{"id":109397,"date":"2025-10-28T16:56:11","date_gmt":"2025-10-28T16:56:11","guid":{"rendered":"https:\/\/ekamu.net\/?p=109397"},"modified":"2025-10-28T16:56:11","modified_gmt":"2025-10-28T16:56:11","slug":"cin-cikisli-minimax-m2-acik-kaynak-kodlu-yapay-zekalar-arasinda-zirveye-yerlesti","status":"publish","type":"post","link":"https:\/\/ekamu.net\/index.php\/2025\/10\/28\/cin-cikisli-minimax-m2-acik-kaynak-kodlu-yapay-zekalar-arasinda-zirveye-yerlesti\/","title":{"rendered":"\u00c7in \u00e7\u0131k\u0131\u015fl\u0131 MiniMax-M2, a\u00e7\u0131k kaynak kodlu yapay zekalar aras\u0131nda zirveye yerle\u015fti"},"content":{"rendered":"

<\/span> Bu y\u0131l\u0131n ba\u015f\u0131nda \u00e7\u0131kan DeepSeek-R1 ile birlikte Bat\u0131l\u0131 rakipleriyle kafa kafaya yar\u0131\u015facak yapay zekalar \u00e7\u0131karabilece\u011fini g\u00f6steren \u00c7in’den dikkat \u00e7ekici modeller gelmeye devam ediyor. \u00dcstelik DeepSeek-R1 gibi bu yeni modeller de a\u00e7\u0131k kaynak kodlu olarak kullan\u0131ma sunuluyor. Bunlar\u0131n son \u00f6rne\u011fi de performans\u0131yla dikkat \u00e7eken MiniMax-M2 <\/strong>oldu. <\/figure>\n

Yapay zeka analiz platformu Artificial Analysis taraf\u0131ndan yay\u0131mlanan Intelligence Index s\u0131ralamas\u0131nda 61 puan alan MiniMax-M2, a\u00e7\u0131k kaynakl\u0131 modeller aras\u0131nda en y\u00fcksek skoru elde ederken<\/strong>, genel s\u0131ralamada da d\u00fcnya be\u015fincili\u011fine y\u00fckseldi. GPT-5 ve Grok 4 gibi kapal\u0131 sistemlerin hemen arkas\u0131na yerle\u015fen model, Gemini 2.5 Pro ve DeepSeek-R1 gibi pop\u00fcler modellerin ise \u00f6n\u00fcnde yer al\u0131yor.<\/p>\n

Minimax-M2, Verimlili\u011fiyle de Dikkat \u00c7ekiyor<\/b><\/p>\n

MiniMax-M2\u2019nin ba\u015far\u0131s\u0131n\u0131n ard\u0131nda, modelin kulland\u0131\u011f\u0131 Mixture-of-Experts (MoE) mimarisi <\/strong>bulunuyor. Model, toplamda 230 milyar parametreye sahip olmas\u0131na ra\u011fmen, \u00e7\u0131kar\u0131m s\u0131ras\u0131nda bunlar\u0131n yaln\u0131zca 10 milyar\u0131n\u0131 etkinle\u015ftiriyor. Bu da modelin hem enerji verimlili\u011fini art\u0131r\u0131yor hem de donan\u0131m gereksinimlerini ciddi \u00f6l\u00e7\u00fcde azalt\u0131yor. Artificial Analysis raporuna g\u00f6re bu yakla\u015f\u0131m, DeepSeek V3.2\u2019nin 37 milyar ve Moonshot AI Kimi K2\u2019nin 32 milyar etkin parametresiyle k\u0131yasland\u0131\u011f\u0131nda \u00e7ok daha dengeli bir yap\u0131 sunuyor. Bu verimlilik sayesinde MiniMax M2, yaln\u0131zca d\u00f6rt adet NVIDIA H100 GPU \u00fczerinde \u00e7al\u0131\u015ft\u0131r\u0131labiliyor. Ayr\u0131ca modelin 100 token\/saniye civar\u0131ndaki \u00e7\u0131kar\u0131m h\u0131z\u0131<\/strong>, bir\u00e7ok rakibine g\u00f6re yakla\u015f\u0131k iki kat daha y\u00fcksek.<\/p>\n

MiniMax-M2\u2019nin performans\u0131n\u0131 \u00f6ne \u00e7\u0131karan bir di\u011fer unsur ise ara\u00e7 kullan\u0131m\u0131 ve yaz\u0131l\u0131m geli\u015ftirme gibi ajan tabanl\u0131 g\u00f6revlerdeki ba\u015far\u0131s\u0131.<\/strong>\u00a0Artificial Analysis uzmanlar\u0131, M2\u2019nin \u201ctalimat takibi ve ara\u00e7 kullan\u0131m\u0131 konular\u0131nda a\u00e7\u0131k kaynak modeller aras\u0131nda yeni bir standart olu\u015fturdu\u011funu” belirtiyor. Ba\u011f\u0131ms\u0131z geli\u015ftirici testlerinde ise modelin karma g\u00f6revlerde yakla\u015f\u0131k %95 do\u011fruluk oran\u0131na ula\u015ft\u0131\u011f\u0131, bunun da GPT-4o (%90) ve Claude 3.5 (%88-89) gibi rakiplerini ge\u00e7ti\u011fi bildiriliyor.<\/p>\n

Hugging Face ve GitHub \u00fczerinden eri\u015fime a\u00e7\u0131lan MiniMax M2\u2019nin bir di\u011fer dikkat \u00e7ekici y\u00f6n\u00fc de\u00a0kullan\u0131m maliyetinin d\u00fc\u015f\u00fckl\u00fc\u011f\u00fc<\/strong>. \u015eirket, modeli 1 milyon giri\u015f token\u2019\u0131 i\u00e7in 0,3 dolar, 1 milyon \u00e7\u0131k\u0131\u015f token\u2019\u0131 i\u00e7inse 1,2 dolar gibi olduk\u00e7a d\u00fc\u015f\u00fck bir \u00fccretle sunuyor. Bu rakam, Claude Sonnet 4.5\u2019in maliyetinin yakla\u015f\u0131k %8\u2019i d\u00fczeyinde.<\/p>\n

GPT, Grok, Gemini gibi geli\u015fmi\u015f yapay zekalarla kafa kafaya yar\u0131\u015fabilecek modellerin \u00c7inli \u015firkelter taraf\u0131ndan neredeyse \u00fccretsiz olarak sunuluyor olmas\u0131, Bat\u0131l\u0131 \u015firketler i\u00e7in \u00f6n\u00fcm\u00fczdeki d\u00f6nemde de y\u00fczle\u015filmesi gereken bir problem olacak <\/strong>gibig\u00f6r\u00fcn\u00fcyor. <\/strong>\u00c7\u00fcnk\u00fc DeepSeek’ten sonra Minimax’in de ortaya b\u00f6yle bir yapay zeka koymas\u0131, \u00c7inli \u015firketlerin en yeni modellerle bile rekabet edebilece\u011fini g\u00f6steriyor.<\/p>\n\n

<\/span><\/p>\n

Kaynak :\u00a0https:\/\/www.donanimhaber.com\/cin-cikisli-minimax-m2-deepseek-i-bile-golgede-birakti–197873<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Bu y\u0131l\u0131n ba\u015f\u0131nda \u00e7\u0131kan DeepSeek-R1 ile birlikte Bat\u0131l\u0131 rakipleriyle kafa kafaya yar\u0131\u015facak yapay zekalar \u00e7\u0131karabilece\u011fini g\u00f6steren \u00c7in’den dikkat \u00e7ekici modeller gelmeye devam ediyor. \u00dcstelik DeepSeek-R1 gibi bu yeni modeller de a\u00e7\u0131k kaynak kodlu …<\/p>\n","protected":false},"author":1,"featured_media":109398,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[6384,566,2529],"class_list":["post-109397","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-teknoloji","tag-deepseek","tag-modeli","tag-modeller"],"_links":{"self":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts\/109397","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/comments?post=109397"}],"version-history":[{"count":1,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts\/109397\/revisions"}],"predecessor-version":[{"id":109400,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts\/109397\/revisions\/109400"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/media\/109398"}],"wp:attachment":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/media?parent=109397"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/categories?post=109397"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/tags?post=109397"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}