{"id":1246,"date":"2026-06-22T17:39:20","date_gmt":"2026-06-22T17:39:20","guid":{"rendered":"https:\/\/convly.ai\/ai-price-performance-index-2026\/"},"modified":"2026-08-01T06:46:23","modified_gmt":"2026-08-01T06:46:23","slug":"ai-price-performance-index-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/ai-price-performance-index-2026\/","title":{"rendered":"Indice 2026 du rapport prix-performance en IA\u00a0: quel mod\u00e8le offre le plus d'intelligence pour un dollar d\u00e9pens\u00e9\u00a0?"},"content":{"rendered":"<p><!--ppi-hero--><\/p>\n<style>\n.ppi-hero{background:linear-gradient(135deg,#0b1220,#16213e);color:#eef2ff;border-radius:14px;padding:28px 26px;margin:0 0 26px}\n.ppi-eyebrow{font-size:12px;letter-spacing:.14em;text-transform:uppercase;color:#8ea2ff!important;margin:0 0 8px;font-weight:700}\n.ppi-headline{font-size:30px;line-height:1.2;margin:0 0 10px;color:#fff!important;font-weight:800}\n.ppi-sub{font-size:16px;line-height:1.55;color:#c7d2fe!important;margin:0 0 20px;max-width:70ch}\n.ppi-stats{display:flex;flex-wrap:wrap;gap:14px;margin:0}\n.ppi-stat{flex:1 1 170px;background:rgba(255,255,255,.06);border:1px solid rgba(255,255,255,.12);border-radius:10px;padding:14px 16px}\n.ppi-num{display:block;font-size:34px;font-weight:800;color:#7bffb0;line-height:1}\n.ppi-stat.b .ppi-num{color:#ffd08a}.ppi-stat.c .ppi-num{color:#8ec5ff}\n.ppi-lab{display:block;font-size:13px;color:#c7d2fe!important;margin-top:6px;line-height:1.35}\n.ppi-chart{overflow-x:auto;margin:24px 0;background:#f8f9fb;border:1px solid #e6e8ef;border-radius:12px;padding:16px}\n.ppi-chart figcaption{font-size:12px;color:#667788;margin-top:8px;text-align:center}\n.ppi-box{border:1px solid #e6e8ef;border-left:4px solid #4263eb;background:#f8f9fb;border-radius:10px;padding:18px 20px;margin:22px 0}\n.ppi-box h2{margin:0 0 10px;font-size:20px}\n.ppi-box blockquote{margin:10px 0;padding:10px 14px;background:#fff;border:1px solid #e6e8ef;border-radius:8px;font-size:14px;color:#334455}\n@media (max-width:600px){.ppi-headline{font-size:23px}.ppi-num{font-size:28px}}\n<\/style>\n<div class=\"ppi-hero\">\n<p class=\"ppi-eyebrow\">Convly Original Study \u00b7 Updated 2026<\/p>\n<div class=\"ppi-headline\">The Price of Intelligence: a 114\u00d7 cost spread across AI models<\/div>\n<p class=\"ppi-sub\">We cross-referenced live API pricing for 30 leading AI models with the independent Artificial Analysis Intelligence Index to answer one question \u2014 which model gives you the most intelligence per dollar? The answer overturns &#8220;you get what you pay for.&#8221;<\/p>\n<div class=\"ppi-stats\">\n<div class=\"ppi-stat a\"><span class=\"ppi-num\">114\u00d7<\/span><span class=\"ppi-lab\">blended-cost spread \u2014 $0.18 to $20 per 1M tokens<\/span><\/div>\n<div class=\"ppi-stat b\"><span class=\"ppi-num\">37\u00d7<\/span><span class=\"ppi-lab\">more intelligence per dollar: DeepSeek V4-Flash vs Claude Opus 4.8<\/span><\/div>\n<div class=\"ppi-stat c\"><span class=\"ppi-num\">~1.8%<\/span><span class=\"ppi-lab\">of Opus&#8217;s cost buys ~67% of its measured intelligence<\/span><\/div>\n<\/p><\/div>\n<\/div>\n<figure class=\"ppi-chart\">\n<svg viewbox=\"0 0 760 360\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" role=\"img\" aria-label=\"Blended API cost per 1M tokens across nine AI models, ranging from $0.18 to $20 \u2014 a 114x spread\" style=\"width:100%;height:auto;min-width:520px\">\n <text x=\"10\" y=\"22\" font-family=\"system-ui,Arial\" font-size=\"16\" font-weight=\"700\" fill=\"#1a1a2e\">Blended API cost per 1M tokens \u2014 a 114\u00d7 spread (2026)<\/text>\n <g font-family=\"system-ui,Arial\" font-size=\"13\" fill=\"#333\">\n <g><text x=\"160\" y=\"63\" text-anchor=\"end\">DeepSeek V4-Flash<\/text><rect x=\"170\" y=\"50\" width=\"6\" height=\"20\" rx=\"3\" fill=\"#2f9e44\"\/><text x=\"184\" y=\"64\" font-size=\"12\" font-weight=\"700\" fill=\"#2f9e44\">$0.18<\/text><\/g>\n <g><text x=\"160\" y=\"95\" text-anchor=\"end\">DeepSeek V4-Pro<\/text><rect x=\"170\" y=\"82\" width=\"15\" height=\"20\" rx=\"3\" fill=\"#4263eb\"\/><text x=\"191\" y=\"96\" font-size=\"12\" fill=\"#555\">$0.54<\/text><\/g>\n <g><text x=\"160\" y=\"127\" text-anchor=\"end\">Claude Haiku 4.5<\/text><rect x=\"170\" y=\"114\" width=\"56\" height=\"20\" rx=\"3\" fill=\"#4263eb\"\/><text x=\"232\" y=\"128\" font-size=\"12\" fill=\"#555\">$2.00<\/text><\/g>\n <g><text x=\"160\" y=\"159\" text-anchor=\"end\">Gemini 3.5 Flash<\/text><rect x=\"170\" y=\"146\" width=\"95\" height=\"20\" rx=\"3\" fill=\"#4263eb\"\/><text x=\"271\" y=\"160\" font-size=\"12\" fill=\"#555\">$3.38<\/text><\/g>\n <g><text x=\"160\" y=\"191\" text-anchor=\"end\">Gemini 3.1 Pro<\/text><rect x=\"170\" y=\"178\" width=\"126\" height=\"20\" rx=\"3\" fill=\"#4263eb\"\/><text x=\"302\" y=\"192\" font-size=\"12\" fill=\"#555\">$4.50<\/text><\/g>\n <g><text x=\"160\" y=\"223\" text-anchor=\"end\">Claude Sonnet 4.6<\/text><rect x=\"170\" y=\"210\" width=\"168\" height=\"20\" rx=\"3\" fill=\"#4263eb\"\/><text x=\"344\" y=\"224\" font-size=\"12\" fill=\"#555\">$6.00<\/text><\/g>\n <g><text x=\"160\" y=\"255\" text-anchor=\"end\">Claude Opus 4.8<\/text><rect x=\"170\" y=\"242\" width=\"280\" height=\"20\" rx=\"3\" fill=\"#4263eb\"\/><text x=\"456\" y=\"256\" font-size=\"12\" fill=\"#555\">$10.00<\/text><\/g>\n <g><text x=\"160\" y=\"287\" text-anchor=\"end\">GPT-5.5<\/text><rect x=\"170\" y=\"274\" width=\"315\" height=\"20\" rx=\"3\" fill=\"#4263eb\"\/><text x=\"491\" y=\"288\" font-size=\"12\" fill=\"#555\">$11.25<\/text><\/g>\n <g><text x=\"160\" y=\"319\" text-anchor=\"end\">Claude Fable 5<\/text><rect x=\"170\" y=\"306\" width=\"560\" height=\"20\" rx=\"3\" fill=\"#e8590c\"\/><text x=\"700\" y=\"320\" font-size=\"12\" font-weight=\"700\" fill=\"#fff\" text-anchor=\"end\">$20.00<\/text><\/g>\n <\/g>\n<\/svg><figcaption>Blended cost = (3 \u00d7 input + output) \u00f7 4, a 3:1 input-to-output ratio typical of real API traffic. Source: Convly AI models database. Green = best value, orange = most expensive.<\/figcaption><\/figure>\n<p>La tarification des mod\u00e8les IA en 2026 couvre une fourchette \u00e9tonnamment large \u2014 or le mod\u00e8le le plus co\u00fbteux n\u2019est nullement 100 fois plus intelligent que le moins cher. Nous avons combin\u00e9 les prix en direct figurant dans notre <a href=\"\/fr\/models\/\">Base de donn\u00e9es des mod\u00e8les d'IA<\/a> et la r\u00e9f\u00e9rence ind\u00e9pendante <strong>Indice d\u2019intelligence artificielle d\u2019Artificial Analysis<\/strong> pour r\u00e9pondre \u00e0 une seule question : <strong>quel mod\u00e8le vous offre le plus d\u2019intelligence pour chaque dollar d\u00e9pens\u00e9 ?<\/strong> La r\u00e9ponse remet en cause l\u2019hypoth\u00e8se selon laquelle on obtient ce pour quoi on paie.<\/p>\n<div class=\"convly-tldr\">\n<h3>Points cl\u00e9s<\/h3>\n<ul>\n<li><strong>\u00c9cart de prix de 114\u00d7.<\/strong> Sur une base de co\u00fbt pond\u00e9r\u00e9, les mod\u00e8les API de pointe vont d\u2019environ 0,18 $ \u00e0 environ 20 $ par million de jetons.<\/li>\n<li><strong>Valeur \u2260 prix.<\/strong> Les mod\u00e8les capables les moins chers offrent <strong>30 \u00e0 40 fois plus d\u2019intelligence par dollar<\/strong> que les mod\u00e8les de pointe les plus co\u00fbteux.<\/li>\n<li><strong>DeepSeek V4-Flash<\/strong> offre environ <strong>67 % de l\u2019intelligence mesur\u00e9e de Claude Opus 4.8 pour environ 1,8 % de son co\u00fbt.<\/strong><\/li>\n<li>Payer le prix fort permet d\u2019obtenir <em>les derniers points<\/em> de capacit\u00e9 \u2014 pas une intelligence proportionnellement sup\u00e9rieure.<\/li>\n<li>Pour la plupart des charges de travail, un mod\u00e8le interm\u00e9diaire ou open source constitue le choix rationnel par d\u00e9faut ; r\u00e9servez les mod\u00e8les de pointe aux t\u00e2ches les plus complexes.<\/li>\n<\/ul>\n<\/div>\n<div class=\"convly-tldr ppi-k3-update\">\n<h3>Update \u2014 July 2026: an open model now tops the table<\/h3>\n<p>Since this study went live, Moonshot released <a href=\"\/fr\/kimi-k3-explained-2026\/\">Kimi K3<\/a> (16 July 2026) \u2014 a 2.8-trillion-parameter open-weight mixture-of-experts scoring <strong>57<\/strong> on the Artificial Analysis Intelligence Index, edging past Claude Opus 4.8 (55.7). It is the first open-weight model to outscore a Western frontier flagship, and at $3\/$15 per 1M tokens it returns roughly <strong>1.7\u00d7 the intelligence per dollar of Opus 4.8<\/strong>.<\/p>\n<p>It does not move the headline spread below, though: K3 lands mid-table on value at 6.3 intelligence points per blended dollar. <a href=\"\/fr\/glm-5-2-explained-2026\/\">GLM 5.2<\/a> still returns about 2.8\u00d7 more capability per dollar, and DeepSeek V4-Flash roughly 30\u00d7 more. The ultra-cheap-Chinese-model era looks to be ending too \u2014 K3 costs about 3\u00d7 its own predecessor. Full analysis: <a href=\"\/fr\/kimi-k3-explained-2026\/\">Kimi K3 explained<\/a> \u00b7 <a href=\"\/fr\/kimi-k3-vs-claude-opus-4-8\/\">K3 vs Opus 4.8<\/a>.<\/p>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a7557edcd2d0\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Basculer<\/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-6a7557edcd2d0\"  aria-label=\"Basculer\" \/><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\/fr\/ai-price-performance-index-2026\/#How_we_measured_it\" >Comment nous avons proc\u00e9d\u00e9 aux mesures<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/fr\/ai-price-performance-index-2026\/#The_2026_blended-cost_index\" >Indice 2026 du co\u00fbt pond\u00e9r\u00e9<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/fr\/ai-price-performance-index-2026\/#Intelligence_per_dollar_%E2%80%94_the_value_ranking\" >Intelligence par dollar \u2014 classement de la valeur<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/fr\/ai-price-performance-index-2026\/#What_this_means_for_you\" >Ce que cela signifie pour vous<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/fr\/ai-price-performance-index-2026\/#Caveats\" >Mises en garde<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/fr\/ai-price-performance-index-2026\/#Bottom_line\" >Conclusion<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/fr\/ai-price-performance-index-2026\/#Download_the_data_%E2%80%94_free_open_CC-BY-40\" >Download the data \u2014 free &amp; open (CC-BY-4.0)<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/fr\/ai-price-performance-index-2026\/#Cite_or_republish_this_study\" >Cite or republish this study<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"How_we_measured_it\"><\/span>Comment nous avons proc\u00e9d\u00e9 aux mesures<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Tarification<\/strong> \u2014 live input\/output API prices (per 1M tokens) from the Convly <a href=\"https:\/\/convly.ai\/fr\/models\/\">Base de donn\u00e9es des mod\u00e8les d'IA<\/a>.<\/li>\n<li><strong>Co\u00fbt combin\u00e9<\/strong> \u2014 ratio jetons d\u2019entr\u00e9e\/jetons de sortie de 3:1 (typique du trafic API r\u00e9el) : <code>co\u00fbt pond\u00e9r\u00e9 = (3 \u00d7 entr\u00e9e + sortie) \u00f7 4<\/code>. Cela permet de comparer directement des mod\u00e8les dont l\u2019entr\u00e9e est peu co\u00fbteuse mais la sortie on\u00e9reuse.<\/li>\n<li><strong>Performances<\/strong> \u2014 l\u2019Indice d\u2019intelligence Artificielle Analysis (indice composite couvrant le raisonnement, la programmation, les math\u00e9matiques et les connaissances), utilis\u00e9 <em>seulement<\/em> lorsqu\u2019un score actuel est publi\u00e9 pour le mod\u00e8le exact.<\/li>\n<li><strong>Valeur<\/strong> \u2014 Indice d\u2019intelligence \u00f7 co\u00fbt combin\u00e9.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"The_2026_blended-cost_index\"><\/span>Indice 2026 du co\u00fbt pond\u00e9r\u00e9<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Mod\u00e8le<\/th>\n<th>Entr\u00e9e : $\/1 million<\/th>\n<th>Sortie : $\/1 million<\/th>\n<th>Co\u00fbt combin\u00e9 par million de dollars<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DeepSeek V4-Flash<\/td>\n<td>$0.14<\/td>\n<td>$0.28<\/td>\n<td><strong>$0.18<\/strong><\/td>\n<\/tr>\n<tr>\n<td>DeepSeek V4-Pro<\/td>\n<td>$0.435<\/td>\n<td>$0.87<\/td>\n<td><strong>$0.54<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Claude Haiku 4.5<\/td>\n<td>$1.00<\/td>\n<td>$5.00<\/td>\n<td><strong>$2.00<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Gemini 3.5 Flash<\/td>\n<td>$1.50<\/td>\n<td>$9.00<\/td>\n<td><strong>$3.38<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Gemini 3.1 Pro<\/td>\n<td>$2.00<\/td>\n<td>$12.00<\/td>\n<td><strong>$4.50<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Claude Sonnet 4.6<\/td>\n<td>$3.00<\/td>\n<td>$15.00<\/td>\n<td><strong>$6.00<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Claude Opus 4.8<\/td>\n<td>$5.00<\/td>\n<td>$25.00<\/td>\n<td><strong>$10.00<\/strong><\/td>\n<\/tr>\n<tr>\n<td>GPT-5.5<\/td>\n<td>$5.00<\/td>\n<td>$30.00<\/td>\n<td><strong>$11.25<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Claude Fable 5<\/td>\n<td>$10.00<\/td>\n<td>$50.00<\/td>\n<td><strong>$20.00<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>L\u2019\u00e9cart est ce qui retient l\u2019attention : <strong>Claude Fable 5 costs 114\u00d7 more per blended token than DeepSeek V4-Flash.<\/strong> Souhaitez-vous obtenir vos propres chiffres ? Essayez notre <a href=\"\/fr\/ai-api-cost-calculator\/\">Calculateur de co\u00fbts des API IA<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Intelligence_per_dollar_%E2%80%94_the_value_ranking\"><\/span>Intelligence par dollar \u2014 classement de la valeur<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Nous divisons d\u00e9sormais la capacit\u00e9 par le co\u00fbt. En utilisant l\u2019Indice d\u2019intelligence Artificielle Analysis (plus \u00e9lev\u00e9 = plus performant) compar\u00e9 au co\u00fbt combin\u00e9, pour les mod\u00e8les disposant d\u2019un score actuel publi\u00e9 :<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Mod\u00e8le<\/th>\n<th>Indice d\u2019intelligence<\/th>\n<th>Co\u00fbt combin\u00e9 par million de dollars<\/th>\n<th>Intelligence par dollar<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DeepSeek V4-Flash<\/td>\n<td>37.4<\/td>\n<td>$0.18<\/td>\n<td><strong>\u2248 208<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Claude Opus 4.8<\/td>\n<td>55.7<\/td>\n<td>$10.00<\/td>\n<td><strong>\u2248 5,6<\/strong><\/td>\n<\/tr>\n<tr>\n<td>GPT-5.5<\/td>\n<td>54.8<\/td>\n<td>$11.25<\/td>\n<td><strong>\u2248 4,9<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Le constat est sans appel : <strong>DeepSeek V4-Flash fournit environ 37 fois plus d\u2019intelligence par dollar que Claude Opus 4.8<\/strong> \u2014 tout en obtenant environ les deux tiers de l\u2019intelligence brute d\u2019Opus. La prime \u00ab fronti\u00e8re \u00bb est bien r\u00e9elle, mais en termes de <em>capacit\u00e9<\/em> elle est faible ; en termes de <em>prix<\/em> elle est \u00e9norme.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_this_means_for_you\"><\/span>Ce que cela signifie pour vous<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>La prime \u00ab fronti\u00e8re \u00bb ach\u00e8te les derniers points, pas une intelligence proportionnelle.<\/strong> Vous payez 30 \u00e0 40 fois plus cher pour environ le tiers sup\u00e9rieur des capacit\u00e9s.<\/li>\n<li><strong>Pour les applications de production \u00e0 fort volume<\/strong> \u2014 chat, classification, extraction, RAG \u2014 un mod\u00e8le \u00e9conomique ou open source constitue le choix rationnel par d\u00e9faut.<\/li>\n<li><strong>R\u00e9servez les mod\u00e8les de pointe<\/strong> (Opus 4.8, GPT-5.5, Fable 5) aux t\u00e2ches les plus complexes de raisonnement et aux travaux agents \u00e0 long terme, o\u00f9 ces derniers points comptent v\u00e9ritablement.<\/li>\n<li><strong>L\u2019h\u00e9bergement local des poids ouverts transforme enti\u00e8rement les calculs<\/strong> \u2014 votre co\u00fbt se limite alors au mat\u00e9riel et \u00e0 l\u2019\u00e9lectricit\u00e9, et non plus au co\u00fbt par jeton. Consultez notre <a href=\"\/fr\/llm-vram-calculator\/\">Calculateur de VRAM<\/a> pour v\u00e9rifier ce que vous pouvez ex\u00e9cuter localement.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Caveats\"><\/span>Mises en garde<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>L\u2019Indice d\u2019intelligence est un indicateur composite approximatif \u2014 votre t\u00e2che peut accorder un poids diff\u00e9rent \u00e0 la programmation, au contexte long ou \u00e0 la multimodalit\u00e9, et les mod\u00e8les de pointe prennent davantage d\u2019avance sur les t\u00e2ches agents les plus complexes. Les prix \u00e9voluent \u00e9galement, et la mise en cache ou les remises par lot peuvent r\u00e9duire vos factures r\u00e9elles de 50 \u00e0 90 %. Consid\u00e9rez cet indice comme une carte indicative de valeur, non comme un jugement d\u00e9finitif pour chaque charge de travail.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>En 2026, l\u2019adage \u00ab on obtient ce pour quoi on paie \u00bb est tout simplement faux pour les API IA. Les mod\u00e8les \u00e9conomiques capables fournissent la grande majorit\u00e9 de l\u2019intelligence de pointe \u00e0 une fraction minime du co\u00fbt. Adaptez le mod\u00e8le \u00e0 la t\u00e2che \u2014 et calculez vos propres besoins avec notre <a href=\"\/fr\/ai-api-cost-calculator\/\">calculateur de co\u00fbts<\/a> avant de vous engager.<\/p>\n<p><em>Sources : Indice d\u2019intelligence Artificielle Analysis (scores d\u2019intelligence) ; base de donn\u00e9es des mod\u00e8les Convly AI (tarifs). Donn\u00e9es actualis\u00e9es en juin 2026.<\/em><\/p>\n<div class=\"ppi-box\">\n<h2><span class=\"ez-toc-section\" id=\"Download_the_data_%E2%80%94_free_open_CC-BY-40\"><\/span>Download the data \u2014 free &amp; open (CC-BY-4.0)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The full 30-model dataset (prices, specs, intelligence scores and value ratios) is open for anyone to analyse or republish with attribution:<\/p>\n<ul>\n<li><a href=\"https:\/\/github.com\/Sakd99\/ai-models-database\" rel=\"noopener\" target=\"_blank\">CSV + JSON on GitHub<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/datasets\/sakd99\/ai-models-database\" rel=\"noopener\" target=\"_blank\">Dataset on Hugging Face<\/a><\/li>\n<\/ul>\n<\/div>\n<div class=\"ppi-box\">\n<h2><span class=\"ez-toc-section\" id=\"Cite_or_republish_this_study\"><\/span>Cite or republish this study<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Free to cite, quote or embed with a link back. Suggested credit:<\/p>\n<blockquote><p>&#8220;The AI Price\/Performance Index 2026,&#8221; Convly \u2014 https:\/\/convly.ai\/ai-price-performance-index-2026\/<\/p><\/blockquote>\n<p><strong>Headline finding for reference:<\/strong> a 114\u00d7 blended-cost spread separates the cheapest capable AI model ($0.18\/1M tokens) from the most expensive ($20\/1M), yet the best-value model returns roughly <strong>37\u00d7 the intelligence-per-dollar<\/strong> of the priciest frontier model while still scoring about two-thirds of its raw intelligence. Journalists and researchers: the underlying data is downloadable above, and we&#8217;re happy to share the full methodology or a high-resolution chart on request.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>We combined live API pricing with the Artificial Analysis Intelligence Index to rank AI models by intelligence per dollar. The price spread is 114\u00d7 \u2014 but value tells a very different story.<\/p>","protected":false},"author":1,"featured_media":1806,"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":[787,790,421,654,788,789],"class_list":["post-1246","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-benchmarks","tag-ai-pricing","tag-claude","tag-deepseek","tag-gpt-5-5","tag-llm-cost","tag-price-performance"],"_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1246","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=1246"}],"version-history":[{"count":5,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1246\/revisions"}],"predecessor-version":[{"id":1592,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1246\/revisions\/1592"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/1806"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=1246"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=1246"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=1246"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}