{"id":1280,"date":"2026-06-23T15:00:30","date_gmt":"2026-06-23T15:00:30","guid":{"rendered":"https:\/\/convly.ai\/?p=1280"},"modified":"2026-06-23T15:00:30","modified_gmt":"2026-06-23T15:00:30","slug":"open-vs-closed-ai-cost-gap-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/pt\/open-vs-closed-ai-cost-gap-2026\/","title":{"rendered":"IA Aberta versus Fechada em 2026: A Diferen\u00e7a Real de Custo (Precificamos 29 Modelos)"},"content":{"rendered":"<p>A IA de pesos abertos \u00e9 realmente mais barata do que as grandes APIs propriet\u00e1rias \u2014 e em quanto? Calculamos os pre\u00e7os das APIs para todos os 29 modelos com pre\u00e7os divulgados em nossa <a href=\"\/pt\/models\/\">base de dados de modelos<\/a>, normalizamos cada um para um \u00fanico custo combinado por milh\u00e3o de tokens e os separamos em pesos abertos versus propriet\u00e1rios. A diferen\u00e7a \u00e9 maior \u2014 e muito mais consistente \u2014 do que a maioria das pessoas imagina.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principais conclus\u00f5es<\/h3>\n<ul>\n<li><strong>Os 5 modelos mais baratos em 2026 s\u00e3o todos de pesos abertos. Os 5 mais caros s\u00e3o todos propriet\u00e1rios.<\/strong><\/li>\n<li>O <strong>o custo t\u00edpico (mediana) de um modelo aberto \u00e9 de ~US$ 0,15<\/strong> por 1 milh\u00e3o de tokens combinados; o custo t\u00edpico de um modelo propriet\u00e1rio \u00e9 de <strong>~US$ 6,00 \u2014 uma diferen\u00e7a de 39\u00d7.<\/strong><\/li>\n<li>Em m\u00e9dia, os modelos propriet\u00e1rios custam <strong>~16\u00d7 mais<\/strong> do que os abertos.<\/li>\n<li>Entre todos os 29 modelos, a faixa completa de pre\u00e7os \u00e9 de <strong>~890\u00d7<\/strong> \u2014 de ~US$ 0,02 a US$ 20 por 1 milh\u00e3o de tokens combinados.<\/li>\n<li>E isso ignora a hospedagem pr\u00f3pria, que elimina totalmente o custo por token <em>para pesos abertos.<\/em> A diferen\u00e7a, em uma \u00fanica tabela<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a3c4aa3b8bfa\" 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-6a3c4aa3b8bfa\"  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\/pt\/open-vs-closed-ai-cost-gap-2026\/#How_we_measured_it\" >Como fizemos a medi\u00e7\u00e3o<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/pt\/open-vs-closed-ai-cost-gap-2026\/#The_gap_in_one_table\" >Os extremos contam a hist\u00f3ria<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/pt\/open-vs-closed-ai-cost-gap-2026\/#The_extremes_tell_the_story\" >Nuance importante: trata-se de custo, n\u00e3o de capacidade<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/pt\/open-vs-closed-ai-cost-gap-2026\/#Important_nuance_this_is_cost_not_capability\" >Por que a diferen\u00e7a \u00e9 estrutural<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/pt\/open-vs-closed-ai-cost-gap-2026\/#Why_the_gap_is_structural\" >Escopo<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/pt\/open-vs-closed-ai-cost-gap-2026\/#Bottom_line\" >Conclus\u00e3o<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"How_we_measured_it\"><\/span>Como fizemos a medi\u00e7\u00e3o<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>\u2014 todos os 29 modelos na base de dados da Convly com pre\u00e7os p\u00fablicos de API.<\/strong> (3 \u00d7 entrada + sa\u00edda) \u00f7 4<\/li>\n<li><strong>Custo combinado<\/strong> \u2014 <code>, uma propor\u00e7\u00e3o t\u00edpica de 3:1 entre entrada e sa\u00edda no tr\u00e1fego real de APIs, permitindo comparar diretamente modelos com entradas baratas mas sa\u00eddas caras.<\/code>\u2014 'pesos abertos' = pesos baix\u00e1veis que voc\u00ea pode hospedar localmente (22 modelos); 'propriet\u00e1rios' = apenas via API (7 modelos).<\/li>\n<li><strong>Classifica\u00e7\u00e3o<\/strong> Fontes<\/li>\n<li><strong>\u2014 pre\u00e7os de API publicados via OpenRouter e DeepInfra, junho de 2026.<\/strong> M\u00e9trica (US$ por 1 milh\u00e3o combinados)<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"The_gap_in_one_table\"><\/span>Os extremos contam a hist\u00f3ria<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Pesos abertos (22)<\/th>\n<th>Propriet\u00e1rios (7)<\/th>\n<th>Proprietary (7)<\/th>\n<th>Lacuna<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>M\u00e9dia<\/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>$6.00<\/td>\n<td><strong>39\u00d7<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Mais barato do grupo<\/td>\n<td>US$ 0,02 (Llama 3.1 8B)<\/td>\n<td>US$ 2,00 (Claude Haiku 4.5)<\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>Mais caro do grupo<\/td>\n<td>US$ 3,00 (Mistral Large 3)<\/td>\n<td>US$ 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>Nuance importante: trata-se de custo, n\u00e3o de capacidade<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Ao ordenar todos os 29 modelos pelo custo combinado, o padr\u00e3o \u00e9 n\u00edtido: modelos de pesos abertos dominam a faixa inferior, enquanto modelos propriet\u00e1rios ocupam a faixa superior:<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>5 mais baratos (todos de pesos abertos)<\/th>\n<th>Custo combinado por 1 milh\u00e3o<\/th>\n<th>5 mais caros (todos propriet\u00e1rios)<\/th>\n<th>Custo combinado por 1 milh\u00e3o<\/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>$4.50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>N\u00e3o h\u00e1 nenhum modelo propriet\u00e1rio entre os ter\u00e7o mais baratos do mercado, nem nenhum modelo de pesos abertos entre o ter\u00e7o mais caro. A \u00fanica zona de sobreposi\u00e7\u00e3o \u00e9 estreita: o modelo propriet\u00e1rio mais barato (Claude Haiku 4.5, US$ 2,00) fica logo abaixo do modelo de pesos abertos mais caro (Mistral Large 3, US$ 3,00).<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Important_nuance_this_is_cost_not_capability\"><\/span>Por que a diferen\u00e7a \u00e9 estrutural<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Os modelos mais caros ainda lideram nas tarefas mais dif\u00edceis de racioc\u00ednio e ag\u00eancia. Em nosso \u00edndice complementar <a href=\"\/pt\/ai-price-performance-index-2026\/\">\u00cdndice de Custo-Desempenho em IA<\/a> descobrimos que o pr\u00eamio dos modelos de ponta adquire os <em>\u00faltimos pontos<\/em> de intelig\u00eancia, n\u00e3o um valor proporcional. Contudo, para a maioria das cargas de trabalho em produ\u00e7\u00e3o \u2014 classifica\u00e7\u00e3o, extra\u00e7\u00e3o, RAG, resumo e chat \u2014 a lacuna de capacidade entre um bom modelo de pesos abertos e um modelo de ponta \u00e9 muito menor do que a lacuna de pre\u00e7o de 39\u00d7. Frequentemente, voc\u00ea paga 39\u00d7 pelos \u00faltimos 10\u201320% de capacidade de que talvez n\u00e3o precise.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Why_the_gap_is_structural\"><\/span>Escopo<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>Conclus\u00e3o<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Para implanta\u00e7\u00f5es em produ\u00e7\u00e3o sens\u00edveis ao custo, um modelo de pesos abertos ou de n\u00edvel intermedi\u00e1rio \u00e9 a escolha racional padr\u00e3o em 2026 \u2014 e a hospedagem pr\u00f3pria elimina totalmente o custo por token (verifique quais modelos sua GPU consegue executar com nossa <a href=\"\/pt\/llm-vram-calculator\/\">Calculadora de VRAM<\/a>). Reserve os modelos propriet\u00e1rios de ponta apenas para as tarefas realmente mais dif\u00edceis. Execute sua pr\u00f3pria an\u00e1lise de uso no <a href=\"\/pt\/ai-api-cost-calculator\/\">Calculadora de custo de API<\/a> para ver seus n\u00fameros exatos.<\/p>\n<p><em>Dados: Banco de dados de modelos da Convly AI (pre\u00e7os de API via OpenRouter e DeepInfra). O custo combinado considera uma propor\u00e7\u00e3o entrada:saida de 3:1. Valores atualizados at\u00e9 junho de 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":1281,"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\/pt\/wp-json\/wp\/v2\/posts\/1280","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/comments?post=1280"}],"version-history":[{"count":1,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/1280\/revisions"}],"predecessor-version":[{"id":1282,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/1280\/revisions\/1282"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media\/1281"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media?parent=1280"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/categories?post=1280"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/tags?post=1280"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}