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 use Microsoft products every day.

Today, that work is showing up where it matters in the products you already rely on. The models we previewed at Build are not just topping leaderboards, they are running in production, at scale, efficiently powering experiences for millions of users across a growing portfolio of Microsoft products, including Bing, PowerPoint, OneDrive, Dynamics 365, and Azure.

Introducing two new model variants

Real-world use cases are not one-size-fits-all. A creative studio chasing maximum fidelity has very different needs from a customer service center serving millions of calls daily. That’s why we’re building families of models: to give every product the right balance of quality, speed, and cost.

Today we’re expanding those options:

• MAI-Image-2.5-Pro is now in public preview. For use cases where quality is top of mind. Hero imagery, detailed editing, precise in-image text rendering. Pro is our highest-fidelity image model to date, priced at $5 per 1M text input tokens, $8 per 1M image input tokens, and $106 per 1M image output tokens.
• MAI-Voice-2-Flash is now in public preview. First introduced at Build, Flash is built for speed and scale. It’s the fast, efficient path for high-volume voice experiences where responsiveness is everything, all while retaining the natural prosody and high acoustic quality found in MAI-Voice-2. Flash is 2x faster than MAI-Voice-2 and 32% cheaper, priced at $15 per 1M characters.

MAI-Voice-2-Flash and MAI-Image-2.5-Pro sit alongside our existing production models so builders can pick the point on the quality-speed-cost curve that fits their job.

Purpose-built models for our Microsoft products

Leaderboards are great for benchmarking the quality of our models, but the proof lies in serving users in real product use cases. Microsoft rigorously evaluates all models before deciding on the best one for use in production environments. MAI models are outperforming competitive models for quality, latency and efficiency for more of Microsoft’s product surfaces:

• Bing Image Creator is now 100% in-house by default. With MAI-Image-2.5‘s new precise image editing capabilities, it is now the default model powering Bing Image Creator end-to-end, for high-quality generation and greater creative control.

Each of these enhancements is a step toward the same goal: Microsoft products, powered by Microsoft models, built to serve the people who use them.

Built with the experts

Some of today’s most consequential and challenging fields, such as medicine or software engineering, demand more than a general-purpose model. They demand deep expertise, tight feedback loops, and models that are capable of solving complex, real-world challenges. Microsoft AI partners directly with domain specialists to adapt and refine our models for their unique needs:

• MAI is partnering with Dragon Copilot, a solution used by 170,000 medical providers, which processed 28 million patient encounters last quarter.MAI-Transcribe-1.5 now supports Dragon Copilot’s multilingual workflow, replacing the previous model with our own best-in-class model across 58 languages. In internal evaluations on multilingual recordings, the model delivers a 50% relative reduction in both transcription and language-identification error rates across most languages, and early research shows promising improvements in the downstream accuracy of medical notes.

None of this is an endpoint. It’s the compounding result of one decision: build models in-house so they can be shaped around the people who use our products, and offer options that deliver customers more choice and better value. That’s what “powered by MAI” means, model by model, product by product. We’re just getting started. In the meantime, get started with the models in Foundry. You can also learn more about MAI-Image-2.5-Pro and MAI-Voice-2-Flash on Microsoft Learn or try them out in the MAI Playground.

Better models, fewer parameters, less tokens

At Build in June, we introduced our hill–climbing machine, our integrated data, model, and harness flywheel. Today we are excited to share two examples inside Microsoft: MAI models specialized for agentic workloads in GitHub Copilot and Excel.

Early results are promising. In our live product deployment, we see that our MAI model deployed in Excel is on par with GPT-5.6 for the most common tasks while being more cost-efficient.

The figure below shows how MAI-Code-1-Flash, post-trained within the GitHub Copilot harness, was used as the starting checkpoint to climb on Excel evaluations, resulting in two highly efficient, specialized models.

MAI-Code-1-Flash in GitHub Copilot

Since launching MAI-Code-1-Flash in GitHub Copilot in June, millions of developers have been using it for their day-to-day work, where it’s outperforming other similarly sized models while using fewer tokens.

• It has an approximately 10% higher code accept rate than GPT 5.4 Mini and Claude Haiku 4.5 in VS Code.

• Developers were 6% more likely to return across multiple days than with GPT 5.4 Mini and 11% more likely than with Claude Haiku 4.5.

• It has 10% lower median token usage than GPT-5.4 mini and Claude Haiku 4.5, with more user-initiated turns.

MAI model live in Excel

Excel offered a test for whether the capabilities built into MAI-Code-1-Flash could transfer beyond the domain they were trained for, moving from agentic coding to agentic knowledge work. To do so, we further trained our MAI-Code-1-Flash checkpoint in an Excel reinforcement learning environment to learn about tools and knowledge workflows in spreadsheets. The result is a model with a command of Excel workflows that is more efficient and less expensive to run.

User feedback from production traffic indicates that the quality of the MAI model in Excel is on par with GPT-5.6 for the most common tasks. In addition to the direct model cost savings, this smaller, more efficient model can be served on both Nvidia H100 and A100 class GPUs rather than requiring only the latest-generation accelerators, which significantly lowers the cost of deployment for Microsoft.

Training agentic models from inside the product stack

These results point toward a broader strategy. By having access to the entire product stack—the model, the harness that runs it, the agents, and product-specific evaluations—we can hill-climb to train efficient, powerful models capable of tasks previously handled by larger, more expensive ones.

Beyond GitHub Copilot and Excel, we’re currently extending this hill-climbing approach to train efficient models across Microsoft’s family of agentic products: Copilot Chat, Outlook, PowerPoint, and more.