{"id":1280,"date":"2026-06-23T15:00:30","date_gmt":"2026-06-23T15:00:30","guid":{"rendered":"https:\/\/convly.ai\/?p=1280"},"modified":"2026-06-23T15:00:30","modified_gmt":"2026-06-23T15:00:30","slug":"open-vs-closed-ai-cost-gap-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/open-vs-closed-ai-cost-gap-2026\/","title":{"rendered":"Open vs Closed AI in 2026: The Real Cost Gap (We Priced 29 Models)"},"content":{"rendered":"<p>Is open-weight AI actually cheaper than the big proprietary APIs \u2014 and by how much? We took the API pricing for all 29 priced models in our <a href=\"\/fr\/models\/\">models database<\/a>, normalized each to a single blended cost per million tokens, and split them into open-weight versus proprietary. The gap is bigger \u2014 and far more consistent \u2014 than most people assume.<\/p>\n<div class=\"convly-tldr\">\n<h3>Points cl\u00e9s<\/h3>\n<ul>\n<li><strong>The 5 cheapest models in 2026 are all open-weight. The 5 most expensive are all proprietary.<\/strong><\/li>\n<li>Le <strong>typical (median) open model costs ~$0.15<\/strong> per 1M blended tokens; the typical proprietary model costs <strong>~$6.00 \u2014 a 39\u00d7 gap.<\/strong><\/li>\n<li>On average, proprietary models cost <strong>~16\u00d7 more<\/strong> than open ones.<\/li>\n<li>Across all 29 models, the full price spread is <strong>~890\u00d7<\/strong> \u2014 from ~$0.02 to $20 per 1M blended tokens.<\/li>\n<li>And that ignores self-hosting, which removes per-token cost <em>entirely<\/em> for open weights.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a3ade031ba5f\" 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-6a3ade031ba5f\"  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\/open-vs-closed-ai-cost-gap-2026\/#How_we_measured_it\" >Comment nous l\u2019avons mesur\u00e9<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/fr\/open-vs-closed-ai-cost-gap-2026\/#The_gap_in_one_table\" >The gap, in one table<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/fr\/open-vs-closed-ai-cost-gap-2026\/#The_extremes_tell_the_story\" >The extremes tell the story<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/fr\/open-vs-closed-ai-cost-gap-2026\/#Important_nuance_this_is_cost_not_capability\" >Important nuance: this is cost, not capability<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/fr\/open-vs-closed-ai-cost-gap-2026\/#Why_the_gap_is_structural\" >Why the gap is structural<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/fr\/open-vs-closed-ai-cost-gap-2026\/#Bottom_line\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"How_we_measured_it\"><\/span>Comment nous l\u2019avons mesur\u00e9<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Scope<\/strong> \u2014 all 29 models in the Convly database with public API pricing.<\/li>\n<li><strong>Co\u00fbt pond\u00e9r\u00e9<\/strong> \u2014 <code>(3 \u00d7 input + output) \/ 4<\/code>, a 3:1 input-to-output ratio typical of real API traffic, so models with cheap input but pricey output are directly comparable.<\/li>\n<li><strong>Classification<\/strong> \u2014 &#8220;open-weight&#8221; = downloadable weights you can self-host (22 models); &#8220;proprietary&#8221; = API-only (7 models).<\/li>\n<li><strong>Sources<\/strong> \u2014 published API pricing via OpenRouter and DeepInfra, June 2026.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"The_gap_in_one_table\"><\/span>The gap, in one table<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Metric (blended $\/1M)<\/th>\n<th>Open-weight (22)<\/th>\n<th>Proprietary (7)<\/th>\n<th>Gap<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Average<\/strong><\/td>\n<td>$0.50<\/td>\n<td>$8.16<\/td>\n<td><strong>16\u00d7<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>Median (typical model)<\/strong><\/td>\n<td>$0.15<\/td>\n<td>$6.00<\/td>\n<td><strong>39\u00d7<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Cheapest in group<\/td>\n<td>$0.02 (Llama 3.1 8B)<\/td>\n<td>$2.00 (Claude Haiku 4.5)<\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>Most expensive in group<\/td>\n<td>$3.00 (Mistral Large 3)<\/td>\n<td>$20.00 (Claude Fable 5)<\/td>\n<td>\u2014<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"The_extremes_tell_the_story\"><\/span>The extremes tell the story<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Sort all 29 models by blended cost and the pattern is stark \u2014 open weights own the bottom, proprietary owns the top:<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>5 cheapest (all open-weight)<\/th>\n<th>Co\u00fbt combin\u00e9 ($\/1 M)<\/th>\n<th>5 most expensive (all proprietary)<\/th>\n<th>Co\u00fbt combin\u00e9 ($\/1 M)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Llama 3.1 8B<\/td>\n<td>$0.02<\/td>\n<td>Claude Fable 5<\/td>\n<td>$20.00<\/td>\n<\/tr>\n<tr>\n<td>Mistral 7B<\/td>\n<td>$0.02<\/td>\n<td>GPT-5.5<\/td>\n<td>$11.25<\/td>\n<\/tr>\n<tr>\n<td>Mistral NeMo 12B<\/td>\n<td>$0.03<\/td>\n<td>Claude Opus 4.8<\/td>\n<td>$10.00<\/td>\n<\/tr>\n<tr>\n<td>Gemma 3 4B<\/td>\n<td>$0.06<\/td>\n<td>Claude Sonnet 4.6<\/td>\n<td>$6.00<\/td>\n<\/tr>\n<tr>\n<td>Qwen3 8B<\/td>\n<td>$0.07<\/td>\n<td>Gemini 3.1 Pro<\/td>\n<td>$4.50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>There is no proprietary model in the cheapest third of the market, and no open-weight model in the most expensive third. The lone overlap zone is narrow: the cheapest proprietary model (Claude Haiku 4.5, $2.00) sits just below the most expensive open one (Mistral Large 3, $3.00).<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Important_nuance_this_is_cost_not_capability\"><\/span>Important nuance: this is cost, not capability<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The priciest models still lead on the hardest reasoning and agentic tasks. In our companion <a href=\"\/fr\/ai-price-performance-index-2026\/\">AI Price-Performance Index<\/a> we found the frontier premium buys the <em>last points<\/em> of intelligence, not proportional value. But for the majority of production workloads \u2014 classification, extraction, RAG, summarization, chat \u2014 the capability gap between a good open model and a frontier model is far smaller than the 39\u00d7 price gap. You are often paying 39\u00d7 for the last 10\u201320% of capability you may not need.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Why_the_gap_is_structural\"><\/span>Why the gap is structural<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This isn&#8217;t a temporary discount war. Intense open-weight competition \u2014 Qwen, Llama, Gemma, DeepSeek and Mistral all shipping strong models under permissive licenses \u2014 has driven the price floor toward zero. Meanwhile frontier labs price for peak capability and enterprise willingness-to-pay. The result is a market that is bifurcating: a race-to-zero floor and a premium ceiling, with a widening canyon between them.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>For cost-sensitive production, an open or mid-tier model is the rational default in 2026 \u2014 and self-hosting removes per-token cost entirely (check what your GPU can run with our <a href=\"\/fr\/llm-vram-calculator\/\">calculateur de VRAM<\/a>). Reserve the proprietary frontier for the genuinely hardest tasks. Run your own usage through the <a href=\"\/fr\/ai-api-cost-calculator\/\">API cost calculator<\/a> to see your exact numbers.<\/p>\n<p><em>Data: Convly AI models database (API pricing via OpenRouter and DeepInfra). Blended cost uses a 3:1 input:output ratio. Figures current as of June 2026.<\/em><\/p>","protected":false},"excerpt":{"rendered":"<p>We priced all 29 models in our database and split them open vs proprietary. The 5 cheapest are all open-weight; the 5 most expensive all proprietary. The typical gap: 39\u00d7.<\/p>","protected":false},"author":1,"featured_media":1281,"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":[813,421,454,745,423,812],"class_list":["post-1280","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-benchmarks","tag-cost-analysis","tag-deepseek","tag-llama","tag-llm-pricing","tag-open-source-ai","tag-open-vs-closed"],"_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1280","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=1280"}],"version-history":[{"count":1,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1280\/revisions"}],"predecessor-version":[{"id":1282,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1280\/revisions\/1282"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/1281"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=1280"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=1280"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=1280"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}