{"id":1280,"date":"2026-06-23T15:00:30","date_gmt":"2026-06-23T15:00:30","guid":{"rendered":"https:\/\/convly.ai\/?p=1280"},"modified":"2026-08-01T06:46:21","modified_gmt":"2026-08-01T06:46:21","slug":"open-vs-closed-ai-cost-gap-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/de\/open-vs-closed-ai-cost-gap-2026\/","title":{"rendered":"Offene vs. geschlossene KI im Jahr 2026: Die reale Kostenl\u00fccke (wir haben 29 Modelle bewertet)"},"content":{"rendered":"<p>Ist Open-Weight-KI tats\u00e4chlich g\u00fcnstiger als die gro\u00dfen propriet\u00e4ren APIs \u2013 und um wie viel? Wir haben die API-Preise aller 29 bewerteten Modelle in unserer <a href=\"\/de\/models\/\">Modelldatenbank<\/a>, auf einen einheitlichen gewichteten Kostenwert pro Million Tokens normiert und in Open-Weight- versus propriet\u00e4re Modelle unterteilt. Die L\u00fccke ist gr\u00f6\u00dfer \u2013 und deutlich konsistenter \u2013 als die meisten vermuten.<\/p>\n<div class=\"convly-tldr\">\n<h3>Wichtigste Erkenntnisse<\/h3>\n<ul>\n<li><strong>Die f\u00fcnf g\u00fcnstigsten Modelle im Jahr 2026 sind s\u00e4mtlich Open-Weight-Modelle. Die f\u00fcnf teuersten sind ausschlie\u00dflich propriet\u00e4r.<\/strong><\/li>\n<li>Die <strong>Ein typisches (medianes) Open-Weight-Modell kostet etwa 0,15 USD<\/strong> pro Million gewichteter Tokens; ein typisches propriet\u00e4res Modell kostet <strong>etwa 6,00 USD \u2013 eine 39-fache Differenz.<\/strong><\/li>\n<li>Im Durchschnitt kosten propriet\u00e4re Modelle <strong>etwa das 16-Fache<\/strong> der Open-Weight-Modelle.<\/li>\n<li>Bei allen 29 Modellen betr\u00e4gt die gesamte Preisspanne <strong>etwa das 890-Fache<\/strong> \u2013 von rund 0,02 bis 20 USD pro Million gewichteter Tokens.<\/li>\n<li>Dies ignoriert jedoch das Self-Hosting, das bei Open-Weight-Modellen die Kosten pro Token <em>vollst\u00e4ndig<\/em> eliminiert.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_86 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a7a0e78306f1\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Umschalten<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a7a0e78306f1\"  aria-label=\"Umschalten\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/de\/open-vs-closed-ai-cost-gap-2026\/#How_we_measured_it\" >So haben wir gemessen<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/de\/open-vs-closed-ai-cost-gap-2026\/#The_gap_in_one_table\" >Die L\u00fccke in einer Tabelle<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/de\/open-vs-closed-ai-cost-gap-2026\/#The_extremes_tell_the_story\" >Die Extremwerte erz\u00e4hlen die Geschichte<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/de\/open-vs-closed-ai-cost-gap-2026\/#Important_nuance_this_is_cost_not_capability\" >Wichtige Nuance: Hier geht es um Kosten, nicht um Leistungsf\u00e4higkeit<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/de\/open-vs-closed-ai-cost-gap-2026\/#Why_the_gap_is_structural\" >Warum die L\u00fccke strukturell bedingt ist<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/de\/open-vs-closed-ai-cost-gap-2026\/#Bottom_line\" >Fazit<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"How_we_measured_it\"><\/span>So haben wir gemessen<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Umfang<\/strong> \u2013 alle 29 Modelle in der Convly-Datenbank mit \u00f6ffentlich zug\u00e4nglichen API-Preisen.<\/li>\n<li><strong>Durchschnittlicher Kostenpreis<\/strong> \u2014 <code>(3 \u00d7 Eingabe + Ausgabe) \u00f7 4<\/code>, ein typisches Verh\u00e4ltnis von Eingabe zu Ausgabe von 3:1 bei realen API-Anfragen, sodass Modelle mit g\u00fcnstiger Eingabe, aber teurer Ausgabe direkt vergleichbar sind.<\/li>\n<li><strong>Klassifikation<\/strong> \u2013 \u201aOpen-Weight\u2018 = herunterladbare Gewichte, die Sie selbst hosten k\u00f6nnen (22 Modelle); \u201apropriet\u00e4r\u2018 = ausschlie\u00dflich \u00fcber API verf\u00fcgbar (7 Modelle).<\/li>\n<li><strong>Quellen<\/strong> \u2013 ver\u00f6ffentlichte API-Preise \u00fcber OpenRouter und DeepInfra, Juni 2026.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"The_gap_in_one_table\"><\/span>Die L\u00fccke in einer Tabelle<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Metrik (gewichteter Preis pro Million)<\/th>\n<th>Open-Weight (22)<\/th>\n<th>Propriet\u00e4r (7)<\/th>\n<th>L\u00fccke<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Durchschnitt<\/strong><\/td>\n<td>$0.50<\/td>\n<td>$8.16<\/td>\n<td><strong>16\u00d7<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>Median (typisches Modell)<\/strong><\/td>\n<td>$0.15<\/td>\n<td>$6.00<\/td>\n<td><strong>39\u00d7<\/strong><\/td>\n<\/tr>\n<tr>\n<td>G\u00fcnstigstes Modell der Gruppe<\/td>\n<td>$0,02 (Llama 3.1 8B)<\/td>\n<td>$2,00 (Claude Haiku 4.5)<\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>Teuerstes Modell der Gruppe<\/td>\n<td>$3,00 (Mistral Large 3)<\/td>\n<td>$20,00 (Claude Fable 5)<\/td>\n<td>\u2014<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"The_extremes_tell_the_story\"><\/span>Die Extremwerte erz\u00e4hlen die Geschichte<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Sortiert man alle 29 Modelle nach den gemittelten Kosten, zeigt sich ein deutliches Muster: Open-Weight-Modelle dominieren das untere Ende der Skala, propriet\u00e4re Modelle das obere Ende:<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>5 g\u00fcnstigste Modelle (alle Open-Weight)<\/th>\n<th>Gewichteter Preis pro Million US-Dollar<\/th>\n<th>5 teuersten Modelle (alle propriet\u00e4r)<\/th>\n<th>Gewichteter Preis pro Million US-Dollar<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Llama 3.1 8B<\/td>\n<td>$0.02<\/td>\n<td>Claude Fable 5<\/td>\n<td>$20.00<\/td>\n<\/tr>\n<tr>\n<td>Mistral 7B<\/td>\n<td>$0.02<\/td>\n<td>GPT-5.5<\/td>\n<td>$11.25<\/td>\n<\/tr>\n<tr>\n<td>Mistral NeMo 12B<\/td>\n<td>$0.03<\/td>\n<td>Claude Opus 4.8<\/td>\n<td>$10.00<\/td>\n<\/tr>\n<tr>\n<td>Gemma 3 4B<\/td>\n<td>$0.06<\/td>\n<td>Claude Sonnet 4.6<\/td>\n<td>$6.00<\/td>\n<\/tr>\n<tr>\n<td>Qwen3 8B<\/td>\n<td>$0.07<\/td>\n<td>Gemini 3.1 Pro<\/td>\n<td>$4.50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Kein propriet\u00e4res Modell befindet sich im g\u00fcnstigsten Drittel des Marktes, und kein Open-Weight-Modell im teuersten Drittel. Die einzige \u00dcberschneidungszone ist schmal: Das g\u00fcnstigste propriet\u00e4re Modell (Claude Haiku 4.5 f\u00fcr $2,00) liegt knapp unter dem teuersten Open-Weight-Modell (Mistral Large 3 f\u00fcr $3,00).<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Important_nuance_this_is_cost_not_capability\"><\/span>Wichtige Nuance: Hier geht es um Kosten, nicht um Leistungsf\u00e4higkeit<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Die teuersten Modelle f\u00fchren nach wie vor bei den anspruchsvollsten Denk- und Agentenaufgaben. In unserem begleitenden <a href=\"\/de\/ai-price-performance-index-2026\/\">KI-Preis-Leistungsindex<\/a> stellten wir fest, dass die Premium-Bezahlung f\u00fcr Spitzenmodelle nicht einen proportionalen Mehrwert, sondern lediglich die <em>letzte Punkte<\/em> an Intelligenz erwirbt. F\u00fcr den Gro\u00dfteil der Produktionsworkloads \u2013 Klassifizierung, Extraktion, RAG, Zusammenfassung, Chat \u2013 ist jedoch die Leistungsl\u00fccke zwischen einem guten Open-Weight-Modell und einem Spitzenmodell weit geringer als die 39\u00d7-Preisdifferenz. Oft zahlt man also das 39-Fache f\u00fcr die letzten 10\u201320 % an Funktionalit\u00e4t, die m\u00f6glicherweise gar nicht ben\u00f6tigt werden.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Why_the_gap_is_structural\"><\/span>Warum die L\u00fccke strukturell bedingt ist<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This isn&#8217;t a temporary discount war. Intense open-weight competition \u2014 Qwen, Llama, Gemma, DeepSeek and Mistral all shipping strong models under permissive licenses \u2014 has driven the price floor toward zero. Meanwhile frontier labs price for peak capability and enterprise willingness-to-pay. The result is a market that is bifurcating: a race-to-zero floor and a premium ceiling, with a widening canyon between them.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>Fazit<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>F\u00fcr kostenorientierte Produktionsumgebungen ist ein Open-Weight- oder Mid-Tier-Modell 2026 die rationale Standardwahl \u2013 und durch Self-Hosting entfallen die Kosten pro Token vollst\u00e4ndig (pr\u00fcfen Sie, welche Modelle Ihre GPU verarbeiten kann, mit unserem <a href=\"\/de\/llm-vram-calculator\/\">VRAM-Rechner<\/a>). Propriet\u00e4re Spitzenmodelle sollten ausschlie\u00dflich f\u00fcr wirklich anspruchsvollste Aufgaben reserviert werden. Berechnen Sie Ihre konkreten Kosten mithilfe des <a href=\"\/de\/ai-api-cost-calculator\/\">API-Kostenrechner<\/a> .<\/p>\n<p><em>Data: Convly <a href=\"https:\/\/convly.ai\/de\/models\/\">Datenbank f\u00fcr KI-Modelle<\/a> (API pricing via OpenRouter and DeepInfra). Blended cost uses a 3:1 input:output ratio. Figures current as of June 2026.<\/em><\/p>","protected":false},"excerpt":{"rendered":"<p>We priced all 29 models in our database and split them open vs proprietary. The 5 cheapest are all open-weight; the 5 most expensive all proprietary. The typical gap: 39\u00d7.<\/p>","protected":false},"author":1,"featured_media":1903,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[247],"tags":[813,421,454,745,423,812],"class_list":["post-1280","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-benchmarks","tag-cost-analysis","tag-deepseek","tag-llama","tag-llm-pricing","tag-open-source-ai","tag-open-vs-closed"],"_links":{"self":[{"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/posts\/1280","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/comments?post=1280"}],"version-history":[{"count":2,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/posts\/1280\/revisions"}],"predecessor-version":[{"id":1881,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/posts\/1280\/revisions\/1881"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/media\/1903"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/media?parent=1280"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/categories?post=1280"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/tags?post=1280"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}