{"id":324827,"date":"2026-08-02T07:00:00","date_gmt":"2026-08-02T04:00:00","guid":{"rendered":"https:\/\/ceotudent.com\/which-ai-model-for-which-task-routing-guide-2026"},"modified":"2026-08-02T07:00:00","modified_gmt":"2026-08-02T04:00:00","slug":"which-ai-model-for-which-task-routing-guide-2026","status":"publish","type":"post","link":"https:\/\/ceotudent.com\/en\/which-ai-model-for-which-task-routing-guide-2026","title":{"rendered":"Which AI Model for Which Task: A Knowledge Worker&#8217;s Routing Guide for 2026"},"content":{"rendered":"<p><strong>TL;DR:<\/strong> &ldquo;Which AI should I use?&rdquo; is one of the most common questions of the assistant era, and almost every answer to it is obsolete within weeks, because the frontier reorders itself constantly. The durable skill is not memorizing today&rsquo;s leader; it is routing, which means matching a task to the one capability axis it truly depends on, then picking any model that clears the bar on that axis at the lowest cost. This guide gives you the five axes that decide model choice, reasoning depth, context length, modality, latency and cost, and privacy, and a Task-to-Model Routing Matrix that maps common knowledge-work jobs to the axis that governs them. It teaches you to read the public benchmarks yourself, SWE-bench Verified for coding, GPQA Diamond for hard reasoning, AIME for math, LMArena for human preference, and Artificial Analysis for a combined view, so you can evaluate a new model the day it launches instead of waiting for someone&rsquo;s ranking. And it gives you the cost-cascade pattern that sends easy work to a cheap model and escalates only the hard cases. A CEO does not buy the most expensive tool for every job; a student never stops re-checking whether last month&rsquo;s best is still this month&rsquo;s.<\/p>\n<p>Every week or two, a new model tops some benchmark, a headline declares a new king, and a fresh round of &ldquo;the best AI for X&rdquo; articles goes stale before it finishes being written. If your model-selection strategy is to remember the current winner, you have signed up for a job with no end, and you will still be wrong half the time because the answer depends on the task, not on the trophy. The professionals who use AI well have quietly stopped playing that game. They route.<\/p>\n<p>Routing means you stop asking &ldquo;which model is best&rdquo; and start asking &ldquo;which capability does this specific task actually need, and what is the cheapest model that clears the bar.&rdquo; That question has a stable answer even when the leaderboard does not, because the axes that matter change far more slowly than the rankings on them.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" 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: #999;color:#999\" 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><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/ceotudent.com\/en\/which-ai-model-for-which-task-routing-guide-2026\/#Why-%E2%80%9Cwhich-model-is-best%E2%80%9D-is-the-wrong-question\" >Why &ldquo;which model is best&rdquo; is the wrong question<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/ceotudent.com\/en\/which-ai-model-for-which-task-routing-guide-2026\/#The-five-axes-that-actually-decide-model-choice\" >The five axes that actually decide model choice<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/ceotudent.com\/en\/which-ai-model-for-which-task-routing-guide-2026\/#The-Task-to-Model-Routing-Matrix\" >The Task-to-Model Routing Matrix<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/ceotudent.com\/en\/which-ai-model-for-which-task-routing-guide-2026\/#Benchmark-literacy-reading-the-tests-yourself\" >Benchmark literacy: reading the tests yourself<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/ceotudent.com\/en\/which-ai-model-for-which-task-routing-guide-2026\/#The-cost-cascade-do-not-pay-frontier-prices-for-lookup-work\" >The cost cascade: do not pay frontier prices for lookup work<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/ceotudent.com\/en\/which-ai-model-for-which-task-routing-guide-2026\/#A-simple-routing-workflow-you-can-run-today\" >A simple routing workflow you can run today<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/ceotudent.com\/en\/which-ai-model-for-which-task-routing-guide-2026\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/ceotudent.com\/en\/which-ai-model-for-which-task-routing-guide-2026\/#Kaynakca\" >Kaynak\u00e7a<\/a><\/li><\/ul><\/nav><\/div>\n<h2 id=\"why-which-model-is-best-is-the-wrong-question\"><span class=\"ez-toc-section\" id=\"Why-%E2%80%9Cwhich-model-is-best%E2%80%9D-is-the-wrong-question\"><\/span>Why &ldquo;which model is best&rdquo; is the wrong question<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>There is no single best model, in the same way there is no single best vehicle. A sports car, a cargo van, and a bicycle are not competing for one crown; they win different tasks. Frontier language models have specialized in exactly this way. One leads on writing code, another on multi-step reasoning, another on holding a million tokens of context, another on cost per call. Asking which is best is a category error. Asking which fits this job is the whole skill.<\/p>\n<p>This reframing is liberating because it converts an impossible tracking problem into a manageable diagnostic one. You do not need to know every model&rsquo;s score on every test. You need to know what your task demands and how to check whether a candidate meets it. That is a question you can answer in minutes and re-answer whenever the field moves. It is the same shift from tool-collecting to tool-fitting that we made in <a href=\"https:\/\/ceotudent.com\/en\/ai-productivity-stack-ranked-comparison-2026\">the AI productivity stack<\/a>: the ranking is perishable, the selection method is not.<\/p>\n<h2 id=\"the-five-axes-that-actually-decide-model-choice\"><span class=\"ez-toc-section\" id=\"The-five-axes-that-actually-decide-model-choice\"><\/span>The five axes that actually decide model choice<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Nearly every routing decision comes down to which of five axes dominates the task. Name the dominant axis and the choice narrows from dozens of models to a short list.<\/p>\n<p><strong>Reasoning depth.<\/strong> Does the task require multi-step logic, planning, math, or careful chains of inference, or is it mostly retrieval and rephrasing? Hard reasoning is where the most capable and most expensive models earn their price. Simple reformatting is where they are pure waste.<\/p>\n<p><strong>Context length.<\/strong> How much material must the model hold at once? Summarizing a paragraph needs almost none. Analyzing a two-hundred-page contract, a full codebase, or a year of meeting notes needs a large context window, and this is a hard constraint: a model that cannot fit the input cannot do the job at any quality.<\/p>\n<p><strong>Modality.<\/strong> Is the input purely text, or does it include images, audio, charts, screenshots, or video? A task that hinges on reading a diagram or a screenshot needs a genuinely multimodal model, and text-only strength does not transfer.<\/p>\n<p><strong>Latency and cost.<\/strong> Is this a one-off deep task where you will happily wait and pay, or a high-volume, real-time job run thousands of times where speed and price per call dominate? The right model for a single strategic analysis is often the wrong model for an automation that fires on every support ticket.<\/p>\n<p><strong>Privacy and control.<\/strong> Can the data leave your environment? Some material, regulated, confidential, or proprietary, cannot go to a third-party API, which pushes you toward open-weight models you can self-host, even at some capability cost. This axis can override all the others.<\/p>\n<p>The discipline is to identify the <strong>dominant<\/strong> axis before shortlisting. Most tasks have one axis that decides the outcome and others that barely matter. A high-volume classification job is governed by cost, not by frontier reasoning. A contract analysis is governed by context length, not by speed. Lead with the axis that binds.<\/p>\n<h2 id=\"the-task-to-model-routing-matrix\"><span class=\"ez-toc-section\" id=\"The-Task-to-Model-Routing-Matrix\"><\/span>The Task-to-Model Routing Matrix<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The following matrix, a CEOtudent editorial framework, maps common knowledge-work tasks to the axis that governs them and the selection rule that follows. It deliberately names no specific model, because the specific winner rotates; the governing axis does not. Use it to build your shortlist, then confirm today&rsquo;s best fit on a live leaderboard.<\/p>\n<table>\n<thead>\n<tr>\n<th>Task<\/th>\n<th>Dominant axis<\/th>\n<th>What to select for<\/th>\n<th>Routing rule<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Drafting, rewriting, summarizing your own text<\/td>\n<td>Latency and cost<\/td>\n<td>Fast, cheap, good-enough writing quality<\/td>\n<td>Use a mid-tier model; frontier reasoning is wasted here<\/td>\n<\/tr>\n<tr>\n<td>Complex coding, refactoring, debugging across files<\/td>\n<td>Reasoning depth (plus context)<\/td>\n<td>Top coding benchmark performance and a large context window<\/td>\n<td>Route to a current coding leader; verify on SWE-bench Verified<\/td>\n<\/tr>\n<tr>\n<td>Multi-step analysis, strategy, hard math or logic<\/td>\n<td>Reasoning depth<\/td>\n<td>Strongest reasoning and planning scores<\/td>\n<td>Route to a frontier reasoning model; check GPQA and AIME<\/td>\n<\/tr>\n<tr>\n<td>Analyzing long documents, whole codebases, large transcripts<\/td>\n<td>Context length<\/td>\n<td>The largest reliable context window that fits the input<\/td>\n<td>Filter first by context capacity, then by quality within that set<\/td>\n<\/tr>\n<tr>\n<td>Reading charts, screenshots, diagrams, photos, audio<\/td>\n<td>Modality<\/td>\n<td>Genuine multimodal capability, not bolted-on<\/td>\n<td>Require a natively multimodal model; ignore text-only leaders<\/td>\n<\/tr>\n<tr>\n<td>High-volume automation, classification, extraction at scale<\/td>\n<td>Latency and cost<\/td>\n<td>Lowest price and latency that meets an accuracy floor<\/td>\n<td>Use the cheapest model that passes your eval; escalate only failures<\/td>\n<\/tr>\n<tr>\n<td>Regulated, confidential, or proprietary data<\/td>\n<td>Privacy and control<\/td>\n<td>Deployable in your own environment<\/td>\n<td>Prefer open-weight self-hosted; accept some capability trade-off<\/td>\n<\/tr>\n<tr>\n<td>Real-time conversation, voice, live assistance<\/td>\n<td>Latency<\/td>\n<td>Fastest response at acceptable quality<\/td>\n<td>Optimize for speed; frontier depth is usually unnecessary<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The pattern across the rows is the point: the expensive frontier model is the right answer for only a minority of tasks, the ones governed by reasoning depth. For the majority, governed by cost, latency, context, or privacy, routing to a smaller, cheaper, or specialized model is not a compromise. It is the correct engineering decision.<\/p>\n<h2 id=\"benchmark-literacy-reading-the-tests-yourself\"><span class=\"ez-toc-section\" id=\"Benchmark-literacy-reading-the-tests-yourself\"><\/span>Benchmark literacy: reading the tests yourself<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The reason you can evaluate a new model the day it launches is that the same handful of public benchmarks are reported every time. Knowing what each one actually measures lets you skip the hype and read the scorecard directly. These are the benchmarks that recur, and what they genuinely test.<\/p>\n<table>\n<thead>\n<tr>\n<th>Benchmark<\/th>\n<th>What it measures<\/th>\n<th>Route it informs<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>SWE-bench Verified<\/td>\n<td>Ability to resolve real, human-verified GitHub software issues end to end<\/td>\n<td>Coding and agentic engineering tasks<\/td>\n<\/tr>\n<tr>\n<td>GPQA Diamond<\/td>\n<td>Graduate-level science questions written to be hard to answer even with web search<\/td>\n<td>Deep reasoning and expert-domain work<\/td>\n<\/tr>\n<tr>\n<td>AIME<\/td>\n<td>Competition mathematics problems requiring multi-step exact reasoning<\/td>\n<td>Math and rigorous logical tasks<\/td>\n<\/tr>\n<tr>\n<td>MMLU and successors<\/td>\n<td>Broad multi-subject knowledge across dozens of academic fields<\/td>\n<td>General-knowledge breadth<\/td>\n<\/tr>\n<tr>\n<td>LMArena (Chatbot Arena)<\/td>\n<td>Human preference from blind head-to-head votes, expressed as an Elo-style rating<\/td>\n<td>Overall real-world usefulness as judged by people<\/td>\n<\/tr>\n<tr>\n<td>Artificial Analysis<\/td>\n<td>An aggregated index combining quality, speed, and price across models<\/td>\n<td>First-pass shortlisting on the cost-quality frontier<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Two literacy rules keep you honest. First, match the benchmark to the axis: a high MMLU score tells you little about coding, and a high SWE-bench score tells you little about reading a chart. A model can lead one and lag another, which is exactly why routing beats a single ranking. Second, treat human-preference and aggregate boards, LMArena and Artificial Analysis, as your freshness mechanism. Because they update continuously and cover many models at once, they are where you confirm today&rsquo;s best fit for a shortlist the matrix built. Benchmarks also age and leak into training data over time, so recency of the board matters as much as the number on it. Reading scores critically rather than trusting a headline is the same evaluation muscle we argue is now core knowledge work in <a href=\"https:\/\/ceotudent.com\/en\/prompt-engineering-is-not-enough-ai-literacy-stack\">the AI literacy stack<\/a>.<\/p>\n<h2 id=\"the-cost-cascade-do-not-pay-frontier-prices-for-lookup-work\"><span class=\"ez-toc-section\" id=\"The-cost-cascade-do-not-pay-frontier-prices-for-lookup-work\"><\/span>The cost cascade: do not pay frontier prices for lookup work<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The single most expensive routing mistake is sending every task to the most capable model because it is the safest choice. It is safe on quality and reckless on cost, and at any real volume the bill and the latency compound fast.<\/p>\n<p>The professional pattern is a cascade. Send the task to a cheap, fast model first. Add a check: is the output good enough against a clear standard? If yes, you are done at a fraction of the cost. If no, escalate to a stronger model, and only then to the frontier. Most tasks resolve at the first or second tier, so you pay frontier prices only for the minority that genuinely need frontier capability. This mirrors how a well-run organization assigns work: routine cases to the fastest available resource, hard cases escalated to the specialist, and nobody putting the most expensive person on tasks a junior could clear. The judgment that makes the cascade work is knowing what &ldquo;good enough&rdquo; means for each task, which is the evaluation skill, not the generation skill. If you are delegating whole workflows rather than single prompts, the same tiering logic scales up, as we lay out in <a href=\"https:\/\/ceotudent.com\/en\/how-to-delegate-to-an-ai-agent-briefing-framework\">the AI agent briefing framework<\/a>.<\/p>\n<h2 id=\"a-simple-routing-workflow-you-can-run-today\"><span class=\"ez-toc-section\" id=\"A-simple-routing-workflow-you-can-run-today\"><\/span>A simple routing workflow you can run today<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Put together, the method is a short sequence you can apply to any task in under a minute.<\/p>\n<p>First, name the dominant axis: reasoning, context, modality, cost, or privacy. Second, apply the hard filters, context capacity and privacy come first, because a model that cannot fit your input or cannot touch your data is disqualified regardless of quality. Third, within the survivors, consult the benchmark that matches your axis plus a live preference board for today&rsquo;s ordering. Fourth, start at the cheapest candidate that plausibly clears the bar and escalate only if it fails your check. Fifth, re-run this whenever the task type recurs at scale or the field visibly moves, because the routing method is permanent but the routes are not.<\/p>\n<p>This is deliberately not a list of model names, because any such list decays. It is a procedure that survives every reshuffle of the leaderboard, which is precisely what makes it worth learning.<\/p>\n<h2 id=\"faq\"><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>Isn&rsquo;t it easier to just use one frontier model for everything?<\/strong><br \/>\nEasier, and usually wrong on cost and sometimes on fit. A frontier model is overkill and overpriced for formatting, summarizing, and high-volume automation, and it can still be the wrong choice for a task that needs a large context window or multimodal input it does not lead on. One model for everything trades a small convenience for a large recurring cost and occasional capability gaps.<\/p>\n<p><strong>How do I keep up when the best model changes every few weeks?<\/strong><br \/>\nYou do not track the winners; you track the method. Name the axis your task needs, then check a continuously updated preference or aggregate board, LMArena or Artificial Analysis, for the current best on that axis. The board does the tracking for you. Learning to read it once replaces reading a hundred &ldquo;best AI&rdquo; articles.<\/p>\n<p><strong>What is the most common routing mistake?<\/strong><br \/>\nDefaulting to the most capable model for everything, which quietly wastes money and latency, and its mirror image, using a cheap model for a task that genuinely needs deep reasoning and getting a confident wrong answer. Both come from skipping the first step: naming the axis the task actually depends on.<\/p>\n<p><strong>Do these benchmarks actually predict real-world performance?<\/strong><br \/>\nImperfectly, which is why you use more than one. Task-specific benchmarks like SWE-bench and GPQA measure a targeted capability; human-preference boards like LMArena capture messier real-world usefulness. Reading them together, and weighting the one that matches your task, is far more reliable than trusting any single number, and benchmarks can also degrade as they leak into training data, so favor recent results.<\/p>\n<p><strong>When should I choose an open-weight model I can self-host?<\/strong><br \/>\nWhen privacy or control is the dominant axis: regulated, confidential, or proprietary data that cannot leave your environment. You may accept some capability trade-off relative to the closed frontier, but for that class of task the trade-off is the point, because a slightly weaker model you can run safely beats a stronger one you are not allowed to use.<\/p>\n<h2 id=\"kaynakca\"><span class=\"ez-toc-section\" id=\"Kaynakca\"><\/span>Kaynak\u00e7a<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>Stanford Institute for Human-Centered AI (2025). Artificial Intelligence Index Report, chapters on technical performance and benchmarking.<\/li>\n<li>OECD (2024). OECD Employment Outlook: analysis of artificial intelligence adoption in the workplace.<\/li>\n<li>National Institute of Standards and Technology (2024). AI Risk Management Framework, guidance on evaluation and model selection.<\/li>\n<li>World Economic Forum (2025). Future of Jobs Report, on AI tool adoption among knowledge workers.<\/li>\n<li>Chiang, W. et al. (2024). Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. Academic publication describing the LMArena methodology.<\/li>\n<li>Jimenez, C. et al. (2024). SWE-bench: Can Language Models Resolve Real-World GitHub Issues? International Conference on Learning Representations.<\/li>\n<\/ul>\n<hr>\n<p><em>This content was compiled with the support of AI following in-depth research, then written and prepared for publication by the CEOtudent editorial team.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The frontier changes every few weeks, so memorizing which model is best today is a losing game. What lasts is knowing which capability axis a task depends on and how to read the public benchmark that measures it. This is a routing guide, not a leaderboard: a task-to-model matrix built on the five axes that actually decide model choice, a plain-English guide to what SWE-bench, GPQA, AIME, and LMArena really test, and the cost-cascade pattern that stops you paying frontier prices for lookup work. Choose like a CEO who routes each job to the cheapest tool that clears the bar, and stay a student who re-checks the leaderboard instead of trusting last month&#8217;s answer.<\/p>\n","protected":false},"author":1,"featured_media":324842,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5,18],"tags":[],"class_list":["post-324827","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-is","category-strateji"],"_links":{"self":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts\/324827","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/comments?post=324827"}],"version-history":[{"count":0,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/posts\/324827\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/media\/324842"}],"wp:attachment":[{"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/media?parent=324827"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/categories?post=324827"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ceotudent.com\/en\/wp-json\/wp\/v2\/tags?post=324827"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}