{"id":1277,"date":"2026-06-23T14:45:04","date_gmt":"2026-06-23T14:45:04","guid":{"rendered":"https:\/\/convly.ai\/?p=1277"},"modified":"2026-06-23T14:45:04","modified_gmt":"2026-06-23T14:45:04","slug":"llama-4-scout-vs-llama-4-maverick","status":"publish","type":"post","link":"https:\/\/convly.ai\/es\/llama-4-scout-vs-llama-4-maverick\/","title":{"rendered":"Llama 4 Scout frente a Llama 4 Maverick: especificaciones, precios y cu\u00e1l elegir (2026)"},"content":{"rendered":"<p><strong>Llama 4 Scout<\/strong> vs <strong>Llama 4 Maverick<\/strong> \u2014 Las dos variantes de Llama 4 de Meta comparadas. A continuaci\u00f3n se muestra una comparaci\u00f3n detallada: especificaciones t\u00e9cnicas, precios de API, ventana de contexto, requisitos de hardware local y una recomendaci\u00f3n clara, basada en datos, sobre cu\u00e1l elegir.<\/p>\n<div class=\"cmp\">\n  <table class=\"cmp-table\">\n    <thead><tr><th>Especificaciones<\/th><th><a href=\"https:\/\/convly.ai\/es\/model\/llama-4-scout\/\">Llama 4 Scout<\/a><\/th><th><a href=\"https:\/\/convly.ai\/es\/model\/llama-4-maverick\/\">Llama 4 Maverick<\/a><\/th><\/tr><\/thead>\n    <tbody>\n          <tr><td class=\"cmp-spec\">Desarrollador<\/td><td class=\"\">Meta<\/td><td class=\"\">Meta<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Tipo<\/td><td class=\"\">Multimodal (MoE)<\/td><td class=\"\">Multimodal (MoE)<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Par\u00e1metros<\/td><td class=\"\">109B totales \/ 17B activos (MoE)<\/td><td class=\"\">400\u202f000 millones totales \/ 17\u202f000 millones activos (MoE)<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Ventana de contexto<\/td><td class=\"cmp-win\">10M<\/td><td class=\"\">1 mill\u00f3n<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Modalidad<\/td><td class=\"\">Texto, imagen \u2192 texto<\/td><td class=\"\">Texto, imagen \u2192 texto<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Licencia<\/td><td class=\"\">Llama 4 Community (restringido en la UE)<\/td><td class=\"\">Llama 4 Community (restringido en la UE)<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Pesos abiertos<\/td><td class=\"\">\u2705 S\u00ed<\/td><td class=\"\">\u2705 S\u00ed<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Precio de entrada ($\/mill\u00f3n)<\/td><td class=\"cmp-win\">$0.1<\/td><td class=\"\">$0.15<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Precio de salida ($\/mill\u00f3n)<\/td><td class=\"\">$0.3<\/td><td class=\"\">$0.6<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">VRAM (4 bits)<\/td><td class=\"\">~65 GB<\/td><td class=\"\">~240 GB<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">GPU m\u00ednima (local)<\/td><td class=\"\">H100 de 80 GB \/ Mac de 128 GB<\/td><td class=\"\">Servidor multi-GPU<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Lanzado<\/td><td class=\"\">2025<\/td><td class=\"\">2025<\/td><\/tr>\n        <\/tbody>\n  <\/table>\n\n    <div class=\"cmp-verdict\">\n    <h3>Diferencias clave<\/h3>\n    <ul><li><strong>Coste:<\/strong> Llama 4 Scout es <strong>un 75 % m\u00e1s econ\u00f3mico<\/strong> que Llama 4 Maverick en t\u00e9rminos de coste promedio por token.<\/li><li><strong>Contexto:<\/strong> Llama 4 Scout destaca en la ventana de contexto (10M frente a 1M), lo que lo hace ideal para documentos extensos, grandes bases de c\u00f3digo y entradas RAG de gran tama\u00f1o.<\/li><li><strong>Apertura:<\/strong> ambos tienen pesos abiertos, por lo que cualquiera puede alojarse localmente o ajustarse finamente. Compara sus necesidades de VRAM arriba para ver qu\u00e9 GPU puedes utilizar.<\/li><li><strong>Ejecuci\u00f3n local de Llama 4 Scout:<\/strong> ~65 GB a 4 bits (m\u00ednimo: H100 de 80 GB \/ Mac de 128 GB).<\/li><li><strong>Ejecute Llama 4 Maverick localmente:<\/strong> ~240 GB a 4 bits (servidor multi-GPU m\u00ednimo).<\/li><\/ul>\n  <\/div>\n\n    <div class=\"cmp-rec\">\n    <h3>\u00bfCu\u00e1l deber\u00edas elegir?<\/h3>\n    <p><strong>Selecciona Llama 4 Scout<\/strong> si buscas un menor coste por token para cargas de trabajo de alto volumen, o necesitas una ventana de contexto m\u00e1s amplia.<\/p>\n    <p><strong>Elija Llama 4 Maverick<\/strong> si se integra bien en tu pila tecnol\u00f3gica existente o si prefieres Meta.<\/p>\n    <p class=\"cmp-tools\">\u2192 Estima los costes reales en la <a href=\"\/es\/ai-api-cost-calculator\/\">Calculadora de costes de API<\/a> \u00b7 verifica el hardware local en la <a href=\"\/es\/llm-vram-calculator\/\">Calculadora de VRAM<\/a> \u00b7 explora todos los <a href=\"\/es\/models\/\">m\u00e1s de 30 modelos<\/a>.<\/p>\n  <\/div>\n<\/div>\n\n<p>Todas las especificaciones y precios se obtienen en tiempo real de nuestra <a href=\"\/es\/models\/\">Base de datos de modelos de IA<\/a> y se mantienen actualizados. Compara cualquiera de estos modelos con otros, o estima tu gasto mensual con las calculadoras gratuitas anteriores.<\/p>","protected":false},"excerpt":{"rendered":"<p>Llama 4 Scout vs Llama 4 Maverick compared: specs, API pricing, context window, VRAM and a clear verdict on which model to choose in 2026.<\/p>","protected":false},"author":1,"featured_media":0,"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":[246],"tags":[395,795,811],"class_list":["post-1277","post","type-post","status-publish","format-standard","hentry","category-ai-comparisons","tag-ai-model-comparison","tag-llama-4-maverick","tag-llama-4-scout"],"_links":{"self":[{"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/posts\/1277","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=1277"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/posts\/1277\/revisions"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/media?parent=1277"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/categories?post=1277"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/tags?post=1277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}