{"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\/it\/llama-4-scout-vs-llama-4-maverick\/","title":{"rendered":"Llama 4 Scout contro Llama 4 Maverick: specifiche, prezzi e quale scegliere (2026)"},"content":{"rendered":"<p><strong>Llama 4 Scout<\/strong> vs <strong>Llama 4 Maverick<\/strong> \u2014 I due varianti di Llama 4 di Meta a confronto. Di seguito trovi un confronto dettagliato: specifiche tecniche, prezzi API, finestra contestuale, requisiti hardware locali e una raccomandazione chiara, basata sui dati, su quale modello scegliere.<\/p>\n<div class=\"cmp\">\n  <table class=\"cmp-table\">\n    <thead><tr><th>Specifiche<\/th><th><a href=\"https:\/\/convly.ai\/it\/model\/llama-4-scout\/\">Llama 4 Scout<\/a><\/th><th><a href=\"https:\/\/convly.ai\/it\/model\/llama-4-maverick\/\">Llama 4 Maverick<\/a><\/th><\/tr><\/thead>\n    <tbody>\n          <tr><td class=\"cmp-spec\">Sviluppatore<\/td><td class=\"\">Meta<\/td><td class=\"\">Meta<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Tipo<\/td><td class=\"\">Multimodale (MoE)<\/td><td class=\"\">Multimodale (MoE)<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Parametri<\/td><td class=\"\">109 miliardi totali \/ 17 miliardi attivi (MoE)<\/td><td class=\"\">400 miliardi totali \/ 17 miliardi attivi (MoE)<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Finestra contestuale<\/td><td class=\"cmp-win\">10 milioni<\/td><td class=\"\">1 milione<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Modalit\u00e0<\/td><td class=\"\">Testo, immagine \u2192 testo<\/td><td class=\"\">Testo, immagine \u2192 testo<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Licenza<\/td><td class=\"\">Llama 4 Community (limitata all\u2019UE)<\/td><td class=\"\">Llama 4 Community (limitata all\u2019UE)<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Pesi aperti<\/td><td class=\"\">\u2705 S\u00ec<\/td><td class=\"\">\u2705 S\u00ec<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Costo input ($\/1 milione)<\/td><td class=\"cmp-win\">$0.1<\/td><td class=\"\">$0.15<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Costo output ($\/1 milione)<\/td><td class=\"\">$0.3<\/td><td class=\"\">$0.6<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">VRAM (4 bit)<\/td><td class=\"\">~65 GB<\/td><td class=\"\">~240 GB<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">GPU minima (locale)<\/td><td class=\"\">H100 80 GB \/ Mac 128 GB<\/td><td class=\"\">Server multi-GPU<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Data di rilascio<\/td><td class=\"\">2025<\/td><td class=\"\">2025<\/td><\/tr>\n        <\/tbody>\n  <\/table>\n\n    <div class=\"cmp-verdict\">\n    <h3>Principali differenze<\/h3>\n    <ul><li><strong>Costo:<\/strong> Llama 4 Scout \u00e8 <strong>il 75% pi\u00f9 economica<\/strong> di Llama 4 Maverick in termini di costo medio per token.<\/li><li><strong>Contesto:<\/strong> Llama 4 Scout offre una finestra contestuale superiore (10 milioni contro 1 milione), risultando quindi pi\u00f9 adatta per documenti lunghi, grandi codebase e input RAG estesi.<\/li><li><strong>Apertura:<\/strong> entrambi hanno pesi aperti, quindi entrambi possono essere ospitati autonomamente o sottoposti a fine-tuning. Confronta le esigenze di VRAM indicate sopra per verificare quali modelli la tua GPU \u00e8 in grado di eseguire.<\/li><li><strong>Esegui Llama 4 Scout in locale:<\/strong> ~65 GB in quantizzazione 4-bit (GPU minima richiesta: H100 80 GB \/ Mac 128 GB).<\/li><li><strong>Esegui Llama 4 Maverick in locale:<\/strong> ~~240 GB a 4 bit (server multi-GPU minimo).<\/li><\/ul>\n  <\/div>\n\n    <div class=\"cmp-rec\">\n    <h3>Quale scegliere?<\/h3>\n    <p><strong>Seleziona Llama 4 Scout<\/strong> se desideri un costo inferiore per token in carichi di lavoro ad alto volume oppure hai bisogno di una finestra di contesto pi\u00f9 ampia.<\/p>\n    <p><strong>Scegli Llama 4 Maverick<\/strong> se si integra bene nel tuo stack esistente o se preferisci Meta.<\/p>\n    <p class=\"cmp-tools\">\u2192 Stima i costi reali con il <a href=\"\/it\/ai-api-cost-calculator\/\">Calcolatore costi API<\/a> \u00b7 verifica l'hardware locale con il <a href=\"\/it\/llm-vram-calculator\/\">Calcolatore VRAM<\/a> \u00b7 esplora tutti i <a href=\"\/it\/models\/\">30+ modelli<\/a>.<\/p>\n  <\/div>\n<\/div>\n\n<p>Tutte le specifiche e i prezzi sono recuperati in tempo reale dal nostro <a href=\"\/it\/models\/\">Database di modelli AI<\/a> e mantenuti aggiornati. Confronta uno qualsiasi dei due modelli con altri oppure stima la tua spesa mensile con i calcolatori gratuiti sopra indicati.<\/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\/it\/wp-json\/wp\/v2\/posts\/1277","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/comments?post=1277"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/1277\/revisions"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media?parent=1277"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/categories?post=1277"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/tags?post=1277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}