{"id":756,"date":"2026-05-30T15:32:04","date_gmt":"2026-05-30T15:32:04","guid":{"rendered":"https:\/\/convly.ai\/zhipu-glm-explained-2026\/"},"modified":"2026-05-31T14:46:33","modified_gmt":"2026-05-31T14:46:33","slug":"zhipu-glm-explained-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/","title":{"rendered":"Zhipu GLM-5.1 en 2026 : Le mod\u00e8le ouvert entra\u00een\u00e9 sans un seul GPU Nvidia"},"content":{"rendered":"<p>Of all the Chinese AI labs, <strong>Zhipu AI<\/strong> \u2014 now operating internationally as <strong>Z.ai<\/strong> \u2014 may be the most strategically significant. Its <strong>GLM-5.1<\/strong> model topped a global coding leaderboard, ships under the permissive MIT license, costs a fraction of Western alternatives, and was trained <strong>entirely on Huawei chips, without a single Nvidia GPU<\/strong>. That last fact makes GLM a statement about the future of AI independence as much as a product. Here&#8217;s the full picture.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principaux enseignements<\/h3>\n<ul>\n<li><strong>GLM-5.1<\/strong> (March 2026, open weights April 7) is a 744B-parameter MoE under the MIT license.<\/li>\n<li><strong>Topped SWE-Bench Pro<\/strong> at 58.4, nudging past GPT-5.4 (57.7) and Claude Opus 4.6 (57.3) \u2014 though some scores are self-reported.<\/li>\n<li><strong>Radically cheap:<\/strong> ~$0.98\/$3.08 per million tokens; a coding plan from $3-$30\/mo vs $100-$200 for Claude Max.<\/li>\n<li><strong>Trained entirely on Huawei Ascend chips<\/strong> \u2014 no Nvidia hardware, a major proof point for AI without US silicon.<\/li>\n<li><strong>Meilleur pour :<\/strong> cost-conscious teams wanting a near-Opus open model they can self-host.<\/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-6a1c765a1deda\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/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-6a1c765a1deda\"  aria-label=\"Toggle\" \/><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\/zhipu-glm-explained-2026\/#Who_is_Zhipu_Zai\" >Who is Zhipu \/ Z.ai<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/#What_GLM-51_actually_is\" >What GLM-5.1 actually is<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/#The_benchmark_%E2%80%94_with_an_honest_asterisk\" >The benchmark \u2014 with an honest asterisk<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/#The_Huawei_angle_%E2%80%94_why_GLM_matters_beyond_benchmarks\" >The Huawei angle \u2014 why GLM matters beyond benchmarks<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/#Where_GLM_wins\" >Where GLM wins<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/#Where_GLM_loses_%E2%80%94_the_honest_caveats\" >Where GLM loses \u2014 the honest caveats<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/#GLM_vs_the_field\" >GLM vs the field<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/#Pros_and_cons\" >Pros and cons<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/#How_to_access_GLM\" >How to access GLM<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/convly.ai\/fr\/zhipu-glm-explained-2026\/#Bottom_line\" >R\u00e9sultat<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Who_is_Zhipu_Zai\"><\/span>Who is Zhipu \/ Z.ai<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Zhipu AI is a Beijing lab spun out of <strong>Tsinghua University<\/strong>, one of China&#8217;s most prestigious institutions. It&#8217;s among the original &#8220;AI tiger&#8221; startups and has positioned itself as the enterprise- and developer-focused alternative to consumer plays like Doubao. In 2026 it rebranded internationally as <strong>Z.ai<\/strong>, signaling a push for global developers.<\/p>\n<p>Its model family is <strong>GLM<\/strong> (General Language Model). Where DeepSeek competes on price and Kimi on agentic coding, Zhipu&#8217;s pitch is &#8220;<strong>90%+ of Claude&#8217;s quality at a tenth of the cost, fully open, and built on a sovereign hardware stack<\/strong>.&#8221; That last part is the differentiator nobody else can claim as cleanly.<\/p>\n<div class=\"convly-specs\">\n<div><strong>Company<\/strong><span>Zhipu AI \/ Z.ai (Beijing; Tsinghua spinout)<\/span><\/div>\n<div><strong>Latest model<\/strong><span>GLM-5.1 (March 27, 2026; weights April 7)<\/span><\/div>\n<div><strong>Architecture<\/strong><span>~744B MoE (post-training upgrade to GLM-5)<\/span><\/div>\n<div><strong>Context window<\/strong><span>200K tokens, 128K max output<\/span><\/div>\n<div><strong>License<\/strong><span>MIT (fully open weights)<\/span><\/div>\n<div><strong>API pricing<\/strong><span>~$0.98 in \/ $3.08 out per 1M; coding plan $3-$30\/mo<\/span><\/div>\n<div><strong>Trained on<\/strong><span>Huawei Ascend 910B (no Nvidia)<\/span><\/div>\n<div><strong>Best for<\/strong><span>Cost-conscious teams wanting a self-hostable near-Opus model<\/span><\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_GLM-51_actually_is\"><\/span>What GLM-5.1 actually is<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>GLM-5.1, released March 27, 2026 with open weights following on April 7, is a post-training upgrade to the GLM-5 base \u2014 the same ~744-billion-parameter Mixture-of-Experts architecture, with significantly enhanced coding, tool use, and autonomous execution. It supports a 200K context window, 128K max output, thinking mode, function calling, structured output, context caching, and native MCP integration.<\/p>\n<p>Critically, it&#8217;s released under the <strong>MIT license<\/strong> on Hugging Face \u2014 download, modify, fine-tune, and deploy commercially with no restrictions or royalties. Combined with strong capability, that makes GLM-5.1 one of the most genuinely useful open models available.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_benchmark_%E2%80%94_with_an_honest_asterisk\"><\/span>The benchmark \u2014 with an honest asterisk<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>On <strong>SWE-Bench Pro<\/strong>, GLM-5.1 reportedly scored <strong>58.4<\/strong>, topping the global leaderboard and edging past GPT-5.4 (57.7) and Claude Opus 4.6 (57.3). Zhipu also claims a coding score of 45.3 \u2014 about <strong>94.6% of Claude Opus 4.6&#8217;s performance<\/strong>.<\/p>\n<p>Here&#8217;s the honest caveat, and it matters: <strong>some of these headline numbers are self-reported by Z.ai<\/strong>, and as of early 2026 independent labs had not fully corroborated the most flattering coding figures. The model is clearly excellent \u2014 multiple third-party reviews confirm it&#8217;s near-Opus on real work \u2014 but treat the exact &#8220;94.6% of Opus&#8221; claim as a vendor figure, not gospel. The practical takeaway holds: GLM-5.1 delivers most of Claude&#8217;s quality for routine work at a tiny fraction of the price.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Huawei_angle_%E2%80%94_why_GLM_matters_beyond_benchmarks\"><\/span>The Huawei angle \u2014 why GLM matters beyond benchmarks<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The single most significant fact about GLM isn&#8217;t a benchmark \u2014 it&#8217;s the hardware. Zhipu trained the <strong>entire GLM-5 family exclusively on Huawei Ascend 910B accelerators<\/strong>, with <strong>no Nvidia GPUs<\/strong> involved.<\/p>\n<p>This is a landmark. US export controls have tried to choke China&#8217;s access to Nvidia&#8217;s best chips precisely to slow Chinese frontier AI. GLM-5.1 is living proof that a competitive, leaderboard-topping frontier model can be built entirely on domestic Chinese silicon. Whatever you think of the geopolitics, it reshapes the strategic picture: the hardware chokepoint is leakier than assumed.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Where_GLM_wins\"><\/span>Where GLM wins<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>1. Claude quality at 1\/30th the cost<\/h3>\n<p>The GLM Coding Plan starts at <strong>$3-$30\/month<\/strong> versus <strong>$100-$200\/month<\/strong> for Claude Max. If GLM delivers 90%+ of Opus quality for your routine coding \u2014 and for many teams it does \u2014 the savings are transformative.<\/p>\n<h3>2. MIT-licensed open weights<\/h3>\n<p>Like DeepSeek, GLM ships its best model fully open. Self-host, fine-tune, air-gap \u2014 total control, no royalties.<\/p>\n<h3>3. Genuinely strong agentic coding<\/h3>\n<p>GLM-5.1&#8217;s enhancements target tool use and autonomous execution, with native MCP support. It&#8217;s built for the agent era, not just chat.<\/p>\n<h3>4. Hardware sovereignty<\/h3>\n<p>For organizations (especially in China and allied markets) that want to avoid dependence on US silicon and software, GLM is the clearest path \u2014 and that strategic appeal is real regardless of benchmarks.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Where_GLM_loses_%E2%80%94_the_honest_caveats\"><\/span>Where GLM loses \u2014 the honest caveats<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>1. Some benchmarks are self-reported<\/h3>\n<p>The most flattering numbers come from Z.ai itself, without full independent corroboration. The model is excellent, but discount the exact vendor claims and test on your own workload.<\/p>\n<h3>2. Hosted-API caveats<\/h3>\n<p>The Z.ai API carries the usual China data-residency and content-moderation considerations. The MIT weights let you self-host to avoid both.<\/p>\n<h3>3. Smaller context than rivals<\/h3>\n<p>200K context is solid but trails DeepSeek and Qwen&#8217;s 1M windows. For very long-document or whole-codebase work, that&#8217;s a real limitation.<\/p>\n<h3>4. Ecosystem still maturing<\/h3>\n<p>Z.ai&#8217;s international developer experience is newer than Alibaba&#8217;s or the US labs&#8217;. Improving quickly, but not yet at parity.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"GLM_vs_the_field\"><\/span>GLM vs the field<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Dimension<\/th>\n<th>GLM-5.1<\/th>\n<th>DeepSeek V4<\/th>\n<th>Kimi K2.6<\/th>\n<th>Claude Opus 4.8<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Open weights<\/td>\n<td class=\"convly-vs-winner\">Yes (MIT)<\/td>\n<td class=\"convly-vs-winner\">Yes (MIT)<\/td>\n<td class=\"convly-vs-winner\">Oui<\/td>\n<td>Non<\/td>\n<\/tr>\n<tr>\n<td>Coding (SWE-Bench Pro)<\/td>\n<td class=\"convly-vs-winner\">58.4<\/td>\n<td>~58<\/td>\n<td class=\"convly-vs-winner\">58.6<\/td>\n<td>Frontier<\/td>\n<\/tr>\n<tr>\n<td>Prix<\/td>\n<td>~$0.98\/$3.08<\/td>\n<td class=\"convly-vs-winner\">~$0.44\/$0.87<\/td>\n<td>~$0.60\/$2.50<\/td>\n<td>~$5\/$25<\/td>\n<\/tr>\n<tr>\n<td>Context window<\/td>\n<td>200K<\/td>\n<td class=\"convly-vs-winner\">1M<\/td>\n<td>262K<\/td>\n<td class=\"convly-vs-winner\">1M<\/td>\n<\/tr>\n<tr>\n<td>Hardware story<\/td>\n<td class=\"convly-vs-winner\">Huawei-only<\/td>\n<td>Nvidia<\/td>\n<td>Nvidia<\/td>\n<td>Nvidia\/TPU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"Pros_and_cons\"><\/span>Pros and cons<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>GLM pros<\/h4>\n<ul>\n<li>Near-Claude quality at a fraction of the price<\/li>\n<li>MIT-licensed open weights \u2014 self-hostable<\/li>\n<li>Strong agentic coding with native MCP support<\/li>\n<li>Trained entirely on Huawei chips (sovereign stack)<\/li>\n<li>Coding plan from $3-$30\/month<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>GLM cons<\/h4>\n<ul>\n<li>Headline benchmarks partly self-reported<\/li>\n<li>200K context trails 1M rivals<\/li>\n<li>Hosted API has China data\/moderation caveats<\/li>\n<li>International ecosystem still maturing<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"How_to_access_GLM\"><\/span>How to access GLM<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Hosted API \/ coding plan:<\/strong> z.ai (formerly open.bigmodel.cn) \u2014 cheapest direct option, including the $3-$30\/mo GLM Coding Plan.<\/li>\n<li><strong>Western hosts:<\/strong> OpenRouter and others serve GLM-5.1 with non-China data residency.<\/li>\n<li><strong>Self-host:<\/strong> download GLM-5.1 weights from Hugging Face (MIT) and run on your own hardware.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>Is GLM-5.1 really as good as Claude?<\/h3>\n<p>For routine coding and general work, multiple reviews put it at roughly 90-95% of Claude Opus 4.6&#8217;s quality \u2014 at a tiny fraction of the cost. For the most demanding frontier reasoning, Claude Opus 4.8 (newer) still leads. The honest framing: GLM gives you most of Claude&#8217;s value for a fraction of the price, with the caveat that the most flattering benchmark numbers are vendor-reported.<\/p>\n<h3>What does the &#8220;no Nvidia&#8221; training mean for me?<\/h3>\n<p>Practically, nothing about using the model. Strategically, it proves competitive frontier models can be built on non-US hardware \u2014 which matters for anyone thinking about long-term AI supply-chain risk and the effectiveness of chip export controls.<\/p>\n<h3>Is GLM open source?<\/h3>\n<p>Yes \u2014 GLM-5.1 weights are on Hugging Face under the MIT license, one of the most permissive available. You can use it commercially with no restrictions.<\/p>\n<h3>Who is Z.ai?<\/h3>\n<p>Z.ai is the international brand of Zhipu AI, a Beijing lab spun out of Tsinghua University. The rebrand in 2026 reflects a push to serve global developers.<\/p>\n<h3>How does the GLM Coding Plan compare to Claude Max?<\/h3>\n<p>GLM&#8217;s coding plan runs $3-$30\/month; Claude Max is $100-$200\/month. If GLM covers your routine work at acceptable quality \u2014 and for many developers it does \u2014 that&#8217;s a 5-30x cost reduction. Many teams now use GLM for bulk coding and reserve Claude for the hardest tasks.<\/p>\n<h3>Is GLM-5.1 free to use?<\/h3>\n<p>Yes \u2014 the GLM-5.1 weights are released under the permissive MIT license, so you can download, self-host, fine-tune, and use them commercially for free (you pay only for compute). Z.ai&#8217;s hosted API is paid but very cheap, including a GLM Coding Plan from $3-$30\/month that undercuts Claude Max many times over.<\/p>\n<h3>Is GLM-5.1 better than DeepSeek V4?<\/h3>\n<p>They&#8217;re close, with different strengths. GLM-5.1 is tuned for agentic coding and tops some coding leaderboards, and its all-Huawei training is a unique strategic angle. DeepSeek V4 is cheaper still, has a larger 1M context window (vs GLM&#8217;s 200K), and is a stronger all-rounder. For routine coding on a budget both are excellent; for the longest-context work, DeepSeek edges it.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>R\u00e9sultat<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>GLM-5.1 is the most strategically interesting model in Chinese AI. It delivers near-Claude coding quality, ships as MIT-licensed open weights, costs a fraction of Western alternatives, and \u2014 uniquely \u2014 proves that a frontier-competitive model can be trained entirely on Chinese silicon.<\/p>\n<p>The honest caveats keep it grounded: discount the self-reported benchmark peaks, note the 200K context ceiling, and route around the hosted API for sensitive data by self-hosting the open weights. Do that, and GLM-5.1 is one of the best value propositions in AI \u2014 and, with its all-Huawei training, the clearest sign yet that the global AI landscape is no longer one the US can control through hardware alone.<\/p>","protected":false},"excerpt":{"rendered":"<p>GLM-5.1 is the Claude alternative that costs $3 a month and was trained without any Nvidia hardware. Zhipu&#8217;s model is a geopolitical statement as much as a technical one.<\/p>","protected":false},"author":1,"featured_media":761,"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":[604],"tags":[609,621,615,622,620],"class_list":["post-756","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-chinese-ai","tag-chinese-ai","tag-glm-5","tag-open-weights-llm","tag-z-ai","tag-zhipu-glm"],"_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/756","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=756"}],"version-history":[{"count":1,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/756\/revisions"}],"predecessor-version":[{"id":781,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/756\/revisions\/781"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/761"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=756"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=756"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=756"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}