[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-microsoft-seven-ai-models-openai-anthropic-build-2026-en":3,"article-related-microsoft-seven-ai-models-openai-anthropic-build-2026-en":31,"series-model-release-fecde3d7-a7ff-475b-b9d3-330fac386b58":84},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":23,"views":27,"created_at":28,"published_at":29,"topic_cluster_id":30},"fecde3d7-a7ff-475b-b9d3-330fac386b58","microsoft-seven-ai-models-openai-anthropic-build-2026-en","7 Microsoft AI models aim at OpenAI and Anthropic","\u003Cp data-speakable=\"summary\">\u003Ca href=\"\u002Ftag\u002Fmicrosoft\">Microsoft\u003C\u002Fa> unveiled seven in-house AI models at Build 2026 to reduce reliance on OpenAI and \u003Ca href=\"\u002Ftag\u002Fanthropic\">Anthropic\u003C\u002Fa>.\u003C\u002Fp>\u003Cp>Microsoft unveiled seven in-house AI models at its Build 2026 conference in San Francisco on 3 June, marking a sharper push to control more of its AI stack.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>項目\u003C\u002Fth>\u003Cth>數值\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Models unveiled\u003C\u002Ftd>\u003Ctd>7\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Build event date\u003C\u002Ftd>\u003Ctd>3 June 2026\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>MAI-Thinking-1 active parameters\u003C\u002Ftd>\u003Ctd>35 billion\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Context window\u003C\u002Ftd>\u003Ctd>256,000 tokens\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Majorana 2 reliability claim\u003C\u002Ftd>\u003Ctd>1,000x more reliable\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>What changed\u003C\u002Fh2>\u003Cp>The headline model is MAI-Thinking-1, Microsoft’s \u003Ca href=\"\u002Fnews\u002Fmicrosoft-first-reasoning-model-tracker-plain-english-en\">first reasoning model\u003C\u002Fa>. It was trained from scratch on commercially licensed data, not distilled from another company’s system.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780642972169-qict.png\" alt=\"7 Microsoft AI models aim at OpenAI and Anthropic\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>Microsoft said MAI-Thinking-1 is built for multi-step instructions, long-context reasoning and code generation. The model has 35 billion active parameters and a 256,000-token context window.\u003C\u002Fp>\u003Cp>Microsoft also launched MAI-Code-1-Flash, a coding model that turns text prompts into source code for apps and websites. It is rolling out in \u003Ca href=\"\u002Fnews\u002F5-github-copilot-plan-changes-for-users-en\">GitHub Copilot\u003C\u002Fa> and Visual Studio Code.\u003C\u002Fp>\u003Cul>\u003Cli>Microsoft says MAI-Thinking-1 beat OpenAI’s GPT-5.5 on quality in a McKinsey tuning test.\u003C\u002Fli>\u003Cli>The company says that result came with up to 10x better cost efficiency, using public pricing data.\u003C\u002Fli>\u003Cli>In blind tests run by Surge, MAI-Thinking-1 was preferred over Anthropic’s Claude Sonnet 4.6.\u003C\u002Fli>\u003Cli>Microsoft says it matches Claude Opus 4.6 on coding benchmarks.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Satya Nadella said companies should move from “consuming a frontier model” to participating in it. Mustafa Suleyman, who leads Microsoft AI, framed the launch as a step toward more model independence after years of heavy spending on outside AI firms.\u003C\u002Fp>\u003Ch2>Why it matters\u003C\u002Fh2>\u003Cp>The new models let Microsoft run more AI workloads on Azure instead of paying third-party model providers. That could lower \u003Ca href=\"\u002Ftag\u002Finference\">inference\u003C\u002Fa> costs and give the company more room to price \u003Ca href=\"\u002Ftag\u002Fcopilot\">Copilot\u003C\u002Fa> and other \u003Ca href=\"\u002Ftag\u002Fdeveloper-tools\">developer tools\u003C\u002Fa> aggressively.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780642966348-j2e1.png\" alt=\"7 Microsoft AI models aim at OpenAI and Anthropic\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>For developers, the immediate impact is broader access inside Microsoft’s own products. For the market, it is another sign that Microsoft wants to compete with OpenAI and Anthropic even as it remains one of the biggest backers of both.\u003C\u002Fp>\u003Cp>The timing also matters: Microsoft’s biggest AI bets are heading toward IPO plans, which could change how much leverage the company has over its partners. The question now is whether Microsoft can turn in-house models into a real product edge, not just a cost-saving move.\u003C\u002Fp>","Microsoft unveiled seven in-house AI models at Build 2026, including MAI-Thinking-1 and MAI-Code-1-Flash, to cut costs and rival OpenAI and Anthropic.","www.euronews.com","https:\u002F\u002Fwww.euronews.com\u002Fnext\u002F2026\u002F06\u002F03\u002Fmicrosoft-launches-its-own-ai-models-to-take-on-openai-and-anthropic",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780642972169-qict.png","model-release","en","b5889da8-fa42-44ed-89a7-3347655b388d",[17,18,19,20,21,22],"Microsoft","Build 2026","AI models","OpenAI","Anthropic","GitHub Copilot",[24,25,26],"Microsoft launched seven in-house AI models at Build 2026.","MAI-Thinking-1 is its first reasoning model, trained from scratch.","The move is meant to cut model costs and reduce dependence on OpenAI and Anthropic.",0,"2026-06-05T07:02:24.142391+00:00","2026-06-05T07:02:24.132+00:00","1bae1133-d241-4581-9332-fbf39690c319",{"tags":32,"relatedLang":43,"relatedPosts":47},[33,35,37,39,41],{"name":18,"slug":34},"build-2026",{"name":17,"slug":36},"microsoft",{"name":20,"slug":38},"openai",{"name":21,"slug":40},"anthropic",{"name":19,"slug":42},"ai-models",{"id":15,"slug":44,"title":45,"language":46},"microsoft-seven-ai-models-openai-anthropic-build-2026-zh","7 款 Microsoft AI 模型登場","zh",[48,54,60,66,72,78],{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"9d05354a-def7-4387-815f-363ef1feead7","microsoft-seven-homegrown-ai-models-openai-dependence-en","7 Microsoft AI models aim to cut OpenAI 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Ranked","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780396386502-5z7x.png","2026-06-02T10:32:38.029835+00:00",{"id":79,"slug":80,"title":81,"cover_image":82,"image_url":82,"created_at":83,"category":13},"97d1ef0a-fdc0-4421-abb1-e1e8a9c5ba8e","llama-3-1-70b-specs-benchmarks-deployment-en","Llama 3.1 70B: Specs, Benchmarks, Deployment","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780395489574-1mhf.png","2026-06-02T10:17:33.495371+00:00",[85,90,95,100,105,110,115,120,125,130],{"id":86,"slug":87,"title":88,"created_at":89},"d4cffde7-9b50-4cc7-bb68-8bc9e3b15477","nvidia-rubin-ai-supercomputer-en","NVIDIA Unveils Rubin: A Leap in AI Supercomputing","2026-03-25T16:24:35.155565+00:00",{"id":91,"slug":92,"title":93,"created_at":94},"eab919b9-fbac-4048-89fc-afad6749ccef","google-gemini-ai-innovations-2026-en","Google's AI Leap with Gemini 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