{"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\/es\/open-vs-closed-ai-cost-gap-2026\/","title":{"rendered":"IA abierta frente a IA cerrada en 2026: la brecha real de costos (hemos cotizado 29 modelos)"},"content":{"rendered":"<p>\u00bfEs realmente m\u00e1s barata la IA de pesos abiertos que las grandes APIs propietarias, y en qu\u00e9 medida? Tomamos los precios de API de los 29 modelos con precios p\u00fablicos incluidos en nuestra <a href=\"\/es\/models\/\">Base de datos de modelos<\/a>, normalizamos cada uno a un \u00fanico coste combinado por mill\u00f3n de tokens y los dividimos en categor\u00edas de \u00abpesos abiertos\u00bb y \u00abpropietarios\u00bb. La brecha es mayor \u2014y mucho m\u00e1s constante\u2014 de lo que la mayor\u00eda supone.<\/p>\n<div class=\"convly-tldr\">\n<h3>Conclusiones clave<\/h3>\n<ul>\n<li><strong>Los 5 modelos m\u00e1s baratos en 2026 son todos de pesos abiertos. Los 5 m\u00e1s caros son todos propietarios.<\/strong><\/li>\n<li>El <strong>el modelo abierto t\u00edpico (mediana) cuesta aproximadamente 0,15 d\u00f3lares<\/strong> por cada mill\u00f3n de tokens combinados; el modelo propietario t\u00edpico cuesta <strong>aproximadamente 6,00 d\u00f3lares \u2014una brecha de 39 veces.<\/strong><\/li>\n<li>En promedio, los modelos propietarios cuestan <strong>aproximadamente 16 veces m\u00e1s<\/strong> que los abiertos.<\/li>\n<li>Entre los 29 modelos analizados, la diferencia total de precios es de <strong>aproximadamente 890 veces<\/strong> \u2014desde unos 0,02 hasta 20 d\u00f3lares por mill\u00f3n de tokens combinados.<\/li>\n<li>Y eso sin considerar el alojamiento propio, que elimina por completo el coste por token <em>en el caso de los pesos abiertos.<\/em> La brecha, resumida en una tabla<\/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-6a7a0e9e79b2b\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Alternar<\/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-6a7a0e9e79b2b\"  aria-label=\"Alternar\" \/><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\/es\/open-vs-closed-ai-cost-gap-2026\/#How_we_measured_it\" >C\u00f3mo lo medimos<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/es\/open-vs-closed-ai-cost-gap-2026\/#The_gap_in_one_table\" >La brecha, resumida en una tabla<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/es\/open-vs-closed-ai-cost-gap-2026\/#The_extremes_tell_the_story\" >Los extremos cuentan la historia<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/es\/open-vs-closed-ai-cost-gap-2026\/#Important_nuance_this_is_cost_not_capability\" >Matiz importante: esto se refiere al coste, no a la capacidad<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/es\/open-vs-closed-ai-cost-gap-2026\/#Why_the_gap_is_structural\" >Por qu\u00e9 la brecha es estructural<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/es\/open-vs-closed-ai-cost-gap-2026\/#Bottom_line\" >Conclusi\u00f3n<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"How_we_measured_it\"><\/span>C\u00f3mo lo medimos<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Alcance<\/strong> \u2014los 29 modelos incluidos en la base de datos de Convly con precios p\u00fablicos de API.<\/li>\n<li><strong>Coste combinado<\/strong> \u2014 <code>(3 \u00d7 entrada + salida) \u00f7 4<\/code>, una proporci\u00f3n t\u00edpica de tr\u00e1fico real de API de 3:1 entre entrada y salida, lo que permite comparar directamente modelos con entradas baratas pero salidas costosas.<\/li>\n<li><strong>Clasificaci\u00f3n<\/strong> \u2014 \u00abpesos abiertos\u00bb: pesos descargables que pueden alojarse localmente (22 modelos); \u00abpropietarios\u00bb: \u00fanicamente mediante API (7 modelos).<\/li>\n<li><strong>Fuentes<\/strong> \u2014 precios p\u00fablicos de API publicados en OpenRouter y DeepInfra, junio de 2026.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"The_gap_in_one_table\"><\/span>La brecha, resumida en una tabla<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>M\u00e9trica (d\u00f3lares combinados por mill\u00f3n)<\/th>\n<th>Pesos abiertos (22)<\/th>\n<th>Propietarios (7)<\/th>\n<th>Diferencia<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Promedio<\/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>Mediana (modelo t\u00edpico)<\/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>M\u00e1s econ\u00f3mico del grupo<\/td>\n<td>0,02 USD (Llama 3.1 8B)<\/td>\n<td>2,00 USD (Claude Haiku 4.5)<\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>M\u00e1s caro del grupo<\/td>\n<td>3,00 USD (Mistral Large 3)<\/td>\n<td>20,00 USD (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>Los extremos cuentan la historia<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Al ordenar los 29 modelos seg\u00fan su costo combinado, el patr\u00f3n es contundente: los modelos de pesos abiertos dominan la parte inferior de la tabla, mientras que los propietarios ocupan la superior:<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>5 modelos m\u00e1s econ\u00f3micos (todos de pesos abiertos)<\/th>\n<th>D\u00f3lares por mill\u00f3n combinados<\/th>\n<th>5 modelos m\u00e1s caros (todos propietarios)<\/th>\n<th>D\u00f3lares por mill\u00f3n combinados<\/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>No hay ning\u00fan modelo propietario entre el tercio m\u00e1s econ\u00f3mico del mercado, ni ning\u00fan modelo de pesos abiertos entre el tercio m\u00e1s caro. La \u00fanica zona de solapamiento es estrecha: el modelo propietario m\u00e1s econ\u00f3mico (Claude Haiku 4.5, 2,00 USD) se sit\u00faa justo por debajo del modelo de pesos abiertos m\u00e1s caro (Mistral Large 3, 3,00 USD).<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Important_nuance_this_is_cost_not_capability\"><\/span>Matiz importante: esto se refiere al coste, no a la capacidad<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Los modelos m\u00e1s caros siguen liderando en las tareas de razonamiento y agentividad m\u00e1s exigentes. En nuestro \u00edndice complementario <a href=\"\/es\/ai-price-performance-index-2026\/\">\u00cdndice de relaci\u00f3n precio-rendimiento de la IA<\/a> descubrimos que la prima de los modelos punteros adquiere los <em>\u00faltimos puntos<\/em> de inteligencia, no un valor proporcional. Sin embargo, para la mayor\u00eda de las cargas de trabajo productivas \u2014clasificaci\u00f3n, extracci\u00f3n, recuperaci\u00f3n aumentada por generaci\u00f3n (RAG), res\u00famenes y chat\u2014 la brecha de capacidades entre un buen modelo de pesos abiertos y un modelo puntero es mucho menor que la diferencia de precios de 39\u00d7. Con frecuencia, usted paga 39\u00d7 m\u00e1s por el \u00faltimo 10\u201320 % de capacidad que quiz\u00e1s no necesite.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Why_the_gap_is_structural\"><\/span>Por qu\u00e9 la brecha es estructural<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>Conclusi\u00f3n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Para entornos productivos sensibles al costo, un modelo de pesos abiertos o de gama media es la opci\u00f3n racional por defecto en 2026 \u2014y alojarlo localmente elimina por completo el costo por token (consulte qu\u00e9 modelos puede ejecutar su GPU con nuestra <a href=\"\/es\/llm-vram-calculator\/\">Calculadora de VRAM<\/a>). Reserve los modelos punteros propietarios \u00fanicamente para las tareas realmente m\u00e1s exigentes. Ejecute su propio uso a trav\u00e9s del <a href=\"\/es\/ai-api-cost-calculator\/\">Calculadora de costos de API<\/a> para conocer sus cifras exactas.<\/p>\n<p><em>Data: Convly <a href=\"https:\/\/convly.ai\/es\/models\/\">Base de datos de modelos de IA<\/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\/es\/wp-json\/wp\/v2\/posts\/1280","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/comments?post=1280"}],"version-history":[{"count":2,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/posts\/1280\/revisions"}],"predecessor-version":[{"id":1881,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/posts\/1280\/revisions\/1881"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/media\/1903"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/media?parent=1280"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/categories?post=1280"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/tags?post=1280"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}