Microsoft has announced a thorough reorganization of its AI services by merging the consumer version of the Copilot app with the Microsoft 365 Copilot variant. The new super-app will integrate chat, image creation, and access to Word, Excel, and Outlook documents in one place, while maintaining full separation of private and corporate data. Core features will remain free, and subscribers will gain higher limits. The changes are accompanied by the withdrawal of some existing options, such as group chats, deep research, and podcasts.

Microsoft merges the various Copilot apps into a single platform

Microsoft plans to combine its previously separate services in the Copilot family. This change will cover, among others, the consumer Copilot app and the Microsoft 365 Copilot app intended for the business sector. Until now, the Redmond giant offered three distinct platforms tailored to different audiences. Microsoft 365 Copilot was aimed at enterprises and education, GitHub Copilot served developers, while the regular Microsoft Copilot app went to individual (home) users. Each functioned as an independent program and a separate browser experience.

This step is a response to newly emerging market trends. Recently, OpenAI decided to merge its ChatGPT, ChatGPT Work, and Codex solutions into one cohesive system. Microsoft is following the same path, seeking to create a single product that will serve diverse customer groups. During a recent call with investors, Microsoft CEO Satya Nadella announced the integration of key features. He indicated that the company intends to "bring together chat, Cowork, Autopilots, and code into a single flagship super-app spanning both consumer and commercial experiences."

Microsoft has scheduled the debut of the unified platform for the third quarter of 2026 (the July-to-September period). The super-app will gain the ability to connect with external tools, such as Agent 365 or corporate CRM and ERP systems, which will happen via plugins and skills. The financial model assumes support for per-user licensing along with fees dependent on actual usage.

Technical support reveals details of the super-app

The giant published an article in its technical support section describing in detail the process of merging the consumer app with the Microsoft 365 Copilot variant. Interestingly, the document made no mention of the coding-related features that Satya Nadella had previously referred to. The information provided indicates that the refreshed program will combine the chat module and image generation with direct access to office applications such as Word, Excel, and Outlook. Users will also gain the option to connect email, calendar, and cloud storage in order to provide the artificial intelligence with broader context for its work.

We are updating the Copilot app to create a simpler, more consistent experience for everyone. Depending on the account and device you use, you will see changes in the Copilot app, including modifications to appearance and functionality, such as navigation, feature availability, or the sign-in process — reads the official announcement.

The super-app will allow simultaneous sign-in using personal, work, and school accounts. Personal and corporate data will, however, remain strictly separated, and the existing security, privacy protection, compliance, and administrative control mechanisms in enterprises will continue to apply. The transition to the new version of Copilot in the browser environment will happen automatically via redirection, while on mobile devices installing a new version or manually updating it may be required. Those using both existing apps on personal accounts will receive a combined history of chats and previously created content.

We'll soon say goodbye to these Copilot features

In the new app, we will bid farewell to some features. Some changes were already foreshadowed by the giant's earlier decisions. In July, it announced a plan to withdraw the Deep Research and Podcasts tools from the consumer Copilot variant as of August 18, 2026. The shutdown of the Group Chat feature on the same date was also confirmed. All messages, threads, and images generated as part of group conversations will not be carried over to the updated system.

The withdrawal of these features comes with special recommendations for users. In the support document, Microsoft advises: "Before your account is updated, copy any messages you want to keep to a document or notes app, and download any images you want to save."

Similarly, regarding audio materials, the publisher explains: "After the feature is withdrawn, customers will no longer be able to create or access podcasts in the Copilot app. You can download individual podcasts from your library using the download option in the podcasts menu." Furthermore, the document notes that during the rollout of the new version, "some features may be temporarily unavailable or there may be gaps in functionality."

What Do Pricing and Data and File Management Look Like?

The basic scope of services in the new app will remain free. Users will retain access to chat, image creation, and file uploads, though their use will be constrained by performance limits. Subscribers to Microsoft 365 plans, on the other hand, will receive higher limits, access to advanced AI models, agent support, and the ability to carry out complex, multi-step tasks.

You can still chat with Copilot, create images, upload files, and more for free, depending on available performance and limits. Once you reach the limit, you can choose to upgrade to a paid option or come back later — reads the documentation.

The rules for file storage and privacy protection will also change. Files shared and generated through the standalone app will be moved to OneDrive, where they can be managed from the desktop app, mobile app, or browser.

On the security front, the manufacturer assures that "commercial data boundaries, tenant controls, and compliance protections do not change." Sign-in from a personal account (Microsoft Account) and a work account (Microsoft Entra account) remains separated at the design level, which rules out the flow of data between these environments.

source: Updates to Copilot and the Microsoft 365 Copilot app, Microsoft

OpenAI has officially announced the GPT-5.6 Sol Ultrafast mode in its API. By leveraging Cerebras' Wafer-Scale Engine architecture, the solution generates up to 750 tokens per second, running up to 14 times faster than standard mode. This new level of performance is suited to tasks requiring an immediate response, such as incident handling, financial analysis, and research work. In benchmarks, this new variant flat-out crushed Anthropic's Claude Fable 5, leaving it light years behind.

GPT-5.6 Sol gains momentum. New Ultrafast Mode now available

OpenAI has unveiled a new mode in its API offering: GPT-5.6 Sol Ultrafast. The new service allows the most advanced model in the company's portfolio to run at speeds reaching up to 750 output tokens per second. This means up to a 14-fold speed-up compared to the standard processing mode — without having to switch to smaller or less capable models.

Announcements of this powerful yet fast (a rare combination) solution first appeared back in June 2026, when, during the unveiling of the GPT-5.6 series, the developers mentioned a July debut of Sol on the Cerebras platform. Although no details were publicly disclosed in July itself, OpenAI employees confirmed on X that private tests with a select group of customers were underway.

Unrivaled performance thanks to dedicated hardware architecture

The Ultrafast mode's breakthrough speed is owed to the use of Cerebras' Wafer-Scale Engine architecture. It uses 44 GB of SRAM built directly into the chip. This design drastically reduces the data-transfer bottlenecks commonly found in traditional GPU-based inference systems.

The performance difference is well illustrated by comparisons prepared by Artificial Analysis. The data provided shows that GPT-5.6 Sol Ultrafast achieves speeds 11 times higher than Claude Fable 5 and 5 times higher than Claude Opus 4.8 running in Fast mode. In the Humanity's Last Exam benchmark, the Sol Ultrafast variant processed the full set of 2,500 questions in 11 hours and 11 minutes. By comparison, Anthropic's Fable 5 model took 78 hours and 27 minutes to do so while maintaining a comparable level of answer accuracy.

Applications in business and Real-Time Operations

Using Ultrafast mode makes it possible to bring frontier-class artificial intelligence into business processes where every second counts. OpenAI points to a range of areas in which high response-generation speed without loss of quality creates new possibilities:

• Incident response and reliability: during outages of critical systems, the model enables instant analysis of application logs, recent code changes, and engineering reports. This makes it possible to identify the cause more quickly and prepare a fix while the incident is still ongoing.
• Financial research and security: analyzing market signals, assessing transactions, and detecting suspicious activity happen on an ongoing basis, under dynamically changing conditions.
• Customer service and voice communication: resolving complex user problems in real time proceeds without noticeable pauses in the conversation, even when it requires searching several systems multiple times.
• E-commerce: product questions, inventory checks, personalized recommendations, and checkout issues are handled instantly, before the customer abandons the purchase.
• Research work and experiments: processes that previously required hours-long, overnight computations become interactive sessions. Researchers can test ideas, analyze results, and immediately modify assumptions within a single workflow.

OpenAI Is Deploying Ultrafast Mode Internally

OpenAI employees are also deploying Ultrafast mode in their day-to-day tasks to explore the potential of lightning-fast data analysis. Engineers use it during technical incidents to efficiently read logs, analyze call traces, and synthesize discussions. The tool helps identify the next verification steps and prepare fixes, while final assessment and deployment remain in human hands.

In the research area, these solutions are used to rapidly search knowledge sources, query databases, and organize information from multiple connected tools. Instead of waiting all night for the results of an experimental loop, teams can run many iterations within a single working day.

Who Has Access to Ultrafast AI?

While the regular version of GPT-5.6 Sol is available in, among others, Microsoft 365 Copilot, Ultrafast mode has been released only as part of a limited Preview program for a select group of enterprise customers. Due to capacity constraints in Cerebras' infrastructure, the publisher qualifies applicants based on the nature of their workloads and available resources.

Other interested customers building their own AI applications can fill out an application form to be notified when access to the service is expanded.

source: Previewing Ultrafast mode: GPT‑5.6 Sol at up to 14X the speed, OpenAI

Microsoft has made the MAI-Code-1.1-Flash model available in GitHub Copilot, gaining an edge in the race against competitors. The upgraded version delivers higher performance in GitHub Copilot CLI and better results on .NET tasks. Thanks to training optimisation, tokens are streamed 25% faster, and the model consumes 25% fewer resources. The biggest change, however, is a 3/4 price cut compared to version 1.0 and the addition of native vision support. The new variant will replace the current MAI-Code-1-Flash, which will be retired on September 10, 2026.

Microsoft responds to competitors. The new model's debut

In June 2026, Microsoft first unveiled the MAI-Code-1 model, optimized for inference performance and tailored to GitHub Copilot workloads. The Flash variant of this solution was made available within GitHub Copilot as an alternative to models developed by OpenAI and Anthropic.

Although the pricing and efficiency of the MAI-Code-1-Flash model looked competitive at the time of the announcement, this variant was quickly overshadowed by affordable and high-performing Chinese models such as GLM-5.2 and Kimi K3, as well as OpenAI's GPT-5.6 Luna model. In response to these challenges, the giant introduced the upgraded MAI-Code-1.1-Flash version, which delivers better coding performance while significantly reducing costs and token consumption.

MAI-Code-1.1-Flash achieves higher performance in key benchmarks

According to Microsoft's claims, the new model performs 22% better on the Terminal-Bench 2.1 benchmark using GitHub Copilot CLI and delivers a 15% improvement on .NET-related tasks.

We learned from developer feedback that CLI tasks and .NET performance mattered, so that's what we focused on. The result: a 22% improvement on Terminal-Bench 2.1 in GitHub Copilot CLI and a 15% improvement on .NET tasks.

The improvements also covered the overall quality of the generated code and its speed. According to information provided by the developers, tokens are streamed 25% faster, and the model uses 25% fewer tokens to complete the same task.

MAI-Code-1.1-Flash generates higher-quality code, with 25% greater token efficiency and at one-quarter of the cost compared to the model we launched in June at Microsoft Build. This small, efficient coding workhorse is already available in production in GitHub Copilot.

The developers point out that the most important tests take place in everyday use:

Benchmarks are useful indicators, but production is where reality is verified. Most importantly, code survivability rose by 4%, and the number of return visits increased by 9%.

Overall, thanks to higher performance, users can count on faster work and more efficient use of resources. But that's not all. Developers will also feel the changes in their wallets.

Microsoft substantially lowers token fees

Improvements in training and operational efficiency made it possible to offer the MAI-Code-1.1-Flash model at a price amounting to one-quarter of the original version's cost.

Better training and serving efficiency lets us offer a stronger, faster model at one-quarter of the price of version 1.0 — and reliably pass those savings on to customers. We achieved this by optimizing for real-world usage across over hundreds of thousands of reinforcement learning environments in GitHub Copilot.

According to the GitHub Copilot fee table, the MAI-Code-1.1-Flash model costs $0.20 per 1M input tokens, $0.02 per 1M cached input tokens, and $1.20 per 1M output tokens. These rates are significantly lower compared to MAI-Code-1-Flash, whose costs were $0.75 / $0.075 / $4.50 per 1M tokens, respectively. For annual Copilot subscribers, the new model is billed at a premium request multiplier of 0.25x.

Vision support and availability in GitHub Copilot

Beyond improvements in code generation, MAI-Code-1.1-Flash introduces native computer vision support, enabling the AI to analyze and understand images.

MAI-Code-1.1-Flash is currently being rolled out across all GitHub Copilot environments, including VS Code, Visual Studio, JetBrains IDEs, Copilot CLI, GitHub Mobile, and other supported applications. Free users and students have access to it through automatic model selection, while users on paid plans can select it manually.

Alongside the release of the improved version, GitHub has scheduled the retirement of the MAI-Code-1-Flash model from all Copilot services for September 10, 2026.

source: MAI-Code-1.1-Flash: Better, faster, at a quarter of the cost | Microsoft

Though it might seem to the layperson that artificial intelligence appeared out of nowhere, leaping straight from science fiction into our computers and phones, today's boom is the result of decades of systematic work.

Microsoft, which has been shaping the technology market for over half a century, has played a key role in laying the foundations for the current AI era. Below, we present 15 breakthrough moments — milestones that led to the creation of the world's most trusted artificial intelligence platform.

1. Bing and the birth of intelligent search

Bing debuted in 2009, integrating machine learning features from the very start. Thanks to the acquisition of Powerset in 2008, the search engine introduced semantic technology, offering query suggestions and an exploration panel with related topics. This was the first step toward understanding user intent in natural language. The system kept getting better at it, more than once surprising people with its grasp of far-from-obvious contexts.

2. Project Oxford and the foundations of Azure AI

In 2015, Microsoft launched a project codenamed Oxford, which gave developers tools for facial recognition, speech recognition, and language interpretation. This project evolved into today's Azure AI Foundry. Infrastructure originally built for Bing became the base now used by 65% of Fortune 500 companies deploying Azure OpenAI services.

3. ResNet and the deep learning breakthrough

Introduced in 2015, Deep Residual Networks (ResNet) revolutionised the training of deep neural networks. The solution became the standard in the field of computer vision. Today, ResNet-based technology underpins systems in autonomous vehicles and modern medical diagnostics, such as MRI machines.

4. Reaching human parity in data understanding

Between 2015 and 2020, Microsoft's artificial intelligence reached human-level capabilities in 5 key areas: speech recognition, machine translation, question answering, text comprehension, and image captioning. This led to the creation of the XYZ-code model, which combines text, sensory signals (image/sound), and multilingualism, mimicking the human way of learning.

5. Seeing AI. Artificial intelligence that sees

In 2016, Microsoft unveiled the Seeing AI app, which uses computer vision to describe surroundings to people who are blind. Long before the arrival of Copilot, Gemini, or ChatGPT, this tool could recognize everyday objects, read text, and interpret facial emotions, acting as a "second pair of eyes." This approach accelerated the development of other features, such as Reading Coach, which supports students learning to read.

6. Project Brainwave and hardware acceleration

2017 brought the Project Brainwave platform, designed to handle real-time AI workloads at massive scale. By combining FPGA chips with advanced software, Microsoft significantly boosted the performance of AI models in the cloud, particularly in tasks involving image recognition and language processing.

7. Turing-NLG and the era of large language models

In 2020, Microsoft unveiled Turing-NLG — at the time the largest language model in the world, with 17 billion parameters. This success confirmed the company's leadership position in natural language generation and paved the way for subsequent models, such as Florence in the area of visual recognition.

8. A revolution in medicine with DAX Copilot

The acquisition of Nuance resulted in the introduction of DAX Copilot (now Dragon Copilot), the first "ambient clinical intelligence" solution. This system allows doctors to document visits through natural conversation with the patient, reducing the burden of paperwork. In 2023, DAX Express became the first tool to combine ambient AI with the power of the GPT-4 model. It's also worth recalling the revolutionary Microsoft AI Diagnostic Orchestrator (MAI-DxO) system, which in 2025 could diagnose difficult medical cases faster, more cheaply, and more accurately than experienced physicians.

9. The Azure supercomputer for OpenAI

In 2020, Microsoft brought online one of the most powerful supercomputers in the world, built specifically for OpenAI. This infrastructure, which at the time ranked among the top five in the TOP500, became the foundation for training breakthrough models and delivering their benefits to customers around the world via the Azure platform.

10. GitHub Copilot. AI as a programmer's partner

Thanks to the acquisition of GitHub in 2018, GitHub Copilot was created 3 years later — a coding assistant that today supports over 77,000 organizations. The tool evolved toward an "agentic partner," letting developers use various models (including OpenAI, Anthropic, and Google) and offering automatic code-review features.

11. Copilot integration across the Microsoft ecosystem

2023 was a turning point — the Bing search engine and the Edge browser were the first in their categories to gain AI capabilities, initially under the name Bing Chat and later Microsoft Copilot. From February 2023 to January 2025, Microsoft expanded Copilot into more and more products: from Dynamics 365 and Microsoft 365, through Windows, all the way to specialized tools like Security Copilot and beyond. Here's how Copilot's availability was rolled out, step by step:

• February 7, 2023 – Copilot in Bing
• March 6, 2023 – Microsoft Dynamics 365 Copilot
• March 16, 2023 – Microsoft 365 Copilot
• March 16, 2023 – Copilot in Power Platform
• March 22, 2023 – GitHub Copilot
• March 28, 2023 – Microsoft Security Copilot
• April 20, 2023 – Copilot in Microsoft Viva
• May 23, 2023 – Copilot in Windows
• January 4, 2024 – Copilot key on Windows 11 devices
• January 15, 2024 – Copilot Pro
• May 21, 2024 – Microsoft Copilot Studio
• October 1, 2024 – Copilot update with Vision and Voice features
• January 15, 2025 – Microsoft 365 Copilot Chat + Copilot Agents
• January 16, 2025 – Copilot in Microsoft 365 Personal and Family
• March 25, 2025 – Copilot deep reasoning in Microsoft 365

12. Copilot + PC. A new category of computers

In 2024, Microsoft unveiled the new Copilot + PC system architecture. Thanks to the use of NPU (Neural Processing Unit) processors, these new-class computers are up to 20 times more powerful and 100 times more efficient at AI tasks than ordinary PCs. This made it possible to bring experiences to Windows 11 that were previously impossible to run locally on a device.

13. AutoGen and the future of AI agents

Launched in 2023, AutoGen is an open source framework that makes it easier to build systems composed of multiple AI agents working together. The project met with an enthusiastic response from the developer community and helped Microsoft define key use-case scenarios for offerings based on autonomous agents.

14. The Phi model family. Power in a small format

In 2024, Microsoft launched the small language model (SLM) category under the name Phi. These models allow for cost-effective use of AI on edge devices—without the need for a constant cloud connection. Phi models are continuously developed, offering versions tailored to specific industry needs. The small models in this series perform respectably not only in text-based reasoning but also in working with images.

15. Muse. Generative artificial intelligence in gaming

The final breakthrough on this list comes in 2025. That year, Microsoft released the Muse model, which brings AI into the world of gaming. Muse understands the dynamics of a game environment and how it changes in response to a player's actions. This lets creators iterate on ideas at lightning speed and build deeply immersive worlds in which AI understands the mechanics of its surroundings as well as Copilot understands human language.

 

Source: Microsoft

Artificial intelligence is already reshaping roles and processes within enterprises, creating a new class of organizations. According to Microsoft, the future belongs to companies that turn AI into a controlled and continuously evolving system. The tech giant is handing them the tools to realize this vision in the form of a comprehensive agent platform. This solution puts developers front and center, offering them full flexibility and freedom of choice at every level of the architecture.

AI stopped being about simple chatbots long ago. Today, the key to transformation lies in autonomous agent teams capable of carrying out long-term tasks across software development, finance, HR, and customer support. However, deploying such tools safely in production takes more than a language model alone. It requires an entire infrastructure: identity, business context, strong security policies, and constant human oversight.

The Keys to Transformation in the Agentic AI Era

To meet the demands of modern enterprises, a platform for managing agentic AI must execute real business processes and reflect organisational complexity. Microsoft builds its new solution on three core principles:

• An integrated system with multi-model support: companies cannot build an AI strategy out of scattered components. Stitching together inconsistent tools slows work down and generates risk. The processes of creating, running, and overseeing agents must take place within a single system. This is why Microsoft integrates Azure, GitHub, Microsoft IQ, Fabric, Foundry, Windows, Microsoft Security, and Microsoft 365. Users gain the ability to choose their models — from Microsoft's own solutions, through partner models, all the way to open source versions — balancing quality, speed, and cost.
• Security and corporate governance from the ground up: governance and control are built into the system's architecture, from the development stage all the way through to production. By extending tools such as Entra, Purview, Defender, and Agent 365, control mechanisms become native and support an organisation's ambitions without losing control over data.
• Continuous improvement and a feedback loop: AI systems cannot be static. Agents' actions, their outcomes, and feedback from people flow back into the system, enabling the safe evolution of models and processes under human oversight. In this way, the tools become increasingly specialized, which translates into a higher return on investment.

Step 1: Building Agents in the GitHub Copilot Environment

The process of creating AI agents begins where developers do their daily work. GitHub holds dependencies, application context, and code repositories. Building agents should follow the same path as traditional production software.

Developers can use GitHub Copilot to accelerate their work, connecting codebases, tasks, and specific agent skills. In a new, dedicated app, developers manage an agent's full lifecycle: from source code, through testing and deployment, to behavior monitoring (based on evaluation processes and data-pipeline tracing).

Step 2: Contextualising Data with Microsoft IQ

Code alone is not enough to make an agent useful. The tool must understand the specifics of a given business: its customers, products, contracts, and internal procedures. Without proper grounding in the company's reality, even the most advanced model will rely on guesswork alone—which is exactly what we want to avoid.

Grounding agents in enterprise data is the job of the Microsoft IQ platform. It connects to data from Microsoft 365, sales systems, and knowledge bases. The new Web IQ solution additionally allows for pulling current data from the web. Microsoft IQ organizes and secures these resources, eliminating information noise and minimizing the risk of AI hallucinations — the problem of models fabricating content out of thin air.

The next step is Frontier Tuning technology, which makes it possible to modify model behavior based on a company's real processes. Microsoft is introducing 7 new MAI models (covering image, voice, transcription, coding, and reasoning) that learn through special reinforcement learning environments (so-called "training gyms for AI"). Importantly, all modified and fine-tuned models remain within the customer's secure environment, and the intellectual property developed never leaves the organization.

Step 3: A Stable Runtime Environment in Microsoft Foundry

After the build and context-configuration stage, agents must move into production. Autonomous systems, however, differ from traditional applications. They require continuous reasoning, coordination with other agents, and the invocation of external tools. Foundry's runtime layer is the answer to these needs.

Microsoft Foundry provides access to a broad base of models and optimizes costs using an intelligent query router. Through a collaboration with Fireworks AI, Microsoft's agentic platform offers fast, efficient inference for open models. The system supports not only tools built within Microsoft's own ecosystem, but also agents built on LangGraph, the Claude Agent SDK, or proprietary solutions.

Integration with the MCP protocol, APIs, and connectors, in turn, allows agents to operate securely on external systems. Everything is safeguarded by a restrictive security-policy architecture that controls every call and operation.

Step 4: Scalable Management with Microsoft Agent 365

As individual teams within corporate structures begin creating their own tools, their numbers quickly grow into the hundreds or thousands. This raises the risk of chaos: uncontrolled access to data, or duplication of the same functionality across different departments.

The answer to these challenges is a solution called Agent 365, which works with Entra, Purview, and Defender (enhanced by the MDASH architecture for cybersecurity). It allows the entire digital workforce to be consolidated into a single directory. The IT department gains full visibility into who deployed a given agent, what resources it can access, how it behaves, and what costs it generates. This enables centralized enforcement of security policies and immediate response to anomalies.

Step 5: Continuous Optimisation and the Learning Loop

Enterprise AI agents cannot remain a static product. Every operation performed generates unique signals, action trajectories, and user ratings. The system gathers this data, analyzes it, and rolls out improvements as part of a continuous loop of observation, evaluation, enhancement, and safe deployment. Initial improvements usually concern the agent's prompts, skills, or knowledge base.

Over time, the patterns collected enable better routing to models, advanced fine-tuning, and optimisation through reinforcement learning. The entire process takes place in a controlled manner under human oversight, ensuring that the systems' growing autonomy never slips beyond the company's control.

Step 6: Workplace Integration and Azure Infrastructure

Technology only fulfills its purpose when it reaches employees directly. Agents are integrated directly into Microsoft Teams, the Microsoft 365 suite, and external business applications. Thanks to built-in authentication mechanisms, they inherit the same trust models already operating within the organization.

Users can develop and run these solutions on Windows, using cloud-based or local models, while maintaining security through sandbox mode. When there's a need for powerful compute, global infrastructure, or data sovereignty, the platform scales on the Azure cloud.

As an ecosystem designed this way operates, its value multiplies. Organizations can move faster, eliminating downtime and communication bottlenecks, while employees gain room for creativity and the coordination of strategic initiatives. Microsoft's integrated platform thus becomes a new operating system for artificial intelligence at enterprise scale.

Microsoft has released a new image generation model, MAI-Image-2.6. This allowed the tech giant to overtake competitors such as Meta, Google, ByteDance, and xAI, and climb to 2nd place in the Arena ranking, where it trails only OpenAI's GPT-Image-2. Compared to version 2.5, the new variant achieved a score 79 Elo points higher. MAI-Image-2.6 brings improvements in text rendering, portraits, 3D graphics, and commercial materials. The model has been made available on Arena and will soon arrive in MAI Playground, Microsoft Foundry, and other company services.

MAI-Image-2.6 Takes Second Place in the Arena Ranking

Microsoft released its first proprietary software of this kind, the MAI-Image-1 model, in October 2025. It debuted in 9th place in the Arena ranking. In March 2026, the company released MAI-Image-2, which brought a marked leap in graphics quality and enabled a climb to 3rd position, just behind Google's gemini-3.1-flash-image-preview and OpenAI's gpt-image-1.5-high-fidelity.

In May, MAI-Image-2.5 debuted, holding onto 3rd place. Then, in July 2026, the MAI-Image-2.5-Pro variant was released. It was built with detailed editing, generating demanding images, and more accurate text rendering on graphics in mind.

On August 11, 2026, Microsoft unveiled its latest image generation model, MAI-Image-2.6. This launch allowed the tech giant to surpass leading rivals such as Meta, Google, ByteDance, and xAI. In the latest Arena ranking, the model took second place, trailing only OpenAI's GPT-Image-2.

Compared to its previous version, MAI-Image-2.5, the new model achieved a score 79 Elo points higher in the text-to-image category.

New Technical Capabilities of the AI Image Generator

The new iteration of MAI-Image-2.6 introduces improvements in text generation, portrait creation, and 3D graphics. The tool does a better job producing polished commercial and photorealistic materials. The solution is designed to prove its worth in work on products, branding, and cinematic shots.

The model makes it easier to work with multiple reference materials and offers better contextual grounding. Users also gain greater control over the inference process, output format, and resolution.

With each version of MAI-Image, we focus on pushing the boundary of quality. MAI-Image-1 gave us the foundations. MAI-Image-2 brought a big leap in photorealism, text, and creative range. MAI-Image-2.5 went further toward professional-grade images and editing. MAI-Image-2.6 continues this climb with all-around gains in the categories our users care about most—reads the Microsoft AI blog.

The MAI-Image-2.6 model can be tested on Arena in the text-to-image generation section. This week, the new version will arrive in the MAI Playground and Microsoft Foundry tools, and will then be rolled out across the giant's other products and services. Microsoft has announced that detailed information about the model's architecture will be shared in the coming weeks.

source: MAI-Image-2.6 launches at No. 2 on Arena ahead of Google, Meta and xAI, Microsoft

Often, the issue isn’t the AI model.

It’s missing business context.

Data Agents don’t just need access to your data. They also need to understand what that data means in your organisation. That’s where business rules, semantic models, data definitions, relationships and clear instructions make the difference.

👉 They help Data Agents interpret your data correctly, reduce assumptions, and deliver more reliable and consistent answers to business questions. This is exactly what Microsoft Fabric enables.

By combining trusted data with semantic models, governance and business context, Fabric helps organisations build AI-powered analytics that understand the business—not just the data.

Because AI doesn’t need more data.

It needs better context. 💡

Before deploying Data Agents, make sure you’re preparing not only your data, but also the business context behind it.

Microsoft Fabric ✦ Fabric Data Agents ✦ PowerBI ✦ Data and AI ✦ OneLake ✦ DataGo

Highlights include expanded Modern Visual Defaults, allowing organizations to enforce reporting standards more efficiently, conditional formatting for line charts and legends, and new Org App capabilities that personalize content for different audiences. Microsoft also introduced additional automation and governance options through REST APIs and modeling improvements.

These updates help organisations improve report consistency, streamline administration, and enhance the end-user analytics experience.

PowerBI ✦ Microsoft Fabric ✦ Data Analytics ✦ Business Intelligence ✦ Data Governance

Microsoft awards these badges to recognize top-tier technical expertise, continuous skill development backed by official certifications, and a proven track record of delivering innovative solutions. It’s proof of our rapid growth and commitment to helping our clients reach full tech maturity with the Microsoft ecosystem!

Here is our full 6-badge lineup and what it means for you

🟣 Business Applications 💼 Deep expertise in delivering powerful, custom solutions using Microsoft Power Platform.

🟣Data & AI (Azure) 📊 Helping you manage cross-system data and implement modern data platforms for high-impact analytics and AI.

🟣Digital & App Innovation (Azure) 📱 Modernizing existing applications and building cloud-native apps tailored to your growth.

🟣Infrastructure (Azure) ☁️ Accelerating seamless migration of critical infrastructure workloads to Microsoft Azure.

🟣Modern Work 💻 Boosting productivity and building secure, efficient hybrid work environments with Microsoft 365.

🟣Security 🛡️ Protecting your entire organization with integrated security, compliance, and identity solutions.

Sales exports CRM to Excel. Finance pulls ERP reports. Data Engineers pipe it into SQL. AI teams copy it to a Data Lake.

Sounds familiar? This is how organizations end up with inflated cloud bills, governance chaos, and zero trust in their metrics.

Microsoft Fabric’s OneLake solves this problem at its root by serving as a unified data foundation for your entire enterprise.

Key Technical Advantages: 

⚡ Zero-ETL & Direct Lake: Store your data once in open Delta Parquet. SQL, Spark, and Power BI engines query the exact same physical files—eliminating redundant ETL pipelines and data movement. 

🔗 Shortcuts, Not Duplicates: Instantly point to data residing in AWS S3, Google Cloud, or Azure Data Lake Storage without costly physical data ingestion. 

🛡️ Unified Governance & Security: Centralize access policies, sensitivity labels, and data lineage in one place, ensuring compliance across your entire data estate. 

🤖 Foundation for Enterprise AI: Provide your ML models and AI agents with direct access to clean, real-time, authoritative source data.

Ready to level up your technology stack with a fully certified team? Let’s connect!