We’re excited to announce Phi-4-multimodal and Phi-4-mini, the latest 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 latest 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 capability to course of speech, imaginative and prescient, and textual content concurrently, opens new prospects for creating progressive and context-aware functions. Phi-4-mini, alternatively, excels in text-based duties, offering excessive accuracy and scalability in a compact type. Now obtainable in Azure AI Foundry, HuggingFace, and the NVIDIA API Catalog the place builders can discover the total 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 improvement as our first multimodal language mannequin. On the core of innovation lies steady enchancment, and that begins with listening to our clients. 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 gadgets to know and purpose throughout a number of enter modalities concurrently. Whether or not deciphering spoken language, analyzing photographs, or processing textual info, it delivers extremely environment friendly, low-latency inference—all whereas optimizing for on-device execution and decreased 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 throughout the similar illustration house. 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 suited to deployment on gadgets and edge computing platforms.
This mannequin represents a step ahead for the Phi household of fashions, providing enhanced efficiency in a small bundle. Whether or not you’re searching for superior AI capabilities on cell gadgets or edge programs, Phi-4-multimodal supplies 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 prospects for app builders, companies, and industries trying to harness the facility of AI in progressive methods. The way forward for multimodal AI is right here, and it’s prepared to remodel your functions.
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 indicators as enter, Phi-4-multimodal achieves a lot stronger efficiency on a number of benchmarks.
Phi-4-multimodal has demonstrated exceptional 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 formidable phrase error price of 6.14%, surpassing the earlier greatest 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 corresponding to GPT-4o mannequin. The mannequin has a niche with shut fashions, reminiscent of Gemini-2.0-Flash and GPT-4o-realtime-preview, on speech query answering (QA) duties because the smaller mannequin measurement ends in 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 exceptional imaginative and prescient capabilities throughout numerous benchmarks, most notably reaching robust efficiency on mathematical and science reasoning. Regardless of its smaller measurement, the mannequin maintains aggressive efficiency on basic multimodal capabilities, reminiscent of 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 pace and effectivity. Regardless of its compact measurement, 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 resolution for superior AI functions.
To grasp the mannequin high quality, we examine Phi-4-mini with a set of fashions over quite a lot of benchmarks as proven in Determine 4.
Perform 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. Via a standardized protocol, perform calling permits the mannequin to seamlessly combine with structured programming interfaces. When a person makes a request, Phi-4-Mini can purpose via the question, establish and name related capabilities with applicable parameters, obtain the perform 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 knowledge sources via well-defined perform interfaces. The next instance simulates a sensible residence 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 progressive 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 trying 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 significantly better latency. The longer context window permits taking in and reasoning over giant textual content content material—paperwork, net pages, code, and extra. Phi-4-mini and multimodal demonstrates robust reasoning and logic capabilities, making it an excellent candidate for analytical duties. Their small measurement additionally makes fine-tuning or customization simpler and extra reasonably priced. The desk beneath reveals examples of finetuning eventualities with Phi-4-multimodal.
| Duties | Base Mannequin | Finetuned Mannequin | Compute |
| Speech translation from English to Indonesian | 17.4 | 35.5 | 3 hours, 16 A100 |
| Medical visible query answering | 47.6 | 56.7 | 5 hours, 8 A100 |
For extra details about customization or to study extra concerning 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 very best for edge case eventualities and compute-constrained environments. Given the brand new capabilities Phi-4-multimodal and Phi-4-mini convey, 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 keep up 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 a normal a part of our developer platform.
—Vivek Pradeep, Vice President Distinguished Engineer of Home windows Utilized Sciences.
- Embedded on to your good system: Cellphone producers integrating Phi-4-multimodal immediately right into a smartphone might allow smartphones to course of and perceive voice instructions, acknowledge photographs, and interpret textual content seamlessly. Customers may gain advantage 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 person expertise by offering highly effective AI capabilities immediately on the system, making certain low latency and excessive effectivity.
- On the highway: Think about an automotive firm integrating Phi-4-multimodal into their in-car assistant programs. The mannequin might allow automobiles to know and reply to voice instructions, acknowledge driver gestures, and analyze visible inputs from cameras. As an illustration, it might improve driver security by detecting drowsiness via 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.
- 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. As an illustration, the mannequin can help analysts by performing intricate mathematical computations required for danger assessments, portfolio administration, and monetary forecasting. Moreover, it might translate monetary statements, regulatory paperwork, and shopper communications into numerous languages and will enhance shopper relations globally.
Microsoft’s dedication to safety and security
Azure AI Foundry supplies customers with a strong set of capabilities to assist organizations measure, mitigate, and handle AI dangers throughout the AI improvement lifecycle for conventional machine studying and generative AI functions. Azure AI evaluations in AI Foundry allow builders to iteratively assess the standard and security of fashions and functions utilizing built-in and customized metrics to tell mitigations.
Each fashions underwent safety and security testing by our inner and exterior safety specialists utilizing methods crafted by Microsoft AI Purple Crew (AIRT). These strategies, developed over earlier Phi fashions, incorporate international views and native audio system of all supported languages. They span areas reminiscent of cybersecurity, nationwide safety, equity, and violence, addressing present tendencies via multilingual probing. Utilizing AIRT’s open-source Python Threat Identification Toolkit (PyRIT) and guide probing, crimson teamers performed single-turn and multi-turn assaults. Working independently from the event groups, AIRT repeatedly shared insights with the mannequin crew. This strategy 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.
Study 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 unimaginable issues you’ll accomplish with our new fashions.