微软发布 MAI-Image-2.7-Preview 图像编辑模型,AA-Image-Editing v2.1 榜单排名第二
微软于 10 月 27 日推出图像编辑模型 MAI-Image-2.7-Preview,在 Artificial Analysis 的 AA-Image-Editing v2.1 榜单以 1,193 Elo 排名第二,仅次于 OpenAI 的 GPT Image 2.5 Sunburst (max)。
微软于 10 月 27 日推出图像编辑模型 MAI-Image-2.7-Preview,在 Artificial Analysis 的 AA-Image-Editing v2.1 榜单以 1,193 Elo 排名第二,仅次于 OpenAI 的 GPT Image 2.5 Sunburst (max)。
Microsoft AI 发布图像编辑模型 MAI Image 2.7 预览版,在 Arena Edit 排名第 3(使 Microsoft AI 成为第 2 的实验室),在 Artificial Analysis 图像编辑榜单排名第 2。
Microsoft's MAI-Image-2.7-Preview takes #2 on AA-Image-Editing v2.1, close behind OpenAI's GPT Image 2.5 Sunburst (max) and a clear step up from MAI-Image-2.6 MAI-Image-2.7-Preview is Microsoft AI's newest image editing model, succeeding MAI-Image-2.6, released in August. In our testing, MAI-Image-2.7-Preview scores higher than MAI-Image-2.6 on all 7 editing actions we measure, and ranks #1 on 4 of them: Scene & Style Edit, Text or Symbol Edits, Reasoning-Based Edit, and Enhancement & Restoration. This new model improves on MAI-Image-2.6 with the biggest gains in Composition & Framing (+57 Elo), Text or Symbol Edits (+49) and Scene & Style Edit (+48). We see its best use cases to be in Live-Action Film, Consumer and Frontier edits, where it ranks #1. MAI-Image-2.7-Preview is available today in MAI Playground for image editing only, with text to image stated to be coming soon. Congratulations to @MicrosoftAI on the release! See below for our detailed analysis and example outputs of MAI-Image-2.7-Preview in the Artificial Analysis Image Arena 🧵
MarkTechPost 盘点了 TypeSafe 于 2026 年 9 月 15 日发布决策模型 Jev 后约 4 周内出现的 20 个替代方案,来自 Cloudflare、OpenAI、Perplexity、Liquid AI、AWS Strands Labs 及独立开发者,按参数规模、许可证、硬件、定价、Jev API 兼容性和基准表现逐项比较。
微软宣布完成新一代 SOTA 基础模型 MAI-Base-2 的预训练,称其性能强于所有参测开源权重模型。该模型总参数 2.8T、活跃参数 80B,在 6,000 个 NVIDIA GB200 GPU 上训练 38 天,平均浮点算力利用率 21%、有效吞吐量 89%,预训练算力效率据称为 DeepSeek-V4-Pro-Base 的 4~12 倍。
Microsoft AI 宣布完成 MAI-Base-2 预训练,这是一个 800 亿激活、2.8T 总参数的 MoE 基础模型,在 held-out 评测中 BPB 低于所测全部开源权重模型,预训练 FLOPs 效率比 DeepSeek-V4-Pro-Base 高 4.2 至 12.3 倍。
推荐理由:官方披露了 MAI-Base-2 的完整预训练方法,读者可以从中了解 scaling ladder、Muon 优化和多轮 dropless MoE 等可借鉴的工程细节。
Microsoft 发布决策模型 Decision-1,用于分类、评估和路由决策,基于 Qwen3.5-9B,据称可引导智能体在复杂环境中行动。
微软发布 Microsoft-Decision-1 决策模型,专为结构化决策任务打造,现已上线 Microsoft Foundry 并接入 OpenRouter。该模型基于 Qwen3.5-9B 后训练,在 36 项基准测试中准确率最高,响应速度比 Quyet-1.0-Large 快 4.5 倍、比 GPT-6 Sol 快 35 倍。定价为每百万 Token 输入 0.042 美元,输出免费。
Introducing Microsoft-Decision-1, our new model for fast decision-making. It delivers top performance on structured decision tasks, outperforming both LLMs and other decision models in latency and quality. We’re already testing it across Microsoft for everything from incident response and quality control to scientific discovery.
微软在旧金山活动中宣布计划将 130B 参数的 MAI Code 1.1 Flash 模型引入 Windows 11 设备。
Microsoft AI 发布实时转写模型 MAI-Transcribe-2-Streaming,支持 60 种语言,首段结果仅约 100 毫秒,微软称其在 Artificial Analysis 准确率排名第一,限期内每小时音频 0.54 美元。
微软于 10 月 1 日发布首个实时流式语音转写模型 MAI-Transcribe-2-Streaming,支持 60 种语言和自动语言检测,可在讲话进行时持续输出文字。
Microsoft AI 发布流式转录模型 MAI-Transcribe-2-Streaming,在 Artificial Analysis 准确率榜排名第一,支持 60 种语言实时转录,收到音频约 100ms 即产出初步结果,内部评测显示字幕出现速度比最接近的竞品快 2 倍,介绍价 $0.54 每小时音频。
Microsoft AI has released MAI-Transcribe-2-Streaming, taking the #1 spot for Final Transcript accuracy and First Partial Transcript accuracy on AA-WER Streaming with 2.5% WER at 0.13s after end of speech MAI-Transcribe-2-Streaming is @MicrosoftAI's new streaming Speech to Text model, joining the non-streaming MAI-Transcribe-2. It leads streaming Final Transcript WER at 2.5%, ahead of the previous #1, SpaceXAI's Grok Voice Transcribe 2.0 at 2.7%, and returns that transcript in 0.13s rather than 0.49s. It is available for streaming transcription at $0.54 per hour of audio, at the higher end of pricing among the leading streaming models. Key takeaways ➤ Final Transcript: MAI-Transcribe-2-Streaming achieves 2.5% WER at 0.13s after end of speech, ranking #1 of 38 models. It is more accurate and faster than Grok Voice Transcribe 2.0 at 2.7% and 0.49s, Muse Voice Transcribe at 3.1% and 0.16s, and ElevenLabs Scribe v2 Realtime at 3.6% and 0.14s. It is also more accurate, though slightly slower, than Cartesia Ink Preview (external endpoints) at 3.1% and 0.11s ➤ First Partial Transcript: The model achieves 2.5% WER at 0.12s, ahead of Grok Voice Transcribe 2.0 at 3.4% and 0.49s, and ahead of Muse Voice Transcribe and ElevenLabs Scribe v2 Realtime on accuracy, both at 3.6%, and slightly faster than both at 0.12s versus 0.13s. It is more accurate but slower than Cartesia Ink-2 (external endpoints) at 4.0% and 0.07s ➤ Price: MAI-Transcribe-2-Streaming costs $0.54 per hour for streaming, or $9.00 per 1,000 minutes. This puts it level with Gemini 3.5 Transcribe Live at $9, above the $6.50 charged for ElevenLabs Scribe v2 Realtime and Deepgram Flux, more than twice Cartesia Ink-2 at $4, and three times Muse Voice Transcribe at $3. Non-streaming transcription costs $0.10 per hour, or $1.67 per 1,000 minutes Congrats to the @MicrosoftAI team on the launch! See more details below
Microsoft Research 在 Nature 发表逆合成预测模型 RetroChimera,结合基于 Transformer 的 R-SMILES 2 和图神经网络模型 NeuralLoc,用 learning-to-rank 集成两者互补预测。
微软于 9 月 4 日发布自研图像生成模型 MAI-Image-2.6-Flash,并通过 Microsoft Foundry 开放公开预览。该模型与旗舰版 MAI-Image-2.6 共享文生图和图像编辑核心能力,重点优化速度与成本;微软 AI 负责人 Mustafa Suleyman 称其生图速度是 GPT Image 2 的 2 倍,GPU 效率提高 72%。
Microsoft 于 9 月 4 日发布 MAI-Image-2.6,称其为迄今最强的图像模型,并面向开发者上线 Microsoft Foundry,同时推出面向低延迟、高吞吐场景的 MAI-Image-2.6-Flash。
推荐理由:原文给出排名、速度和效率数字,并说明两个版本分别面向精度与吞吐的不同场景,便于开发者按工作流选型。
Excited to see early customers already using Astra on Azure! https://azure.microsoft.com/en-us/blog/gpt-6-astra-frontier-intelligence-for-work-now-available-in-microsoft-foundry/
推荐理由:Satya Nadella 确认 GPT-6 Astra 已在 Microsoft Foundry 开放,早期客户已可在 Azure 上使用。
微软 9 月 3 日发布语音转文字模型 MAI-Transcribe-2,称其是自家最快、最准确、最便宜的转录模型,支持 60 种语言。
Microsoft AI 发布转录模型 MAI-Transcribe-2,称其在 FLEURS 基准 60 种语言上平均词错率 5.2% 排名第一,并占据 Artificial Analysis 准确率-延迟 Pareto 前沿。
推荐理由:官方给出具体基准排名、对比速度倍数和定价,读者可以据此评估它对现有语音转写方案的可替换性。
微软研究院推出 GigaPath-Flash 和 GigaTIME-Flash,旨在通过蒸馏技术大幅降低计算成本以支持大规模病理研究。GigaPath-Flash 将十亿参数模型压缩至 22M 参数,在保持竞争力的同时实现约 50 倍的算力节省;GigaTIME-Flash 则提升了空间蛋白质组学预测效率并改善了泛化能力。两款模型均以 Apache 2.0 协议开源,专为科研设计而非临床诊断,使研究人员能在单 GPU 上处理百万级切片数据。
Microsoft's MAI-Image-2.6-Preview lands at #1 on the Artificial Analysis Image Editing Leaderboard and takes #2 in Text to Image MAI-Image-2.6 is the newest model in Microsoft AI's MAI-Image family, announced August 10. Microsoft highlights stronger text rendering, better portraits and 3D imagery, and more polished commercial and photorealistic outputs. Like the MAI-Image-2.5 family, it handles both text to image generation and image editing. In the Artificial Analysis Image Arena, MAI-Image-2.6-Preview debuts at #1 on the Image Editing Leaderboard, ahead of Microsoft's own MAI-Image-2.5-Pro, Reve 2.1, and OpenAI's GPT Image 2, and giving Microsoft the top two spots on the board. In Text to Image it takes #2, behind only OpenAI's GPT Image 2 and ahead of Reve 2.1. On our refreshed Text to Image taxonomy, MAI-Image-2.6-Preview takes the top spot on 5 of the 19 category leaderboards (Material, Knowledge, Frontier, Retail & Ecommerce, and Marketing & Advertising), ahead of GPT Image 2, which leads every other category. MAI-Image-2.6 extends a rapid run of strong image releases from Microsoft AI. MAI-Image-2.5 debuted at #2 in Text to Image in June. MAI-Image-2.5-Pro, launched July 23, took #1 in Image Editing when we published our results last week. MAI-Image-2.6 now takes that top spot from its own sibling, and sits at #2 in Text to Image against MAI-Image-2.5-Pro's #8. MAI-Image-2.6 is available in the MAI Playground and in Private Preview on Microsoft Foundry. Congratulations to @MicrosoftAI on the release! See below for our analysis of MAI-Image-2.6-Preview and other leading models in the Artificial Analysis Image Arena 🧵
Skala 1.1,微软研究院更新的深度学习交换关联泛函,提供了更高的精度、在计算化学生态系统中更广泛的可及性,以及一个用于跟踪计算性能的动态基准。https://msft.it/6011aOXe5
Skala 1.1,微软研究院更新的深度学习交换关联泛函,提供了更高的精度、在计算化学生态系统中更广泛的可及性,以及一个用于跟踪计算性能的活基准。https://msft.it/6016aMXZn
微软研究院发布深度学习交换关联泛函 Skala 1.1。该模型基于比前代多 2.5 倍的数据训练,在 GMTKN55 基准测试的 55 个类别中 32 项获第一,以 meta-GGA 的计算成本实现了优于最昂贵全局杂化泛函的精度。Skala 现已集成至 CP2K,并正在接入 Psi4、FHI-aims、ORCA 和 VASP 等主流电子结构软件,同时推出动态基准以追踪性能优化。
MAI-Image-2.6-Preview by @MicrosoftAI has landed at #3 in Single Image Edit with 1,420 pts! This is +19 pts above MAI-Image-2.5, moving from #5 to #3. It sits 19 pts behind preliminary Grok Imagine Image 2.0 (low) at #2 and 43 pts behind GPT Image 2 (medium) at #1. By category, MAI-Image-2.6-Preview is #3 in across: - Product, Branding & Commercial Design - 3D Imaging & Modeling - Cartoon, Anime & Fantasy - Photorealistic & Cinematic Imagery #4 in Portraits and #8 in Text Rendering. The biggest jumps over MAI-Image-2.5 are in Text Rendering (+43 pts) and Product, Branding & Commercial Design (+38 pts). Congrats again to the @MicrosoftAI team on this release!
我们的首个推理模型 MAI-Thinking-1 从零开始构建,现已在 Microsoft Foundry 上线。为团队点赞!更多详情如下。
推荐理由:微软首个自研推理模型进入 Foundry,使用 Azure 的团队在推理模型选型上多了一个自研选项,来源可完全在微软体系内验证。
微软发布面向 GitHub Copilot 的代码模型 MAI Code 1.1 Flash,称其代码质量更优、token 效率提升 25%、成本仅为 6 月前代的四分之一。
微软发布升级版编程模型 MAI-Code-1.1-Flash,在 Terminal-Bench 2.1 测试中表现提升 22%,.NET 相关任务提升 15%,Token 生成速度提升 25%,完成相同任务所需 Token 减少 25%。
我们最新的代码模型比6月发布的模型效率提升25%,质量更高,成本仅为四分之一。现已上线GitHub Copilot——欢迎试用。