18 releases covered on ThursdAI · microsoft.com ↗
Microsoft ships MAI-Code-1-Flash into GitHub Copilot
Part of the seven-model MAI launch at Build 2026, MAI-Code-1-Flash is Microsoft AI's fast coding model and ships directly into GitHub Copilot. The panel saw it as a sign Microsoft intends to serve its own models inside its developer surfaces instead of relying solely on OpenAI.
Microsoft launches MAI-Thinking-1, a 1T MoE trained from scratch
Microsoft AI used Build 2026 to launch seven MAI models, headlined by MAI-Thinking-1, a 1T total, 35B active MoE reasoning model trained from scratch on 33T tokens without distillation. The panel read the launch as Microsoft becoming a frontier model lab in its own right rather than only an OpenAI distribution channel.
Microsoft MAI-Image-2.5 jumps to #3 on Arena text-to-image
MAI-Image-2.5 jumped to number two on Arena's image-to-image leaderboard shortly after launch, with notable strength in image cleanup, backgrounds, documents, and diagrams. Hands-on tests on the show were mixed, and it is publicly accessible through playground.microsoft.ai.
Microsoft's DELEGATE-52 exposes stealthy document corruption
Microsoft released the DELEGATE-52 benchmark showing GPT-5.4 loses 28% of document content after 20 iterative edits. Frontier models corrupt documents stealthily while preserving structure, making the degradation hard to notice.
Microsoft MAI releases MAI-Image-2 image generation model
MAI-Image-2 is Microsoft's new in-house image generation model, debuting at #3 in image-gen rankings as part of the MAI three-model release. The panel compared its positioning against specialist image products and foundation-model APIs.
Microsoft MAI ships MAI-Transcribe-1, ranked #1 in transcription
Microsoft's MAI lab released MAI-Transcribe-1, an in-house speech transcription model that debuted at #1 in transcription quality. It is part of a three-model drop showing Microsoft expanding its first-party model stack beyond its OpenAI dependence.
Microsoft MAI debuts MAI-Voice-1 expressive voice model
MAI-Voice-1 is Microsoft's expressive voice model, the third piece of the MAI in-house model drop alongside transcription and image generation. The panel discussed how Microsoft's first-party voice stack compares to specialist voice providers.
Microsoft shares VibeVoice-Realtime-0.5B with ~300ms latency TTS
Microsoft published VibeVoice-Realtime-0.5B on Hugging Face, a small realtime text-to-speech model claiming roughly 300ms latency. The show framed it as more evidence that sub-second audio response is becoming table stakes for production voice agents.
Microsoft ships Fara-7B, a 7B on-device computer use agent
Microsoft Research released Fara-7B, a best-in-class 7B-parameter vision-language model for computer use that runs on-device. It scores 73.5% on WebVoyager, beating OpenAI's computer-use preview while being small enough to run locally.
Microsoft adds agentic powers and voice to Copilot Mode in Edge
Microsoft answered Atlas with agentic enhancements to Copilot Mode in Edge, including a voice mode that can see and discuss the current page, plus broader Copilot updates (and Clippy back as an easter egg via the Mico avatar). In Alex's hands-on testing the agentic features did not actually work, so real-world parity with Atlas and Comet is unproven.
Microsoft makes every Windows 11 PC an AI PC with Copilot voice input
Microsoft announced that every Windows 11 machine becomes an 'AI PC,' adding 'Hey Copilot' voice input and deeper agentic Copilot integration at the OS level. The panel discussed it as a sign of AI assistants moving into the default computing experience.
Microsoft's MAI-DxO hits 85.5% on NEJM diagnostic cases vs 20% for doctors
Microsoft AI published MAI-DxO, a medical diagnostic orchestration system that reached 85.5% accuracy on challenging NEJM-style cases compared to roughly 20% for practicing physicians. The result is framed as a systems win rather than a single-model win, suggesting orchestration may outperform individual models in high-stakes expert workflows.
Microsoft ships Phi-4-reasoning and Phi-4-reasoning-plus (14B, MIT)
Microsoft fine-tuned the 14B Phi-4 on 1.4M curated chain-of-thought traces (SFT) and added a small RL stage (Plus variant) to create two MIT-licensed reasoning models. They punch far above their weight: Phi-4-reasoning-plus outperforms DeepSeek-R1-Distill-70B on AIME 25 (78% vs 51%) and sits within a few points of the full 671B DeepSeek-R1, while running on a single GPU with explicit <think> scaffolding.
Microsoft releases BitNet 1.58-bit model weights on Hugging Face
Microsoft published BitNet (listed in the show notes as BitNet v1.5), its native 1.58-bit quantized LLM, as open weights on Hugging Face. The ternary-weight approach targets extremely efficient CPU inference at a fraction of the memory of standard models.
Microsoft releases Phi-4-multimodal and Phi-4-mini open weights
Microsoft expanded the Phi family with Phi-4-multimodal-instruct, a small open-weights model that handles text, vision, and audio in a single model, alongside a compact Phi-4-mini. The weights shipped on Hugging Face, continuing Microsoft's push for capable small models that can run on-device.
Microsoft unveils Majorana 1 quantum chip and a new state of matter
Microsoft announced the Majorana 1 quantum chip alongside a claimed new state of matter called topological superconductivity, carving a new path for quantum computing. Alex called the announcement 'absolutely mind blowing' as a potential big deal for the future of computing.
Microsoft MUSE generates playable game worlds from a single second of video
Microsoft's MUSE can generate minutes of playable gameplay from just a single second of video frames and controller actions, preserving screen elements like health bars and percentages. It is based on the World and Human Action Model (WHAM) architecture, trained on a billion gameplay images from Xbox, with the model released on Hugging Face.
Microsoft ships OmniParser v2 for faster screen parsing in GUI agents
Microsoft released OmniParser v2, a better and faster screen-parsing model that converts UI screenshots into structured elements for GUI agents. It improves the computer-use agent stack and is available with a public Gradio demo.
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