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here these models work well and where they fall short. Downstream clinical applications will require additional multi-institutional and prospective validation. PODCAST SERIES Join Microsoft’s Doug Burger and guests as they dig into the fundamental truths about AI and how it will reshape the future. GigaPath and GigaTIME demonstrated what pathology foundation models can learn from whole slides and tumor tissue. GigaPath-Flash and GigaTIME-Flash are a step toward making those capabilities usable a

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arly forensics, including the AI’s answers and Chinese zodiac-based naming, suggest it’s from China’s Zhipu AI, potentially GLM-5.3 Flash or GLM-6 . Another potential candidate is Microsoft’s MAI family, though all four anonymous drops on OpenRouter in six months have been from Chinese labs. The model continues to draw heavy usage thanks to the provider’s "near-unlimited" free offer for the week, with capacity for 100T tokens a day. Why it matters: We've never had a Flash-level model trade blows

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canning is a critical function in the Security Operating Center and feeds Project Perception, our new agentic security system. Built with safety first Because MAI-Cyber-1-Flash is Microsoft’s first cyber model, we built trust into every layer of the system, from model training to customer deployment. The model was developed with a security-first calibration, rigorously evaluated by Microsoft’s AI Red Team, tested through automated and expert-led adversarial exercises, and independently assessed

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way to AI sovereignty. Three days after Laguna shipped, Jensen Huang made a version of the same argument in his first post on X, sharing an open letter signed by NVIDIA alongside Microsoft, Meta and Hugging Face on why open models strengthen security, accelerate innovation and enable sovereignty, and why the world needs frontier models of both kinds. The same logic applies inside an enterprise. Most frontier AI is consumed through an API or a desktop app, which means the customer’s code crosses

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ability to render text with this kind of accuracy is a real breakthrough. It also understands natural language edits, so creative iteration becomes faster and far more intuitive. Microsoft has firmly established itself among the leaders in generative AI.” A year ago, we set out to develop purpose-built models in-house at Microsoft AI. Models trained on clean, traceable, enterprise-grade data, without distillation from third-party models, and designed from the ground up to serve the people who u

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Image Edit leaderboard, ahead of Nano Banana 2.1 Built for high-quality generation and precise, controllable editing, it brings production-ready image workflows to developers and Microsoft products. Today, we’re launching MAI-Image-2.5 for maximum fidelity, and MAI-Image-2.5-Flash for fast, scalable production workloads. Features and capabilities Step-change in text-to-image quality MAI-Image-2.5 produces more detailed, coherent images from prompts, with stronger text rendering, product imagery

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d an AI that places humanity first. That always prioritizes human well-being and human progress. This is the core philosophy and motivation behind our superintelligence efforts at Microsoft. It shapes everything we do. And as a platform company, our job – and our commitment – is to keep you developers building at the absolute frontier. So today we’re very excited to announce a family of seven new models across image, voice, transcription, thinking, and coding. These are all built with real atten

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LI; X; FB Updated as of June 8, 2026. Today we are announcing a family of seven new models developed in-house at Microsoft AI. Beyond these models, we’re building a superintelligence lab – a system and an approach we believe will define the next phase of AI. This is an extraordinary time in technology. The compute used to train frontier models has increased by a factor of one trillion. Now we expect another thousand-fold increase over the next three years, which in turn means more advanced capab

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LI; X; FB Updated as of June 8, 2026. Today we’re introducing MAI-Code-1-Flash, a new Microsoft coding model built for fast, efficient assistance in everyday developer workflows. We trained it from the ground up on clean, traceable and enterprise-grade data, without distillation from third-party models. The model is rolling out to GitHub Copilot individual users in Visual Studio Code in the model picker and under the default auto picker. Features and capabilities Agentic coding in real developer

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-teaming) are invited to join our new Cyber Verification Program. Opus 4.7 is available today across all Claude products and our API, Amazon Bedrock, Google Cloud’s Vertex AI, and Microsoft Foundry. Pricing remains the same as Opus 4.6: $5 per million input tokens and $25 per million output tokens. Developers can use claude-opus-4-7 via the Claude API. Testing Claude Opus 4.7 Claude Opus 4.7 has garnered strong feedback from our early-access testers: In early testing, we’re seeing the potential

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dgments on complete trajectories. Average token cost per task is calculated based on each vendor's official API pricing (without caching); MEWC: Internal benchmark. Built on MEWC (Microsoft Excel World Championship), comprising 179 problems from the main and other regional divisions of Excel esports competitions from 2021–2026. It evaluates the model's ability to understand competition Excel spreadsheets and use Excel tools to complete problems. Scores are calculated by comparing output and answ

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nstall pyaudio Debian/Ubuntu sudo apt-get install python-pyaudio python3-pyaudio or pip install pyaudio CentOS sudo yum install -y portaudio portaudio-devel && pip install pyaudio Microsoft Windows python -m pip install pyaudio import pyaudio import os import requests import base64 import pathlib import threading import time import dashscope The DashScope Python SDK version must be 1.23.9 or later. from dashscope.audio.qwen tts realtime import QwenTtsRealtime , QwenTtsRealtimeCallback , AudioFor

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LI; X; FB Update – November 4, 2025: We have begun launching MAI-Image-1 into select Microsoft products! Try it in Bing Image Creator: Available at bing.com/create, in the Bing mobile app, or right from the Bing search bar, Bing Image Creator is built to meet people where they already search and create. MAI-Image-1 is now an option alongside DALL-E 3 and GPT4o in the model menu, enabling you to experiment and pick the model that best matches your creative goals. Try it in Copilot Audio Expressio

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LI; X; FB At Microsoft AI (MAI) we believe AI should be used to empower every person on the planet. We are creating AI for everyone, a supportive, helpful presence always in the service of humanity. It will be the gateway to a universe of knowledge and a set of capabilities that enable people and organizations to achieve more. Responsible, reliable, filled with personality and expertise, we are focused on creating applied AI as a platform for category defining and deeply trusted products that un

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ng, where the model learns its most basic understanding of language. The second trick is far more uncommon: weight initialization via tiling for the larger model size, inspired by Microsoft’s Phi family of models. This one’s based on the following realization: Why initialize the ModernBERT-large’s initial weights with random numbers when we have a perfectly good (if we dare say so ourselves) set of ModernBERT-base weights just sitting there? And indeed, it turns out that tiling ModernBERT-base’s

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ce efficiency compared to models of a similar scale and larger such as Gemma-2B (Google), SmolLM-1.7B (Huggingface), OpenELM-1.1B (Apple), StableLM-1.6B (StabilityAI) and Phi-1.5 (Microsoft); Zamba2-mini is extremely inference-efficient, achieving 1.67x faster time-to-first-token and a 23.3% reduction in memory overhead compared to Phi1.5-1.3B; We release the model weights open-source (Apache 2.0) Zamba2-mini (1.2B) Efficiency Zamba2-mini achieves the quality of a 2-3B dense transformer while on

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mark performance and superior inference efficiency compared to models of a similar scale such as Gemma2-2.7B (Google), StableLM-3B (StabilityAI), OpenELM-3B (Apple) and Phi2-2.7B (Microsoft); Architectural improvements over Zamba-7B :Mamba1 blocks have been replaced with Mamba2 blocks; Instead of a single shared attention block, we utilize two shared attention blocks which are interleaved in an ABAB pattern throughout the network; We apply a LoRA projector to each shared MLP block, which allows

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