{"id":788,"date":"2026-06-06T01:59:11","date_gmt":"2026-06-06T01:59:11","guid":{"rendered":"https:\/\/convly.ai\/best-local-llms-to-run-on-ollama-2026\/"},"modified":"2026-06-10T05:04:42","modified_gmt":"2026-06-10T05:04:42","slug":"best-local-llms-to-run-on-ollama-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/","title":{"rendered":"The Best Local LLMs to Run on Ollama in 2026 (Ranked by Use Case)"},"content":{"rendered":"<p>Ollama can run more than a hundred models, which is exactly why people freeze when picking one. The good news: you only need a handful. This guide ranks the best local LLMs in 2026 by the job you&#8217;re trying to do \u2014 general work, coding, reasoning, or squeezing onto weak hardware \u2014 and tells you the memory each one needs.<\/p>\n<p>New here? Start with <a href=\"https:\/\/convly.ai\/es\/what-is-ollama-complete-guide-2026\/\">what Ollama is<\/a>, then <a href=\"https:\/\/convly.ai\/es\/ollama-system-requirements-2026\/\">check your hardware<\/a> before downloading anything.<\/p>\n<div class=\"convly-tldr\">\n<h3>Conclusiones clave<\/h3>\n<ul>\n<li><strong>Mejor opci\u00f3n general:<\/strong> <strong>Gemma 4 26B A4B<\/strong> \u2014 tool calling + vision, runs comfortably, the most practical pick for most people. <code>ollama run gemma4<\/code><\/li>\n<li><strong>Best for coding:<\/strong> <strong>Qwen 3.6 27B<\/strong> \u2014 the strongest dense coding model at ~77% SWE-bench, needs ~22 GB VRAM.<\/li>\n<li><strong>Best for reasoning\/math:<\/strong> <strong>DeepSeek-R1 7B<\/strong> \u2014 best chain-of-thought performance you can run small.<\/li>\n<li><strong>Best for weak hardware:<\/strong> <strong>Gemma2 2B<\/strong> \u2014 runs on ~1.7 GB RAM, fine on a CPU-only laptop.<\/li>\n<li><strong>Safest commercial license:<\/strong> Qwen 3 and Gemma 4 ship under Apache 2.0.<\/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-6a38af8d5ea2a\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Alternar<\/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-6a38af8d5ea2a\"  aria-label=\"Alternar\" \/><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\/es\/best-local-llms-to-run-on-ollama-2026\/#How_to_think_about_picking_a_model\" >How to think about picking a model<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#Best_all-rounder_Gemma_4_26B_A4B\" >Best all-rounder: Gemma 4 26B A4B<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#Best_for_coding_Qwen_36_27B\" >Best for coding: Qwen 3.6 27B<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#Best_for_reasoning_and_math_DeepSeek-R1_7B\" >Best for reasoning and math: DeepSeek-R1 7B<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#Best_for_weak_hardware_Gemma2_2B\" >Best for weak hardware: Gemma2 2B<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#Best_for_enterprise_scale_Qwen3_235B-A22B\" >Best for enterprise scale: Qwen3 235B-A22B<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#Quick_comparison\" >Quick comparison<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#A_simple_decision_path\" >A simple decision path<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#Quantization_why_the_same_model_can_need_4_GB_or_14_GB\" >Quantization: why the same model can need 4 GB or 14 GB<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#FAQ\" >Preguntas frecuentes<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#Bottom_line\" >Conclusi\u00f3n<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/convly.ai\/es\/best-local-llms-to-run-on-ollama-2026\/#Related_articles\" >Art\u00edculos relacionados<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"How_to_think_about_picking_a_model\"><\/span>How to think about picking a model<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Three things decide which model is &#8220;best&#8221; for you, in this order:<\/p>\n<ol>\n<li><strong>What can your hardware fit?<\/strong> A model has to fit in your RAM or VRAM (in quantized form). The best model you <em>can&#8217;t<\/em> run is useless. Match the size to your machine using our <a href=\"https:\/\/convly.ai\/es\/ollama-system-requirements-2026\/\">gu\u00eda de requisitos del sistema<\/a>.<\/li>\n<li><strong>What&#8217;s the job?<\/strong> Coding, general chat, reasoning, and document work reward different models. A great coder isn&#8217;t always a great writer.<\/li>\n<li><strong>Does the license matter?<\/strong> If you&#8217;re building a product, prefer Apache 2.0 models (Qwen 3, Gemma 4) over more restrictively licensed ones.<\/li>\n<\/ol>\n<h2><span class=\"ez-toc-section\" id=\"Best_all-rounder_Gemma_4_26B_A4B\"><\/span>Best all-rounder: Gemma 4 26B A4B<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Google&#8217;s <strong>Gemma 4 26B A4B<\/strong> (released April 2026) is the model we&#8217;d put in most people&#8217;s hands first. It&#8217;s a mixture-of-experts design with built-in tool calling and vision support, and it punches well above its memory footprint \u2014 making it ideal for local agents, function calling, and structured output. It&#8217;s Apache 2.0, so you can build on it commercially.<\/p>\n<pre><code>ollama run gemma4\n<\/code><\/pre>\n<p>If you want a single model for chat, light coding, summarizing, and agent work, this is the safe default.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Best_for_coding_Qwen_36_27B\"><\/span>Best for coding: Qwen 3.6 27B<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>For writing and refactoring code locally \u2014 without sending a line to an API \u2014 <strong>Qwen 3.6 27B<\/strong> is the strongest dense coding model you can run, landing around <strong>77% on SWE-bench<\/strong> and needing roughly <strong>22 GB of VRAM<\/strong>. If your machine can hold it, it&#8217;s the closest thing to a cloud coding assistant that never phones home.<\/p>\n<p>Running on tighter hardware? Drop to a smaller Qwen coder variant or use Gemma 4. For the full breakdown of coding-specific picks and how they compare on real tasks, see our guide to the <a href=\"https:\/\/convly.ai\/es\/best-local-llm-for-coding-2026\/\">best local LLM for coding<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Best_for_reasoning_and_math_DeepSeek-R1_7B\"><\/span>Best for reasoning and math: DeepSeek-R1 7B<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>DeepSeek-R1 7B<\/strong> is a chain-of-thought model that delivers the best local math and reasoning performance at the 7B size. Because it &#8220;thinks&#8221; through problems step by step, it&#8217;s the one to reach for when correctness on multi-step logic matters more than speed. At 7B it fits on modest hardware, which makes it an unusually accessible reasoning model.<\/p>\n<pre><code>ollama run deepseek-r1\n<\/code><\/pre>\n<h2><span class=\"ez-toc-section\" id=\"Best_for_weak_hardware_Gemma2_2B\"><\/span>Best for weak hardware: Gemma2 2B<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>No discrete GPU? <strong>Gemma2 2B<\/strong> is the fastest CPU-inference option and needs only about <strong>1.7 GB of RAM<\/strong>. It won&#8217;t win benchmarks, but it&#8217;s genuinely usable for summarization, simple Q&amp;A, and drafting on a basic laptop \u2014 proof that you don&#8217;t need a workstation to start with local AI.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Best_for_enterprise_scale_Qwen3_235B-A22B\"><\/span>Best for enterprise scale: Qwen3 235B-A22B<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you have serious hardware and want a frontier-class open model with a clean license, <strong>Qwen3 235B-A22B<\/strong> is one of the safest enterprise picks: a mixture-of-experts model with 235B total parameters but only 22B active per token, under Apache 2.0. It&#8217;s well suited to multilingual apps and commercial products \u2014 provided you have the memory to host it.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Quick_comparison\"><\/span>Quick comparison<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Modelo<\/th>\n<th>Ideal para<\/th>\n<th>Rough memory<\/th>\n<th>Licencia<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Gemma 4 26B A4B<\/td>\n<td>General \/ agents \/ vision<\/td>\n<td>Mid-range GPU<\/td>\n<td>Apache 2.0<\/td>\n<\/tr>\n<tr>\n<td>Qwen 3.6 27B<\/td>\n<td>Coding<\/td>\n<td>~22 GB VRAM<\/td>\n<td>Apache 2.0<\/td>\n<\/tr>\n<tr>\n<td>DeepSeek-R1 7B<\/td>\n<td>Reasoning \/ math<\/td>\n<td>Modest<\/td>\n<td>MIT<\/td>\n<\/tr>\n<tr>\n<td>Gemma2 2B<\/td>\n<td>Weak \/ CPU-only hardware<\/td>\n<td>~1.7 GB RAM<\/td>\n<td>Gemma license<\/td>\n<\/tr>\n<tr>\n<td>Qwen3 235B-A22B<\/td>\n<td>Enterprise \/ multilingual<\/td>\n<td>Very high<\/td>\n<td>Apache 2.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"A_simple_decision_path\"><\/span>A simple decision path<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>One model for everything \u2192<\/strong> Gemma 4.<\/li>\n<li><strong>Mostly coding, strong GPU \u2192<\/strong> Qwen 3.6 27B.<\/li>\n<li><strong>Hard reasoning or math \u2192<\/strong> DeepSeek-R1.<\/li>\n<li><strong>Old laptop, no GPU \u2192<\/strong> Gemma2 2B.<\/li>\n<li><strong>Building a commercial product \u2192<\/strong> stick to the Apache 2.0 models (Qwen 3, Gemma 4).<\/li>\n<\/ul>\n<p>Whichever you choose, the command is the same \u2014 <code>ollama run &lt;model&gt;<\/code> \u2014 and you can keep several installed and switch freely. To run any of them, you&#8217;ll first need Ollama set up: here&#8217;s our <a href=\"https:\/\/convly.ai\/es\/how-to-install-ollama-2026\/\">gu\u00eda paso a paso para la instalaci\u00f3n<\/a>.<\/p>\n<p><!--ai-enriched--><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Quantization_why_the_same_model_can_need_4_GB_or_14_GB\"><\/span>Quantization: why the same model can need 4 GB or 14 GB<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Every VRAM figure in this guide is really a quantization figure. A model&#8217;s raw weights ship at 16-bit precision (FP16), but Ollama compresses them before they run on your machine \u2014 and that compression level, not the parameter count alone, decides whether a model fits. When you run <code>ollama run gemma4<\/code> without specifying a tag, Ollama pulls a <strong>Q4_K_M<\/strong> build by default: a 4-bit quantization that is the de facto standard for consumer hardware.<\/p>\n<p>The savings are dramatic. A 7B model takes roughly <strong>14 GB at FP16, about 7.7 GB at Q8_0, and only ~4.5 GB at Q4_K_M<\/strong>. That 4-bit default is why a 7B reasoning model fits an 8 GB card with room to spare, and why the &#8220;22 GB&#8221; for a 27B coder isn&#8217;t 50+ GB. The quality cost is smaller than most people expect: Q4_K_M typically loses only <strong>1\u20133% on benchmarks like MMLU<\/strong> versus full precision \u2014 a 7B model scoring 73% at FP16 lands around 71\u201372%. In practice that surfaces as the occasional reworded sentence, not wrong answers.<\/p>\n<p>So when should you move off the default?<\/p>\n<ul>\n<li><strong>Stay on Q4_K_M<\/strong> for chat, drafting, summarizing, and general agent work. It is the best balance of quality and footprint, full stop.<\/li>\n<li><strong>Step up to Q8_0<\/strong> (near-lossless, but roughly double the memory) only for code generation and exacting reasoning, where a single wrong token breaks the output \u2014 and only if you have the VRAM headroom.<\/li>\n<li><strong>Drop to Q3 or smaller<\/strong> as a last resort to squeeze a bigger model onto a small card. You will feel the quality loss, and a smaller model at Q4 is usually the better trade.<\/li>\n<\/ul>\n<p>You pull a specific level by appending the tag: <code>ollama run qwen3.6:27b-q8_0<\/code> instead of the bare name. The rule of thumb that holds across hardware: <strong>a bigger model at Q4 almost always beats a smaller model at Q8<\/strong> at the same memory budget. Quantization is what lets you run the model you actually want \u2014 pick the largest model your machine fits at Q4_K_M first, then only raise precision if quality demands it and the VRAM is there.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>Preguntas frecuentes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>What is the best Ollama model in 2026?<\/h3>\n<p>For most people, Gemma 4 26B A4B \u2014 it&#8217;s a capable all-rounder with tool calling and vision, an Apache 2.0 license, and a reasonable memory footprint. For coding specifically, Qwen 3.6 27B is stronger; for reasoning, DeepSeek-R1.<\/p>\n<h3>What&#8217;s the best local LLM for low-end hardware?<\/h3>\n<p>Gemma2 2B. It runs in about 1.7 GB of RAM and works on CPU-only laptops. If you have a little more headroom, a 7\u20138B model like DeepSeek-R1 7B gives noticeably better quality while still fitting modest machines.<\/p>\n<h3>Which local model is closest to ChatGPT?<\/h3>\n<p>The largest open models you can host \u2014 like Qwen3 235B-A22B \u2014 close much of the gap, but on the hardest reasoning tasks the best cloud frontier models still lead. For everyday chat, coding, and document work, a well-chosen local model is more than good enough and keeps your data private.<\/p>\n<h3>Do I need a powerful GPU for these models?<\/h3>\n<p>It depends on the model. Gemma2 2B runs on a CPU; a 7B model is comfortable on 8 GB of memory; Qwen 3.6 27B wants ~22 GB of VRAM. Match the model to your hardware using our <a href=\"https:\/\/convly.ai\/es\/ollama-system-requirements-2026\/\">gu\u00eda de requisitos del sistema<\/a>.<\/p>\n<h3>Are these models free for commercial use?<\/h3>\n<p>Qwen 3 and Gemma 4 ship under Apache 2.0, which is permissive for commercial use. DeepSeek-R1 is MIT-licensed. Always confirm the specific model&#8217;s license before shipping a product, since terms can vary by release.<\/p>\n<h3>How do I download a higher-quality, less-compressed version of a model?<\/h3>\n<p>Append the quantization tag to the model name. <code>ollama run qwen3.6:27b<\/code> gives you the default Q4_K_M build; <code>ollama run qwen3.6:27b-q8_0<\/code> pulls the near-lossless 8-bit version of the <em>mismo<\/em> model, which roughly doubles the memory needed. Browse a model&#8217;s page on ollama.com to see every tag it actually publishes \u2014 naming follows the <code>model:size-quant<\/code> pattern. For chat and general use the Q4_K_M default is the right call; reserve Q8_0 for coding or precise reasoning where you have VRAM to spare.<\/p>\n<h3>Can I run more than one model at the same time?<\/h3>\n<p>Yes, but they share your memory. Ollama loads a model on demand and keeps it resident for a few minutes, so switching between, say, Gemma 4 and DeepSeek-R1 is instant once both are installed \u2014 but running them concurrently means their footprints add up. On a single 8\u201316 GB GPU, expect to run one capable model at a time and let Ollama swap them as you call each. Keep as many installed as you like; only the active ones consume VRAM.<\/p>\n<h3>Why does my model slow down or run out of memory on long documents?<\/h3>\n<p>Because context costs VRAM. Beyond the model&#8217;s own weights, Ollama allocates a KV cache that grows linearly with the context window, and modern Ollama scales the default context with your hardware (about 4K tokens under 24 GB of VRAM, rising to 32K from 24\u201348 GB and 256K beyond that). Feeding in a long document or chat history can add gigabytes of cache and sharply cut tokens-per-second. If you hit limits, shorten the context length, or enable KV-cache quantization, which can roughly halve that overhead with minimal quality impact.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>Conclusi\u00f3n<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You don&#8217;t need to test a hundred models \u2014 you need the right four or five. Run Gemma 4 as your default, Qwen 3.6 when you&#8217;re coding, DeepSeek-R1 when you need to reason, and Gemma2 2B when hardware is tight. Each is a single <code>ollama run<\/code> away, and all of them keep your data on your own machine.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Art\u00edculos relacionados<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/es\/claude-5-new-ai-models-june-2026\/\">\u00bfExiste una Claude 5? Claude Fable 5 y todos los principales modelos de IA de junio de 2026<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/es\/llm-hallucinations-complete-guide\/\">Alucinaciones de modelos de lenguaje de gran tama\u00f1o en 2026: por qu\u00e9 ocurren y c\u00f3mo evitarlas<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/es\/prompt-engineering-techniques\/\">Ingenier\u00eda de indicaciones (prompt engineering) en 2026: 12 t\u00e9cnicas que realmente funcionan<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/es\/what-is-ollama-complete-guide-2026\/\">\u00bfQu\u00e9 es Ollama? Gu\u00eda completa para ejecutar modelos de lenguaje de gran tama\u00f1o localmente en 2026<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Ollama can run 100+ models, but you only need a handful. Here are the best local LLMs in 2026 ranked by what you&#8217;re actually trying to do \u2014 and the VRAM each one needs.<\/p>","protected":false},"author":1,"featured_media":794,"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":[3],"tags":[629,630,633,632,631,606],"class_list":["post-788","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-llms","tag-best-local-llm","tag-best-ollama-models","tag-deepseek-r1","tag-gemma-4","tag-ollama-models","tag-qwen-3"],"_links":{"self":[{"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/posts\/788","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/comments?post=788"}],"version-history":[{"count":2,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/posts\/788\/revisions"}],"predecessor-version":[{"id":954,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/posts\/788\/revisions\/954"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/media\/794"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/media?parent=788"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/categories?post=788"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/es\/wp-json\/wp\/v2\/tags?post=788"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}