{"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\/fr\/llama-4-scout-vs-llama-4-maverick\/","title":{"rendered":"Llama 4 Scout contre Llama 4 Maverick : sp\u00e9cifications, tarifs et choix (2026)"},"content":{"rendered":"<p><strong>Llama 4 Scout<\/strong> contre <strong>Llama 4 Maverick<\/strong> \u2014 Les deux variantes Llama 4 de Meta compar\u00e9es. Voici ci-dessous une comparaison d\u00e9taill\u00e9e : sp\u00e9cifications techniques, tarifs d\u2019API, fen\u00eatre de contexte, exigences mat\u00e9rielles locales, ainsi qu\u2019une recommandation claire, fond\u00e9e sur les donn\u00e9es, quant au mod\u00e8le \u00e0 privil\u00e9gier.<\/p>\n<div class=\"cmp\">\n  <table class=\"cmp-table\">\n    <thead><tr><th>Sp\u00e9cifications<\/th><th><a href=\"https:\/\/convly.ai\/fr\/model\/llama-4-scout\/\">Llama 4 Scout<\/a><\/th><th><a href=\"https:\/\/convly.ai\/fr\/model\/llama-4-maverick\/\">Llama 4 Maverick<\/a><\/th><\/tr><\/thead>\n    <tbody>\n          <tr><td class=\"cmp-spec\">D\u00e9veloppeur<\/td><td class=\"\">Meta<\/td><td class=\"\">Meta<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Type<\/td><td class=\"\">Multimodal (MoE)<\/td><td class=\"\">Multimodal (MoE)<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Param\u00e8tres<\/td><td class=\"\">109 milliards au total \/ 17 milliards actifs (MoE)<\/td><td class=\"\">400 milliards au total \/ 17 milliards actifs (MoE)<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Fen\u00eatre de contexte<\/td><td class=\"cmp-win\">10 M<\/td><td class=\"\">1 million<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Modalit\u00e9<\/td><td class=\"\">Texte, image \u2192 texte<\/td><td class=\"\">Texte, image \u2192 texte<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Licence<\/td><td class=\"\">Llama 4 Community (restreint \u00e0 l\u2019UE)<\/td><td class=\"\">Llama 4 Community (restreint \u00e0 l\u2019UE)<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Poids ouverts<\/td><td class=\"\">\u2705 Oui<\/td><td class=\"\">\u2705 Oui<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Prix d'entr\u00e9e ($\/1 million)<\/td><td class=\"cmp-win\">$0.1<\/td><td class=\"\">$0.15<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Prix de sortie ($\/1 million)<\/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 Go<\/td><td class=\"\">~240 Go<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">GPU minimal requis (en local)<\/td><td class=\"\">H100 80 Go \/ Mac 128 Go<\/td><td class=\"\">Serveur multi-GPU<\/td><\/tr>\n          <tr><td class=\"cmp-spec\">Date de sortie<\/td><td class=\"\">2025<\/td><td class=\"\">2025<\/td><\/tr>\n        <\/tbody>\n  <\/table>\n\n    <div class=\"cmp-verdict\">\n    <h3>Principales diff\u00e9rences<\/h3>\n    <ul><li><strong>Co\u00fbt :<\/strong> Llama 4 Scout est <strong>75 % moins cher<\/strong> que Llama 4 Maverick sur une base de co\u00fbt moyen par jeton.<\/li><li><strong>Contexte :<\/strong> Llama 4 Scout offre une fen\u00eatre de contexte sup\u00e9rieure (10 M contre 1 M), ce qui le rend mieux adapt\u00e9 aux documents longs, aux grands bases de code et aux entr\u00e9es volumineuses RAG.<\/li><li><strong>Ouverture :<\/strong> les deux mod\u00e8les ont des poids ouverts, ce qui signifie qu\u2019ils peuvent tous deux \u00eatre auto-h\u00e9berg\u00e9s ou affin\u00e9s. Comparez leurs besoins en VRAM ci-dessus pour d\u00e9terminer quel mod\u00e8le votre GPU peut ex\u00e9cuter.<\/li><li><strong>Ex\u00e9cutez Llama 4 Scout localement :<\/strong> ~65 Go en 4 bits (GPU minimal requis : H100 80 Go \/ Mac 128 Go).<\/li><li><strong>Ex\u00e9cutez Llama 4 Maverick localement :<\/strong> ~~240 Go en quantification 4 bits (serveur multi-GPU minimal).<\/li><\/ul>\n  <\/div>\n\n    <div class=\"cmp-rec\">\n    <h3>Lequel choisir ?<\/h3>\n    <p><strong>Choisir Llama 4 Scout<\/strong> si vous recherchez un co\u00fbt inf\u00e9rieur par jeton pour des charges de travail \u00e0 fort volume, ou si vous avez besoin d\u2019une fen\u00eatre de contexte plus grande.<\/p>\n    <p><strong>Choisissez Llama 4 Maverick<\/strong> si celui-ci s\u2019int\u00e8gre bien \u00e0 votre pile technologique existante ou si vous pr\u00e9f\u00e9rez Meta.<\/p>\n    <p class=\"cmp-tools\">\u2192 Estimez vos co\u00fbts r\u00e9els avec le <a href=\"\/fr\/ai-api-cost-calculator\/\">Calculateur de co\u00fbt d\u2019API<\/a> \u00b7 v\u00e9rifiez la compatibilit\u00e9 de votre mat\u00e9riel local avec le <a href=\"\/fr\/llm-vram-calculator\/\">Calculateur de VRAM<\/a> \u00b7 parcourez l'ensemble des <a href=\"\/fr\/models\/\">30+ mod\u00e8les<\/a>.<\/p>\n  <\/div>\n<\/div>\n\n<p>Toutes les sp\u00e9cifications et les prix sont r\u00e9cup\u00e9r\u00e9s en temps r\u00e9el depuis notre <a href=\"\/fr\/models\/\">Base de donn\u00e9es de mod\u00e8les d'IA<\/a> et r\u00e9guli\u00e8rement mis \u00e0 jour. Comparez l'un ou l'autre de ces mod\u00e8les avec d'autres, ou estimez votre d\u00e9pense mensuelle gr\u00e2ce aux calculateurs gratuits ci-dessus.<\/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\/fr\/wp-json\/wp\/v2\/posts\/1277","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/comments?post=1277"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1277\/revisions"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=1277"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=1277"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=1277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}