. Whether or not deciphering spoken language, analyzing pictures, or processing textual info, it delivers extremely environment friendly, low-latency inference—all whereas optimizing for on-device execution and diminished computational overhead.

Natively constructed for multimodal experiences

Phi-4-multimodal is a single mannequin with mixture-of-LoRAs that features speech, imaginative and prescient, and language, all processed concurrently inside the identical illustration area. The result’s a single, unified mannequin able to dealing with textual content, audio, and visible inputs—no want for advanced pipelines or separate fashions for various modalities.

The Phi-4-multimodal is constructed on a brand new structure that enhances effectivity and scalability. It incorporates a bigger vocabulary for improved processing, helps multilingual capabilities, and integrates language reasoning with multimodal inputs. All of that is achieved inside a robust, compact, extremely environment friendly mannequin that’s fitted to deployment on units and edge computing platforms.

This mannequin represents a step ahead for the Phi household of fashions, providing enhanced efficiency in a small package deal. Whether or not you’re searching for superior AI capabilities on cell units or edge programs, Phi-4-multimodal gives a high-capability choice that’s each environment friendly and versatile.

Unlocking new capabilities

With its elevated vary of capabilities and suppleness, Phi-4-multimodal opens thrilling new potentialities for app builders, companies, and industries seeking to harness the facility of AI in modern methods. The way forward for multimodal AI is right here, and it’s prepared to remodel your purposes.

Phi-4-multimodal is able to processing each visible and audio collectively. The next desk reveals the mannequin high quality when the enter question for imaginative and prescient content material is artificial speech on chart/desk understanding and doc reasoning duties. In comparison with different present state-of-the-art omni fashions that may allow audio and visible alerts as enter, Phi-4-multimodal achieves a lot stronger efficiency on a number of benchmarks.

Phi-4-multimodal has demonstrated outstanding capabilities in speech-related duties, rising as a number one open mannequin in a number of areas. It outperforms specialised fashions like WhisperV3 and SeamlessM4T-v2-Massive in each automated speech recognition (ASR) and speech translation (ST). The mannequin has claimed the highest place on the Huggingface OpenASR leaderboard with a powerful phrase error charge of 6.14%, surpassing the earlier finest efficiency of 6.5% as of February 2025. Moreover, it’s amongst a number of open fashions to efficiently implement speech summarization and obtain efficiency ranges akin to GPT-4o mannequin. The mannequin has a niche with shut fashions, akin to Gemini-2.0-Flash and GPT-4o-realtime-preview, on speech query answering (QA) duties because the smaller mannequin dimension leads to much less capability to retain factual QA information. Work is being undertaken to enhance this functionality within the subsequent iterations.

Phi-4-multimodal with solely 5.6B parameters demonstrates outstanding imaginative and prescient capabilities throughout numerous benchmarks, most notably reaching robust efficiency on mathematical and science reasoning. Regardless of its smaller dimension, the mannequin maintains aggressive efficiency on common multimodal capabilities, akin to doc and chart understanding, Optical Character Recognition (OCR), and visible science reasoning, matching or exceeding shut fashions like Gemini-2-Flash-lite-preview/Claude-3.5-Sonnet.

What’s Phi-4-mini?

Phi-4-mini is a 3.8B parameter mannequin and a dense, decoder-only transformer that includes grouped-query consideration, 200,000 vocabulary, and shared input-output embeddings, designed for velocity and effectivity. Regardless of its compact dimension, it continues outperforming bigger fashions in text-based duties, together with reasoning, math, coding, instruction-following, and function-calling. Supporting sequences as much as 128,000 tokens, it delivers excessive accuracy and scalability, making it a robust answer for superior AI purposes.

To grasp the mannequin high quality, we evaluate Phi-4-mini with a set of fashions over a wide range of benchmarks as proven in Determine 4.

Operate calling, instruction following, lengthy context, and reasoning are highly effective capabilities that allow small language fashions like Phi-4-mini to entry exterior information and performance regardless of their restricted capability. By means of a standardized protocol, operate calling permits the mannequin to seamlessly combine with structured programming interfaces. When a consumer makes a request, Phi-4-Mini can motive by way of the question, determine and name related features with applicable parameters, obtain the operate outputs, and incorporate these outcomes into its responses. This creates an extensible agentic-based system the place the mannequin’s capabilities will be enhanced by connecting it to exterior instruments, utility program interfaces (APIs), and information sources by way of well-defined operate interfaces. The next instance simulates a sensible dwelling management agent with Phi-4-mini.

At Headwaters, we’re leveraging fine-tuned SLM like Phi-4-mini on the sting to boost operational effectivity and supply modern options. Edge AI demonstrates excellent efficiency even in environments with unstable community connections or in fields the place confidentiality is paramount. This makes it extremely promising for driving innovation throughout numerous industries, together with anomaly detection in manufacturing, fast diagnostic assist in healthcare, and enhancing buyer experiences in retail. We’re wanting ahead to delivering new options within the AI agent period with Phi-4 mini.
 
—Masaya Nishimaki, Firm Director, Headwaters Co., Ltd. 

Customization and cross-platform

Due to their smaller sizes, Phi-4-mini and Phi-4-multimodal fashions can be utilized in compute-constrained inference environments. These fashions can be utilized on-device, particularly when additional optimized with ONNX Runtime for cross-platform availability. Their decrease computational wants make them a decrease price choice with a lot better latency. The longer context window permits taking in and reasoning over massive textual content content material—paperwork, net pages, code, and extra. Phi-4-mini and multimodal demonstrates robust reasoning and logic capabilities, making it candidate for analytical duties. Their small dimension additionally makes fine-tuning or customization simpler and extra inexpensive. The desk beneath reveals examples of finetuning situations with Phi-4-multimodal.

For extra details about customization or to study extra in regards to the fashions, check out Phi Cookbook on GitHub. 

How can these fashions be utilized in motion?

These fashions are designed to deal with advanced duties effectively, making them ideally suited for edge case situations and compute-constrained environments. Given the brand new capabilities Phi-4-multimodal and Phi-4-mini carry, the makes use of of Phi are solely increasing. Phi fashions are being embedded into AI ecosystems and used to discover numerous use instances throughout industries.

Language fashions are highly effective reasoning engines, and integrating small language fashions like Phi into Home windows permits us to take care of environment friendly compute capabilities and opens the door to a way forward for steady intelligence baked in throughout all of your apps and experiences. Copilot+ PCs will construct upon Phi-4-multimodal’s capabilities, delivering the facility of Microsoft’s superior SLMs with out the vitality drain. This integration will improve productiveness, creativity, and education-focused experiences, changing into an ordinary a part of our developer platform.

—Vivek Pradeep, Vice President Distinguished Engineer of Home windows Utilized Sciences.

1. Embedded on to your good system: Telephone producers integrating Phi-4-multimodal straight right into a smartphone might allow smartphones to course of and perceive voice instructions, acknowledge pictures, and interpret textual content seamlessly. Customers may benefit from superior options like real-time language translation, enhanced picture and video evaluation, and clever private assistants that perceive and reply to advanced queries. This is able to elevate the consumer expertise by offering highly effective AI capabilities straight on the system, making certain low latency and excessive effectivity.
2. On the highway: Think about an automotive firm integrating Phi-4-multimodal into their in-car assistant programs. The mannequin might allow automobiles to grasp and reply to voice instructions, acknowledge driver gestures, and analyze visible inputs from cameras. For example, it might improve driver security by detecting drowsiness by way of facial recognition and offering real-time alerts. Moreover, it might provide seamless navigation help, interpret highway indicators, and supply contextual info, making a extra intuitive and safer driving expertise whereas linked to the cloud and offline when connectivity isn’t obtainable.
3. Multilingual monetary companies: Think about a monetary companies firm integrating Phi-4-mini to automate advanced monetary calculations, generate detailed experiences, and translate monetary paperwork into a number of languages. For example, the mannequin can help analysts by performing intricate mathematical computations required for danger assessments, portfolio administration, and monetary forecasting. Moreover, it will possibly translate monetary statements, regulatory paperwork, and consumer communications into numerous languages and will enhance consumer relations globally.

Microsoft’s dedication to safety and security

Azure AI Foundry gives customers with a strong set of capabilities to assist organizations measure, mitigate, and handle AI dangers throughout the AI growth lifecycle for conventional machine studying and generative AI purposes. Azure AI evaluations in AI Foundry allow builders to iteratively assess the standard and security of fashions and purposes utilizing built-in and customized metrics to tell mitigations.

Each fashions underwent safety and security testing by our inside and exterior safety consultants utilizing methods crafted by Microsoft AI Purple Workforce (AIRT). These strategies, developed over earlier Phi fashions, incorporate international views and native audio system of all supported languages. They span areas akin to cybersecurity, nationwide safety, equity, and violence, addressing present tendencies by way of multilingual probing. Utilizing AIRT’s open-source Python Threat Identification Toolkit (PyRIT) and guide probing, crimson teamers carried out single-turn and multi-turn assaults. Working independently from the event groups, AIRT constantly shared insights with the mannequin workforce. This method assessed the brand new AI safety and security panorama launched by our newest Phi fashions, making certain the supply of high-quality capabilities.

Check out the mannequin playing cards for Phi-4-multimodal and Phi-4-mini, and the technical paper to see an overview of really useful makes use of and limitations for these fashions.

Be taught extra about Phi-4

We invite you to come back discover the chances with Phi-4-multimodal and Phi-4-mini in Azure AI Foundry, Hugging Face, and NVIDIA API Catalog with a full multimodal expertise. We are able to’t wait to listen to your suggestions and see the unbelievable issues you’ll accomplish with our new fashions.