{"id":1274,"date":"2026-06-23T14:45:04","date_gmt":"2026-06-23T14:45:04","guid":{"rendered":"https:\/\/convly.ai\/?p=1274"},"modified":"2026-06-23T14:45:04","modified_gmt":"2026-06-23T14:45:04","slug":"mistral-7b-vs-llama-3-1-8b","status":"publish","type":"post","link":"https:\/\/convly.ai\/it\/mistral-7b-vs-llama-3-1-8b\/","title":{"rendered":"Mistral 7B vs Llama 3.1 8B: specifiche, prezzi e quale scegliere (2026)"},"content":{"rendered":"<p><strong>Mistral 7B<\/strong> vs <strong>Llama 3.1 8B<\/strong> \u2014 i classici modelli locali compatti, rivisitati. Di seguito trovi un confronto completo: 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\/mistral-7b\/\">Mistral 7B<\/a><\/th><th><a href=\"https:\/\/convly.ai\/it\/model\/llama-3-1-8b\/\">Llama 3.1 8B<\/a><\/th><\/tr><\/thead>\n    <tbody>\n          <tr><td class=\"cmp-spec\">Sviluppatore<\/td><td class=\"\">Mistral AI<\/td><td class=\"\">Meta<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Tipo<\/td><td class=\"\">LLM (densa)<\/td><td class=\"\">LLM (densa)<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Parametri<\/td><td class=\"\">7B<\/td><td class=\"\">8B<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Finestra contestuale<\/td><td class=\"\">32K<\/td><td class=\"cmp-win\">128K<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Modalit\u00e0<\/td><td class=\"\">Testo \u2192 Testo<\/td><td class=\"\">Testo \u2192 Testo<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Licenza<\/td><td class=\"\">Apache 2.0 (open)<\/td><td class=\"\">Llama 3.1 Community (open)<\/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=\"\">$0.02<\/td><td class=\"\">$0.02<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Costo output ($\/1 milione)<\/td><td class=\"\">$0.03<\/td><td class=\"\">$0.03<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">VRAM (4 bit)<\/td><td class=\"\">~4,5 GB<\/td><td class=\"\">~5 GB<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">GPU minima (locale)<\/td><td class=\"\">Qualsiasi GPU da 6 GB<\/td><td class=\"\">Qualsiasi GPU da 8 GB<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Data di rilascio<\/td><td class=\"\">2023<\/td><td class=\"\">2024<\/td><\/tr>\n        <\/tbody>\n  <\/table>\n\n    <div class=\"cmp-verdict\">\n    <h3>Principali differenze<\/h3>\n    <ul><li><strong>Costo:<\/strong> Mistral 7B e Llama 3.1 8B hanno prezzi simili, con una differenza inferiore al 15%.<\/li><li><strong>Contesto:<\/strong> Llama 3.1 8B offre una finestra contestuale superiore (128K contro 32K), risultando pi\u00f9 adatto 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 Mistral 7B in locale:<\/strong> ~~4,5 GB in quantizzazione a 4 bit (minimo: qualsiasi GPU da 6 GB).<\/li><li><strong>Esegui Llama 3.1 8B in locale:<\/strong> ~~5 GB in quantizzazione a 4 bit (minimo: qualsiasi GPU da 8 GB).<\/li><\/ul>\n  <\/div>\n\n    <div class=\"cmp-rec\">\n    <h3>Quale scegliere?<\/h3>\n    <p><strong>Scegli Mistral 7B<\/strong> se si integra nel tuo stack esistente o se preferisci Mistral AI.<\/p>\n    <p><strong>Scegli Llama 3.1 8B<\/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 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>Mistral 7B vs Llama 3.1 8B 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":[395,805,809],"class_list":["post-1274","post","type-post","status-publish","format-standard","hentry","category-ai-comparisons","tag-ai-model-comparison","tag-llama-3-1-8b","tag-mistral-7b"],"_links":{"self":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/1274","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=1274"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/1274\/revisions"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media?parent=1274"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/categories?post=1274"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/tags?post=1274"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}