{"id":16929,"date":"2026-07-22T09:27:03","date_gmt":"2026-07-22T13:27:03","guid":{"rendered":"https:\/\/wp.glbgpt.com\/?p=16929"},"modified":"2026-07-22T09:27:03","modified_gmt":"2026-07-22T13:27:03","slug":"gemini-3-5-flash-lite-review","status":"publish","type":"post","link":"https:\/\/wp.glbgpt.com\/nl\/hub\/gemini-3-5-flash-lite-review","title":{"rendered":"Gemini 3.5 Flash-Lite-recensie: praktische tests, prijzen en prestaties"},"content":{"rendered":"<p class=\"wp-block-paragraph\"><strong>Is Gemini 3.5 Flash-Lite worth using?<\/strong> Yes\u2014especially for fast, repeatable work such as document processing, structured extraction, translation and bounded coding tasks. It combines a 1-million-token input window with low API pricing and some of the fastest measured output speeds in its class.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In my four controlled tests, <strong>Gemini 3.5 Flash-Lite<\/strong> completed every task on the first request and scored 38\/40. It was excellent at JSON extraction, localization and debugging, but its long-document action register assigned two owners that should have remained unassigned. The practical verdict is simple: use it for throughput, but validate fields where small mistakes carry consequences.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If you want to try it without configuring an API, <a href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_content_home&amp;login=1\">GlobalGPT<\/a> is a simple way to start. Gemini 3.5 Flash-Lite is available alongside popular models<a href=\"https:\/\/www.glbgpt.com\/home\/gpt-5-6-sol?inviter=hub_content_gptsol56&amp;login=1\"> such as GPT-5.6,<\/a> Claude Fable 5 and Kimi K3, giving you access to the strengths of several leading AI families in one workspace.<\/p>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<figure class=\"wp-block-image size-large\"><img alt=\"\" fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"640\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-1024x640.png\" class=\"wp-image-15877\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-1024x640.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-300x187.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-768x480.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-1536x960.png 1536w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-2048x1279.png 2048w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/03\/image-831-18x12.png 18w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-3e41869c wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-black-color has-luminous-vivid-amber-background-color has-text-color has-background has-link-color wp-element-button\">Probeer meer dan 100 topmodellen op GlobalGPT<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 14px;margin:12px 0}summary{cursor:pointer;font-weight:700}.toc{background:#f8fafc;padding:18px 22px;border-radius:8px}.small{font-size:.92rem;color:#667085}.metric-grid{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px;margin:22px 0}.metric{border:1px solid #dbe3f0;border-radius:8px;padding:16px;background:linear-gradient(145deg,#fff,#f7f9ff)}.metric strong{display:block;font-size:1.65rem;color:#1849a9;line-height:1.1}.metric span{font-size:.82rem;color:#667085}.chart{border:1px solid #d0d5dd;border-radius:8px;padding:20px;margin:22px 0;background:#fff}.bar-row{display:grid;grid-template-columns:170px 1fr 60px;gap:10px;align-items:center;margin:13px 0}.bar-track{height:14px;background:#eef2f6;border-radius:4px;overflow:hidden}.bar-fill{height:100%;background:linear-gradient(90deg,#155eef,#7f56d9)}.bar-fill.warn{background:linear-gradient(90deg,#f79009,#fdb022)}.test-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px;margin:22px 0}.test-card{border:1px solid #d0d5dd;border-radius:8px;overflow:hidden;background:#fff}.test-head{display:flex;justify-content:space-between;gap:12px;padding:16px 18px;background:#f8fafc;border-bottom:1px solid #e4e7ec}.score{font-size:1.45rem;font-weight:800;color:#067647}.score.warn{color:#b54708}.test-body{padding:16px 18px}.mini-stats{display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:0 0 14px}.mini-stats div{background:#f5f7fa;border-radius:6px;padding:9px}.mini-stats strong{display:block;font-size:1rem}.mini-stats span{font-size:.72rem;color:#667085}.check-list{margin:10px 0 0;padding-left:20px}.check-list li{margin:6px 0}.experience{background:#f9fafb;border-left:3px solid #98a2b3;padding:11px 13px;margin-top:14px}.test-card details.evidence-toggle{margin:15px 0 0;padding:0;border:0;border-top:1px solid #e4e7ec;border-radius:0}.test-card details.evidence-toggle summary{padding:12px 0 2px;color:#155eef;font-size:.85rem}.evidence-block{margin-top:10px;padding:12px 14px;background:#f5f7fa;border:1px solid #e4e7ec;border-radius:6px}.evidence-block strong{display:block;margin-bottom:6px;font-size:.76rem;color:#475467;text-transform:uppercase;letter-spacing:.04em}.evidence-block pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<nav class=\"toc\" aria-label=\"Inhoudsopgave\">\n<strong>Inhoudsopgave<\/strong>\n<ol>\n<li><a href=\"#short-verdict\">Short verdict<\/a><\/li>\n<li><a href=\"#what-it-is\">What it is and key specifications<\/a><\/li>\n<li><a href=\"#capabilities\">Capabilities and best-fit tasks<\/a><\/li>\n<li><a href=\"#benchmarks\">Benchmarks and independent speed data<\/a><\/li>\n<li><a href=\"#test-setup\">How I tested it<\/a><\/li>\n<li><a href=\"#hands-on-tests\">Four hands-on tests<\/a><\/li>\n<li><a href=\"#speed-cost\">Speed and cost<\/a><\/li>\n<li><a href=\"#pricing\">Prijs en waarde<\/a><\/li>\n<li><a href=\"#pros-cons\">Pros, cons and best users<\/a><\/li>\n<li><a href=\"#who-should-use\">Who should use it<\/a><\/li>\n<li><a href=\"#verdict\">Eindoordeel<\/a><\/li>\n<li><a href=\"#faq\">FAQ<\/a><\/li>\n<\/ol>\n<\/nav>\n\n\n\n<h2 id=\"short-verdict\" class=\"wp-block-heading\">Gemini 3.5 Flash-Lite Review: The Short Verdict<\/h2>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 14px;margin:12px 0}summary{cursor:pointer;font-weight:700}.toc{background:#f8fafc;padding:18px 22px;border-radius:8px}.small{font-size:.92rem;color:#667085}.metric-grid{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px;margin:22px 0}.metric{border:1px solid #dbe3f0;border-radius:8px;padding:16px;background:linear-gradient(145deg,#fff,#f7f9ff)}.metric strong{display:block;font-size:1.65rem;color:#1849a9;line-height:1.1}.metric span{font-size:.82rem;color:#667085}.chart{border:1px solid #d0d5dd;border-radius:8px;padding:20px;margin:22px 0;background:#fff}.bar-row{display:grid;grid-template-columns:170px 1fr 60px;gap:10px;align-items:center;margin:13px 0}.bar-track{height:14px;background:#eef2f6;border-radius:4px;overflow:hidden}.bar-fill{height:100%;background:linear-gradient(90deg,#155eef,#7f56d9)}.bar-fill.warn{background:linear-gradient(90deg,#f79009,#fdb022)}.test-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px;margin:22px 0}.test-card{border:1px solid #d0d5dd;border-radius:8px;overflow:hidden;background:#fff}.test-head{display:flex;justify-content:space-between;gap:12px;padding:16px 18px;background:#f8fafc;border-bottom:1px solid #e4e7ec}.score{font-size:1.45rem;font-weight:800;color:#067647}.score.warn{color:#b54708}.test-body{padding:16px 18px}.mini-stats{display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:0 0 14px}.mini-stats div{background:#f5f7fa;border-radius:6px;padding:9px}.mini-stats strong{display:block;font-size:1rem}.mini-stats span{font-size:.72rem;color:#667085}.check-list{margin:10px 0 0;padding-left:20px}.check-list li{margin:6px 0}.experience{background:#f9fafb;border-left:3px solid #98a2b3;padding:11px 13px;margin-top:14px}.test-card details.evidence-toggle{margin:15px 0 0;padding:0;border:0;border-top:1px solid #e4e7ec;border-radius:0}.test-card details.evidence-toggle summary{padding:12px 0 2px;color:#155eef;font-size:.85rem}.evidence-block{margin-top:10px;padding:12px 14px;background:#f5f7fa;border:1px solid #e4e7ec;border-radius:6px}.evidence-block strong{display:block;margin-bottom:6px;font-size:.76rem;color:#475467;text-transform:uppercase;letter-spacing:.04em}.evidence-block pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<div class=\"callout\"><strong>Verdict:<\/strong> The model is an excellent fit for fast, inexpensive tasks with outputs that can be checked automatically: JSON extraction, localization, classification and bounded code fixes. It handled all four test requests on the first technical attempt and averaged 3.748 seconds end to end through the Broly route. For long documents, its main-story comprehension was strong, but a strict owner field was wrong in two of three runs. Use it for throughput; verify high-consequence fields.<\/div>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 14px;margin:12px 0}summary{cursor:pointer;font-weight:700}.toc{background:#f8fafc;padding:18px 22px;border-radius:8px}.small{font-size:.92rem;color:#667085}.metric-grid{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px;margin:22px 0}.metric{border:1px solid #dbe3f0;border-radius:8px;padding:16px;background:linear-gradient(145deg,#fff,#f7f9ff)}.metric strong{display:block;font-size:1.65rem;color:#1849a9;line-height:1.1}.metric span{font-size:.82rem;color:#667085}.chart{border:1px solid #d0d5dd;border-radius:8px;padding:20px;margin:22px 0;background:#fff}.bar-row{display:grid;grid-template-columns:170px 1fr 60px;gap:10px;align-items:center;margin:13px 0}.bar-track{height:14px;background:#eef2f6;border-radius:4px;overflow:hidden}.bar-fill{height:100%;background:linear-gradient(90deg,#155eef,#7f56d9)}.bar-fill.warn{background:linear-gradient(90deg,#f79009,#fdb022)}.test-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px;margin:22px 0}.test-card{border:1px solid #d0d5dd;border-radius:8px;overflow:hidden;background:#fff}.test-head{display:flex;justify-content:space-between;gap:12px;padding:16px 18px;background:#f8fafc;border-bottom:1px solid #e4e7ec}.score{font-size:1.45rem;font-weight:800;color:#067647}.score.warn{color:#b54708}.test-body{padding:16px 18px}.mini-stats{display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:0 0 14px}.mini-stats div{background:#f5f7fa;border-radius:6px;padding:9px}.mini-stats strong{display:block;font-size:1rem}.mini-stats span{font-size:.72rem;color:#667085}.check-list{margin:10px 0 0;padding-left:20px}.check-list li{margin:6px 0}.experience{background:#f9fafb;border-left:3px solid #98a2b3;padding:11px 13px;margin-top:14px}.test-card details.evidence-toggle{margin:15px 0 0;padding:0;border:0;border-top:1px solid #e4e7ec;border-radius:0}.test-card details.evidence-toggle summary{padding:12px 0 2px;color:#155eef;font-size:.85rem}.evidence-block{margin-top:10px;padding:12px 14px;background:#f5f7fa;border:1px solid #e4e7ec;border-radius:6px}.evidence-block strong{display:block;margin-bottom:6px;font-size:.76rem;color:#475467;text-transform:uppercase;letter-spacing:.04em}.evidence-block pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<div class=\"metric-grid\" aria-label=\"Review summary metrics\">\n<div class=\"metric\"><strong>38\/40<\/strong><span>objective first-output score<\/span><\/div>\n<div class=\"metric\"><strong>3.748s<\/strong><span>average routed response time<\/span><\/div>\n<div class=\"metric\"><strong>4\/4<\/strong><span>tasks completed on first request<\/span><\/div>\n<div class=\"metric\"><strong>$0.0037<\/strong><span>API-equivalent test cost<\/span><\/div>\n<\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Best at:<\/strong> structured extraction, localization and bounded debugging.<\/li>\n\n\n\n<li><strong>Main caution:<\/strong> exact ownership fields in long documents need validation.<\/li>\n\n\n\n<li><strong>Value case:<\/strong> high-throughput work where speed and repeat-call cost both matter.<\/li>\n<\/ul>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 14px;margin:12px 0}summary{cursor:pointer;font-weight:700}.toc{background:#f8fafc;padding:18px 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1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<div class=\"cta-box\"><strong>Put Gemini 3.5 Flash-Lite into a complete AI workflow<\/strong><p>Use it for fast document, localization and structured-data tasks, then continue with leading models and creative tools in the same GlobalGPT workspace.<\/p><div class=\"cta-actions\"><a class=\"cta-button\" href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_content_home&amp;login=1\">Try Gemini 3.5 Flash-Lite<\/a><\/div><\/div>\n\n\n\n<h2 id=\"what-it-is\" class=\"wp-block-heading\">What Is Gemini 3.5 Flash-Lite?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Gemini 3.5 Flash-Lite<\/strong> is the fastest and lowest-cost model in Google&#8217;s 3.5 family. The official API reference describes it as a low-latency multimodal model optimized for high-throughput subagent work, document parsing and simple data extraction. It accepts text, images, video, audio and PDFs, but produces text rather than native image, audio or video output.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The published limits are 1,048,576 input tokens and 65,536 output tokens. That makes the model relevant to large document collections, although a large context window does not guarantee perfect retrieval from every field. The model reference lists structured outputs, function calling, code execution, file search, search grounding, caching, thinking and URL context as supported.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1008\" height=\"1024\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/gemini-flash-lite-specs-1008x1024.webp\" alt=\"The official model reference lists multimodal inputs, text output and a 1,048,576-token input limit.\" class=\"wp-image-16933\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/gemini-flash-lite-specs-1008x1024.webp 1008w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/gemini-flash-lite-specs-295x300.webp 295w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/gemini-flash-lite-specs-768x780.webp 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/gemini-flash-lite-specs-1511x1536.webp 1511w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/gemini-flash-lite-specs-12x12.webp 12w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/gemini-flash-lite-specs.webp 1600w\" sizes=\"(max-width: 1008px) 100vw, 1008px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">There is one documentation conflict worth flagging. Google&#8217;s launch article says Computer Use is now a built-in tool, while the model reference viewed on July 22 marks Computer Use as unsupported. This may reflect different product surfaces or a documentation rollout delay. If that capability is essential, check the exact API or platform interface before designing a production workflow.<\/p>\n\n\n\n<h2 id=\"capabilities\" class=\"wp-block-heading\">Gemini 3.5 Flash-Lite Capabilities and Best-Fit Tasks<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The practical advantage is not simply that it answers quickly. The model combines long-context input, controllable thinking levels and schema-constrained output at a price suited to repeated calls. Google specifically names agentic search, document processing, translation and simple data processing. Those are workloads where a small saving on every request becomes meaningful at scale.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Document operations:<\/strong> summarize files, extract action items, classify records and answer questions across long packets.<\/li>\n\n\n\n<li><strong>Structured processing:<\/strong> convert messy messages or reports into predictable JSON for downstream automation.<\/li>\n\n\n\n<li><strong>Localization:<\/strong> preserve names, figures and constraints while adapting tone for a target market.<\/li>\n\n\n\n<li><strong>Agent sub-tasks:<\/strong> handle bounded steps that do not require the most expensive reasoning model.<\/li>\n\n\n\n<li><strong>Coding assistance:<\/strong> diagnose self-contained bugs, propose corrections and generate executable tests.<\/li>\n<\/ul>\n\n\n\n<h2 id=\"benchmarks\" class=\"wp-block-heading\">Gemini 3.5 Flash-Lite Benchmarks<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Google reports a substantial generation-over-generation improvement. The strongest gains appear in agentic coding, computer interaction and long-context retrieval. These are vendor-reported results, so they describe controlled evaluation performance rather than guaranteed behavior on a private workflow.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Benchmark<\/th><th>3.5 Flash-Lite<\/th><th>Vergelijking<\/th><th>Wat het test<\/th><\/tr><\/thead><tbody><tr><td>SWE-Bench Pro<\/td><td>54.2%<\/td><td>3 Flash: 49.6%<\/td><td>Real software engineering tasks<\/td><\/tr><tr><td>Terminal-Bench 2.1<\/td><td>54.0%<\/td><td>3.1 Flash-Lite: 31.0%<\/td><td>Terminal and agentic coding<\/td><\/tr><tr><td>OSWorld-gecertificeerd<\/td><td>74.0%<\/td><td>3 Flash: 65.1%<\/td><td>Computer interaction<\/td><\/tr><tr><td>GDPval-AA v2<\/td><td>1140<\/td><td>3.1 Flash-Lite: 642<\/td><td>Real-world knowledge work<\/td><\/tr><tr><td>GDM-MRCR v2<\/td><td>72.2%<\/td><td>3.1 Flash-Lite: 60.1%<\/td><td>Long-context retrieval<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 14px;margin:12px 0}summary{cursor:pointer;font-weight:700}.toc{background:#f8fafc;padding:18px 22px;border-radius:8px}.small{font-size:.92rem;color:#667085}.metric-grid{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px;margin:22px 0}.metric{border:1px solid #dbe3f0;border-radius:8px;padding:16px;background:linear-gradient(145deg,#fff,#f7f9ff)}.metric strong{display:block;font-size:1.65rem;color:#1849a9;line-height:1.1}.metric span{font-size:.82rem;color:#667085}.chart{border:1px solid #d0d5dd;border-radius:8px;padding:20px;margin:22px 0;background:#fff}.bar-row{display:grid;grid-template-columns:170px 1fr 60px;gap:10px;align-items:center;margin:13px 0}.bar-track{height:14px;background:#eef2f6;border-radius:4px;overflow:hidden}.bar-fill{height:100%;background:linear-gradient(90deg,#155eef,#7f56d9)}.bar-fill.warn{background:linear-gradient(90deg,#f79009,#fdb022)}.test-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px;margin:22px 0}.test-card{border:1px solid #d0d5dd;border-radius:8px;overflow:hidden;background:#fff}.test-head{display:flex;justify-content:space-between;gap:12px;padding:16px 18px;background:#f8fafc;border-bottom:1px solid #e4e7ec}.score{font-size:1.45rem;font-weight:800;color:#067647}.score.warn{color:#b54708}.test-body{padding:16px 18px}.mini-stats{display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:0 0 14px}.mini-stats div{background:#f5f7fa;border-radius:6px;padding:9px}.mini-stats strong{display:block;font-size:1rem}.mini-stats span{font-size:.72rem;color:#667085}.check-list{margin:10px 0 0;padding-left:20px}.check-list li{margin:6px 0}.experience{background:#f9fafb;border-left:3px solid #98a2b3;padding:11px 13px;margin-top:14px}.test-card details.evidence-toggle{margin:15px 0 0;padding:0;border:0;border-top:1px solid #e4e7ec;border-radius:0}.test-card details.evidence-toggle summary{padding:12px 0 2px;color:#155eef;font-size:.85rem}.evidence-block{margin-top:10px;padding:12px 14px;background:#f5f7fa;border:1px solid #e4e7ec;border-radius:6px}.evidence-block strong{display:block;margin-bottom:6px;font-size:.76rem;color:#475467;text-transform:uppercase;letter-spacing:.04em}.evidence-block pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<div class=\"chart\" aria-label=\"Official benchmark improvement chart\">\n<strong>Official improvement over the comparison model<\/strong>\n<div class=\"bar-row\"><span>Terminal-Bench 2.1<\/span><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:100%\"><\/div><\/div><strong>+23.0<\/strong><\/div>\n<div class=\"bar-row\"><span>SWE-Bench Pro<\/span><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:20%\"><\/div><\/div><strong>+4.6<\/strong><\/div>\n<div class=\"bar-row\"><span>OSWorld-gecertificeerd<\/span><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:39%\"><\/div><\/div><strong>+8.9<\/strong><\/div>\n<div class=\"bar-row\"><span>GDM-MRCR v2<\/span><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:53%\"><\/div><\/div><strong>+12.1<\/strong><\/div>\n<p class=\"data-note\">Percentage-point gains. Terminal-Bench and GDM-MRCR compare with 3.1 Flash-Lite; SWE-Bench Pro and OSWorld compare with Gemini 3 Flash. Source: Google, July 21, 2026.<\/p>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/google-flash-lite-official-1024x576.webp\" alt=\"Google lists 350 output tokens per second and API pricing of $0.30 input and $2.50 output per million tokens.\" class=\"wp-image-16935\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/google-flash-lite-official-1024x576.webp 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/google-flash-lite-official-300x169.webp 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/google-flash-lite-official-768x432.webp 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/google-flash-lite-official-18x10.webp 18w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/google-flash-lite-official.webp 1265w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Independent data adds useful context. At the time of checking, Artificial Analysis gave <strong>Gemini 3.5 Flash-Lite<\/strong> an Intelligence Index score of 36 and measured 388.8 output tokens per second. Google&#8217;s launch post cited an earlier rounded figure of 350 tokens per second from the same organization. The change is a reminder that live benchmark pages can update as providers, samples and measurement windows change.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/artificial-analysis-summary-1024x576.webp\" alt=\"Artificial Analysis measured very high output speed, while noting that similarly priced peers can have lower output-token prices.\" class=\"wp-image-16936\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/artificial-analysis-summary-1024x576.webp 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/artificial-analysis-summary-300x169.webp 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/artificial-analysis-summary-768x432.webp 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/artificial-analysis-summary-1536x864.webp 1536w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/artificial-analysis-summary-18x10.webp 18w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/artificial-analysis-summary.webp 1600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">First-day community reactions were predictably mixed. One Reddit post called the model a step backward but offered little task-level evidence; another discussion highlighted the published long-context improvement. On X, AI developer Philipp Schmid focused on the 350-token-per-second result and estimated task cost. These posts are useful signals about what users care about\u2014speed, price and generation-to-generation quality\u2014but they are too early and anecdotal to outweigh controlled tests.<\/p>\n\n\n\n<h2 id=\"test-setup\" class=\"wp-block-heading\">How I Tested Gemini 3.5 Flash-Lite<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">I ran four single-model tasks on July 22, 2026, using the exact model ID <code>gemini-3.5-flash-lite<\/code> through a configured Broly gateway. Every task used the first valid output. I froze the prompts, correct answers, validators and retry rules before seeing the results. Only the long-document task triggered a pre-registered three-run stability check.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><th>Taken<\/th><td>Structured extraction, long-document briefing, localization and Python debugging<\/td><\/tr><tr><th>Scores<\/th><td>Objective checks against schemas, reference facts, constraints and executable tests<\/td><\/tr><tr><th>Timing<\/th><td>End-to-end Broly route latency, not Google-native output speed<\/td><\/tr><tr><th>Kosten<\/th><td>API-equivalent estimate using official token rates; not a GlobalGPT subscription charge<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 id=\"hands-on-tests\" class=\"wp-block-heading\">Gemini 3.5 Flash-Lite Hands-On Test Results<\/h2>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 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0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<div class=\"test-grid\" aria-label=\"Hands-on test summary\">\n<section class=\"test-card\"><div class=\"test-head\"><strong>Structured extraction<\/strong><span class=\"score\">10\/10<\/span><\/div><div class=\"test-body\"><div class=\"mini-stats\"><div><strong>3.298s<\/strong><span>route time<\/span><\/div><div><strong>347<\/strong><span>total tokens<\/span><\/div><div><strong>$0.00041<\/strong><span>API equivalent<\/span><\/div><\/div><span class=\"badge\">First-try pass<\/span><\/div><\/section>\n<section class=\"test-card\"><div class=\"test-head\"><strong>Long-document brief<\/strong><span class=\"score warn\">8\/10<\/span><\/div><div class=\"test-body\"><div class=\"mini-stats\"><div><strong>4.595s<\/strong><span>route time<\/span><\/div><div><strong>1,587<\/strong><span>total tokens<\/span><\/div><div><strong>$0.00172<\/strong><span>API equivalent<\/span><\/div><\/div><span class=\"badge warn\">Owner-field error<\/span><\/div><\/section>\n<section class=\"test-card\"><div class=\"test-head\"><strong>Localization<\/strong><span class=\"score\">10\/10<\/span><\/div><div class=\"test-body\"><div class=\"mini-stats\"><div><strong>3.524s<\/strong><span>route time<\/span><\/div><div><strong>410<\/strong><span>total tokens<\/span><\/div><div><strong>$0.00047<\/strong><span>API equivalent<\/span><\/div><\/div><span class=\"badge\">All constraints met<\/span><\/div><\/section>\n<section class=\"test-card\"><div class=\"test-head\"><strong>Python debuggen<\/strong><span class=\"score\">10\/10<\/span><\/div><div class=\"test-body\"><div class=\"mini-stats\"><div><strong>3.574s<\/strong><span>route time<\/span><\/div><div><strong>642<\/strong><span>total tokens<\/span><\/div><div><strong>$0.00111<\/strong><span>API equivalent<\/span><\/div><\/div><span class=\"badge\">All tests passed<\/span><\/div><\/section>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Test 1: Structured data extraction<\/h3>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 14px;margin:12px 0}summary{cursor:pointer;font-weight:700}.toc{background:#f8fafc;padding:18px 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1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<section class=\"test-card\"><div class=\"test-head\"><strong>Messy operations note \u2192 strict JSON<\/strong><span class=\"score\">10\/10<\/span><\/div><div class=\"test-body\">\n<div class=\"mini-stats\"><div><strong>9\/9<\/strong><span>required keys<\/span><\/div><div><strong>Valid<\/strong><span>JSON parse<\/span><\/div><div><strong>0<\/strong><span>invented fields<\/span><\/div><\/div>\n<ul class=\"check-list\"><li>Captured every stated value.<\/li><li>Kept the possible timeout explicitly unconfirmed.<\/li><li>Excluded the irrelevant renewal note.<\/li><li>Needed no repair prompt.<\/li><\/ul>\n<p class=\"experience\"><strong>Hands-on take:<\/strong> This was the cleanest result in the set. I could have passed the output directly to a parser, which is exactly what a high-throughput extraction model needs to do.<\/p>\n<details class=\"evidence-toggle\"><summary>View prompt and model output<\/summary>\n<div class=\"evidence-block\"><strong>Prompt<\/strong><pre>Convert these messy support notes into one JSON object with exactly these keys: customer, plan, issue, affected_users, first_reported, workaround, owner, next_update, unresolved. Use null when a value is not stated and return valid JSON only.\n\nNotes: Northstar Labs \u2014 Growth plan. CSV exports over 50,000 rows stall at 92%. 14 analysts are affected. Workaround: split exports below 20,000 rows. Priya owns the investigation. A queue timeout is possible but unconfirmed. Next update: Tuesday 16:00 UTC.<\/pre><p class=\"evidence-note\">Prompt excerpt: the irrelevant renewal-note sentence was omitted here for readability; it remained in the exact test prompt.<\/p><\/div>\n<div class=\"evidence-block\"><strong>Model output<\/strong><pre>{\n  \"customer\": \"Northstar Labs\",\n  \"plan\": \"Growth\",\n  \"issue\": \"CSV exports over 50,000 rows stall at 92%\",\n  \"affected_users\": 14,\n  \"first_reported\": \"Monday 09:15 UTC\",\n  \"workaround\": \"split exports into batches below 20,000 rows\",\n  \"owner\": \"Priya\",\n  \"next_update\": \"Tuesday 16:00 UTC\",\n  \"unresolved\": [\"possible queue timeout (not confirmed)\"]\n}<\/pre><\/div>\n<\/details>\n<\/div><\/section>\n\n\n\n<h3 class=\"wp-block-heading\">Test 2: Long-document understanding<\/h3>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 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pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<section class=\"test-card\"><div class=\"test-head\"><strong>Project packet \u2192 140-word executive brief<\/strong><span class=\"score warn\">8\/10<\/span><\/div><div class=\"test-body\">\n<div class=\"mini-stats\"><div><strong>6\/6<\/strong><span>core facts<\/span><\/div><div><strong>\u2264140<\/strong><span>word limit met<\/span><\/div><div><strong>1<\/strong><span>field error<\/span><\/div><\/div>\n<ul class=\"check-list\"><li>Retrieved both launch and privacy dates.<\/li><li>Preserved the 1.8% crash rate and 1.0% target.<\/li><li>Correctly rejected two unapproved proposals.<\/li><li><strong>Missed:<\/strong> wrote departments as owners instead of <code>Not assigned<\/code>.<\/li><\/ul>\n<p class=\"experience\"><strong>Hands-on take:<\/strong> The summary captured the decision-making story, but the owner mistake could send an action item to the wrong team. This is where \u201cmostly correct\u201d is not enough.<\/p>\n<details class=\"evidence-toggle\"><summary>View prompt excerpt and model output<\/summary>\n<div class=\"evidence-block\"><strong>Prompt excerpt<\/strong><pre>Turn the Project Atlas packet into exactly four sections: Executive Brief (maximum 140 words), Confirmed Decisions, Action Register, and Open Questions\/Risks. In the Action Register use Owner, Action, Due, Dependency, Status.\n\nUse the confirmed August 12 launch date with its August 5 privacy condition and the current 1.8% crash rate versus the 1.0% target. Do not convert the French proposal, $18,000 ceiling, September mention, or drafted email into approved commitments. Where the packet gives no owner or due date, write Not assigned.<\/pre><p class=\"evidence-note\">The production prompt included the complete eight-section project packet; this excerpt preserves the decisive instructions and facts.<\/p><\/div>\n<div class=\"evidence-block\"><strong>Model output excerpt<\/strong><pre>Executive Brief: Project Atlas is scheduled for August 12, provided the privacy review closes by August 5. The mobile crash rate is 1.8% against a 1.0% release target. French localization was not approved, and the $18,000 testing ceiling is not permission to spend.\n\nAction Register excerpt:\n| Owner   | Action                                      | Due         |\n| Privacy | Complete remaining privacy review checks     | August 5    |\n| Legal   | Review the updated data-processing notice    | Not assigned |\n\nExpected owner value for both rows: Not assigned.<\/pre><p class=\"evidence-note\">The output preserved the central decisions but converted two organizational functions into owners, causing the 8\/10 score.<\/p><\/div>\n<\/details>\n<\/div><\/section>\n\n\n\n<p class=\"wp-block-paragraph\">I repeated this task twice under the frozen stability rule. The main facts stayed correct in all three runs, but the strict owner rule passed only once. Gemini 3.5 Flash-Lite therefore looks reliable for reading the narrative of a long packet, not for unsupervised extraction of every schema-sensitive field.<\/p>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid 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0}.bar-track{height:14px;background:#eef2f6;border-radius:4px;overflow:hidden}.bar-fill{height:100%;background:linear-gradient(90deg,#155eef,#7f56d9)}.bar-fill.warn{background:linear-gradient(90deg,#f79009,#fdb022)}.test-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px;margin:22px 0}.test-card{border:1px solid #d0d5dd;border-radius:8px;overflow:hidden;background:#fff}.test-head{display:flex;justify-content:space-between;gap:12px;padding:16px 18px;background:#f8fafc;border-bottom:1px solid #e4e7ec}.score{font-size:1.45rem;font-weight:800;color:#067647}.score.warn{color:#b54708}.test-body{padding:16px 18px}.mini-stats{display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:0 0 14px}.mini-stats div{background:#f5f7fa;border-radius:6px;padding:9px}.mini-stats strong{display:block;font-size:1rem}.mini-stats span{font-size:.72rem;color:#667085}.check-list{margin:10px 0 0;padding-left:20px}.check-list li{margin:6px 0}.experience{background:#f9fafb;border-left:3px solid #98a2b3;padding:11px 13px;margin-top:14px}.test-card details.evidence-toggle{margin:15px 0 0;padding:0;border:0;border-top:1px solid #e4e7ec;border-radius:0}.test-card details.evidence-toggle summary{padding:12px 0 2px;color:#155eef;font-size:.85rem}.evidence-block{margin-top:10px;padding:12px 14px;background:#f5f7fa;border:1px solid #e4e7ec;border-radius:6px}.evidence-block strong{display:block;margin-bottom:6px;font-size:.76rem;color:#475467;text-transform:uppercase;letter-spacing:.04em}.evidence-block pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<div class=\"chart\" aria-label=\"Long-document stability chart\"><strong>Three-run stability check<\/strong>\n<div class=\"bar-row\"><span>Ronde 1<\/span><div class=\"bar-track\"><div class=\"bar-fill warn\" style=\"width:80%\"><\/div><\/div><strong>8\/10<\/strong><\/div>\n<div class=\"bar-row\"><span>Ronde 2<\/span><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:100%\"><\/div><\/div><strong>10\/10<\/strong><\/div>\n<div class=\"bar-row\"><span>Ronde 3<\/span><div class=\"bar-track\"><div class=\"bar-fill warn\" style=\"width:80%\"><\/div><\/div><strong>8\/10<\/strong><\/div>\n<ul class=\"check-list\"><li>Core facts correct: <strong>3\/3 runs<\/strong><\/li><li>Exact owner rule correct: <strong>1\/3 runs<\/strong><\/li><\/ul><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Test 3: Translation and localization<\/h3>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 14px;margin:12px 0}summary{cursor:pointer;font-weight:700}.toc{background:#f8fafc;padding:18px 22px;border-radius:8px}.small{font-size:.92rem;color:#667085}.metric-grid{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px;margin:22px 0}.metric{border:1px solid #dbe3f0;border-radius:8px;padding:16px;background:linear-gradient(145deg,#fff,#f7f9ff)}.metric strong{display:block;font-size:1.65rem;color:#1849a9;line-height:1.1}.metric span{font-size:.82rem;color:#667085}.chart{border:1px solid #d0d5dd;border-radius:8px;padding:20px;margin:22px 0;background:#fff}.bar-row{display:grid;grid-template-columns:170px 1fr 60px;gap:10px;align-items:center;margin:13px 0}.bar-track{height:14px;background:#eef2f6;border-radius:4px;overflow:hidden}.bar-fill{height:100%;background:linear-gradient(90deg,#155eef,#7f56d9)}.bar-fill.warn{background:linear-gradient(90deg,#f79009,#fdb022)}.test-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px;margin:22px 0}.test-card{border:1px solid #d0d5dd;border-radius:8px;overflow:hidden;background:#fff}.test-head{display:flex;justify-content:space-between;gap:12px;padding:16px 18px;background:#f8fafc;border-bottom:1px solid #e4e7ec}.score{font-size:1.45rem;font-weight:800;color:#067647}.score.warn{color:#b54708}.test-body{padding:16px 18px}.mini-stats{display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:0 0 14px}.mini-stats div{background:#f5f7fa;border-radius:6px;padding:9px}.mini-stats strong{display:block;font-size:1rem}.mini-stats span{font-size:.72rem;color:#667085}.check-list{margin:10px 0 0;padding-left:20px}.check-list li{margin:6px 0}.experience{background:#f9fafb;border-left:3px solid #98a2b3;padding:11px 13px;margin-top:14px}.test-card details.evidence-toggle{margin:15px 0 0;padding:0;border:0;border-top:1px solid #e4e7ec;border-radius:0}.test-card details.evidence-toggle summary{padding:12px 0 2px;color:#155eef;font-size:.85rem}.evidence-block{margin-top:10px;padding:12px 14px;background:#f5f7fa;border:1px solid #e4e7ec;border-radius:6px}.evidence-block strong{display:block;margin-bottom:6px;font-size:.76rem;color:#475467;text-transform:uppercase;letter-spacing:.04em}.evidence-block pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<section class=\"test-card\"><div class=\"test-head\"><strong>Product copy \u2192 constrained localization<\/strong><span class=\"score\">10\/10<\/span><\/div><div class=\"test-body\">\n<div class=\"mini-stats\"><div><strong>99<\/strong><span>words delivered<\/span><\/div><div><strong>4\/4<\/strong><span>numeric facts<\/span><\/div><div><strong>0<\/strong><span>banned terms<\/span><\/div><\/div>\n<ul class=\"check-list\"><li>Retained both required product names.<\/li><li>Added no unsupported promise.<\/li><li>Read naturally rather than literally translated.<\/li><\/ul>\n<p class=\"experience\"><strong>Hands-on take:<\/strong> The copy was usable after one small stylistic trim: the CTA repeated the model name. The hard constraints themselves were handled perfectly.<\/p>\n<details class=\"evidence-toggle\"><summary>View prompt and model output<\/summary>\n<div class=\"evidence-block\"><strong>Prompt<\/strong><pre>Localize the Chinese product update into natural US English for an in-app announcement. Use 90\u2013120 words and a headline of no more than eight words. Preserve GlobalGPT, Gemini 3.5 Flash-Lite, 350 tokens\/second, $0.30, $2.50, and 1 million tokens. Avoid \u201crevolutionary,\u201d \u201cgame-changing,\u201d \u201ccheapest,\u201d and \u201cbest.\u201d End with a calm one-sentence CTA.<\/pre><p class=\"evidence-note\">The full test also supplied the Chinese source paragraph describing availability, positioning, pricing, context size and best-fit workflows.<\/p><\/div>\n<div class=\"evidence-block\"><strong>Model output<\/strong><pre>Gemini 3.5 Flash-Lite Now Available\n\nGlobalGPT now supports Gemini 3.5 Flash-Lite, a model designed for tasks requiring low latency and high throughput. Officially rated at a generation speed of roughly 350 tokens\/second, it features a 1 million token context window.\n\nAPI pricing is set at $0.30 per 1 million input tokens and $2.50 per 1 million output tokens. This model is well-suited for translation, document processing, structured extraction, and multi-step workflows.\n\nYou can select this model in GlobalGPT today and use it alongside other leading models within the same platform.\n\nStart using Gemini 3.5 Flash-Lite in your workflow now.<\/pre><\/div>\n<\/details>\n<\/div><\/section>\n\n\n\n<h3 class=\"wp-block-heading\">Test 4: Python debugging<\/h3>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 14px;margin:12px 0}summary{cursor:pointer;font-weight:700}.toc{background:#f8fafc;padding:18px 22px;border-radius:8px}.small{font-size:.92rem;color:#667085}.metric-grid{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px;margin:22px 0}.metric{border:1px solid #dbe3f0;border-radius:8px;padding:16px;background:linear-gradient(145deg,#fff,#f7f9ff)}.metric strong{display:block;font-size:1.65rem;color:#1849a9;line-height:1.1}.metric span{font-size:.82rem;color:#667085}.chart{border:1px solid #d0d5dd;border-radius:8px;padding:20px;margin:22px 0;background:#fff}.bar-row{display:grid;grid-template-columns:170px 1fr 60px;gap:10px;align-items:center;margin:13px 0}.bar-track{height:14px;background:#eef2f6;border-radius:4px;overflow:hidden}.bar-fill{height:100%;background:linear-gradient(90deg,#155eef,#7f56d9)}.bar-fill.warn{background:linear-gradient(90deg,#f79009,#fdb022)}.test-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px;margin:22px 0}.test-card{border:1px solid #d0d5dd;border-radius:8px;overflow:hidden;background:#fff}.test-head{display:flex;justify-content:space-between;gap:12px;padding:16px 18px;background:#f8fafc;border-bottom:1px solid #e4e7ec}.score{font-size:1.45rem;font-weight:800;color:#067647}.score.warn{color:#b54708}.test-body{padding:16px 18px}.mini-stats{display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:0 0 14px}.mini-stats div{background:#f5f7fa;border-radius:6px;padding:9px}.mini-stats strong{display:block;font-size:1rem}.mini-stats span{font-size:.72rem;color:#667085}.check-list{margin:10px 0 0;padding-left:20px}.check-list li{margin:6px 0}.experience{background:#f9fafb;border-left:3px solid #98a2b3;padding:11px 13px;margin-top:14px}.test-card details.evidence-toggle{margin:15px 0 0;padding:0;border:0;border-top:1px solid #e4e7ec;border-radius:0}.test-card details.evidence-toggle summary{padding:12px 0 2px;color:#155eef;font-size:.85rem}.evidence-block{margin-top:10px;padding:12px 14px;background:#f5f7fa;border:1px solid #e4e7ec;border-radius:6px}.evidence-block strong{display:block;margin-bottom:6px;font-size:.76rem;color:#475467;text-transform:uppercase;letter-spacing:.04em}.evidence-block pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<section class=\"test-card\"><div class=\"test-head\"><strong>Buggy pricing function \u2192 tested fix<\/strong><span class=\"score\">10\/10<\/span><\/div><div class=\"test-body\">\n<div class=\"mini-stats\"><div><strong>2\/2<\/strong><span>bugs identified<\/span><\/div><div><strong>5\/5<\/strong><span>required asserts<\/span><\/div><div><strong>4\/4<\/strong><span>invalid types rejected<\/span><\/div><\/div>\n<ul class=\"check-list\"><li>Stopped Python booleans passing as integers.<\/li><li>Applied the discount only above the threshold.<\/li><li>Returned all four expected totals.<\/li><\/ul>\n<p class=\"experience\"><strong>Hands-on take:<\/strong> The fix ran immediately and the explanation was short enough to review quickly. That supports bounded debugging, not autonomous repository work.<\/p>\n<details class=\"evidence-toggle\"><summary>View prompt and model output<\/summary>\n<div class=\"evidence-block\"><strong>Prompt<\/strong><pre>Debug this function. The first 100 units cost $0.10 each; every unit above 100 costs $0.07. Quantity must be a non-negative integer. The function currently charges the wrong amount above 100 and accepts booleans. Provide a two-sentence diagnosis, corrected Python, and five asserts for 0, 100, 101, 250, and invalid input.\n\ndef calculate_cost(quantity):\n    if not isinstance(quantity, int) or quantity &lt; 0:\n        raise ValueError(...)\n    if quantity &lt;= 100:\n        return round(quantity * 0.10, 2)\n    return round(quantity * 0.07, 2)<\/pre><\/div>\n<div class=\"evidence-block\"><strong>Model output excerpt<\/strong><pre>def calculate_cost(quantity):\n    if type(quantity) is not int or quantity &lt; 0:\n        raise ValueError('quantity must be a non-negative integer')\n    if quantity &lt;= 100:\n        total = quantity * 0.10\n    else:\n        total = (100 * 0.10) + ((quantity - 100) * 0.07)\n    return round(total, 2)\n\nassert calculate_cost(0) == 0.00\nassert calculate_cost(100) == 10.00\nassert calculate_cost(101) == 10.07\nassert calculate_cost(250) == 20.50<\/pre><p class=\"evidence-note\">The full output also included the requested boolean-rejection test and a two-sentence diagnosis of both bugs.<\/p><\/div>\n<\/details>\n<\/div><\/section>\n\n\n\n<h2 id=\"speed-cost\" class=\"wp-block-heading\">Gemini 3.5 Flash-Lite Speed and Test Cost<\/h2>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid 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0}.bar-track{height:14px;background:#eef2f6;border-radius:4px;overflow:hidden}.bar-fill{height:100%;background:linear-gradient(90deg,#155eef,#7f56d9)}.bar-fill.warn{background:linear-gradient(90deg,#f79009,#fdb022)}.test-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px;margin:22px 0}.test-card{border:1px solid #d0d5dd;border-radius:8px;overflow:hidden;background:#fff}.test-head{display:flex;justify-content:space-between;gap:12px;padding:16px 18px;background:#f8fafc;border-bottom:1px solid #e4e7ec}.score{font-size:1.45rem;font-weight:800;color:#067647}.score.warn{color:#b54708}.test-body{padding:16px 18px}.mini-stats{display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:0 0 14px}.mini-stats div{background:#f5f7fa;border-radius:6px;padding:9px}.mini-stats strong{display:block;font-size:1rem}.mini-stats span{font-size:.72rem;color:#667085}.check-list{margin:10px 0 0;padding-left:20px}.check-list li{margin:6px 0}.experience{background:#f9fafb;border-left:3px solid #98a2b3;padding:11px 13px;margin-top:14px}.test-card details.evidence-toggle{margin:15px 0 0;padding:0;border:0;border-top:1px solid #e4e7ec;border-radius:0}.test-card details.evidence-toggle summary{padding:12px 0 2px;color:#155eef;font-size:.85rem}.evidence-block{margin-top:10px;padding:12px 14px;background:#f5f7fa;border:1px solid #e4e7ec;border-radius:6px}.evidence-block strong{display:block;margin-bottom:6px;font-size:.76rem;color:#475467;text-transform:uppercase;letter-spacing:.04em}.evidence-block pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<div class=\"metric-grid\"><div class=\"metric\"><strong>3.298s<\/strong><span>fastest routed task<\/span><\/div><div class=\"metric\"><strong>4.595s<\/strong><span>slowest routed task<\/span><\/div><div class=\"metric\"><strong>2,986<\/strong><span>combined tokens<\/span><\/div><div class=\"metric\"><strong>$0.0037<\/strong><span>combined API equivalent<\/span><\/div><\/div>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 14px;margin:12px 0}summary{cursor:pointer;font-weight:700}.toc{background:#f8fafc;padding:18px 22px;border-radius:8px}.small{font-size:.92rem;color:#667085}.metric-grid{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px;margin:22px 0}.metric{border:1px solid #dbe3f0;border-radius:8px;padding:16px;background:linear-gradient(145deg,#fff,#f7f9ff)}.metric strong{display:block;font-size:1.65rem;color:#1849a9;line-height:1.1}.metric span{font-size:.82rem;color:#667085}.chart{border:1px solid #d0d5dd;border-radius:8px;padding:20px;margin:22px 0;background:#fff}.bar-row{display:grid;grid-template-columns:170px 1fr 60px;gap:10px;align-items:center;margin:13px 0}.bar-track{height:14px;background:#eef2f6;border-radius:4px;overflow:hidden}.bar-fill{height:100%;background:linear-gradient(90deg,#155eef,#7f56d9)}.bar-fill.warn{background:linear-gradient(90deg,#f79009,#fdb022)}.test-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px;margin:22px 0}.test-card{border:1px solid #d0d5dd;border-radius:8px;overflow:hidden;background:#fff}.test-head{display:flex;justify-content:space-between;gap:12px;padding:16px 18px;background:#f8fafc;border-bottom:1px solid #e4e7ec}.score{font-size:1.45rem;font-weight:800;color:#067647}.score.warn{color:#b54708}.test-body{padding:16px 18px}.mini-stats{display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:0 0 14px}.mini-stats div{background:#f5f7fa;border-radius:6px;padding:9px}.mini-stats strong{display:block;font-size:1rem}.mini-stats span{font-size:.72rem;color:#667085}.check-list{margin:10px 0 0;padding-left:20px}.check-list li{margin:6px 0}.experience{background:#f9fafb;border-left:3px solid #98a2b3;padding:11px 13px;margin-top:14px}.test-card details.evidence-toggle{margin:15px 0 0;padding:0;border:0;border-top:1px solid #e4e7ec;border-radius:0}.test-card details.evidence-toggle summary{padding:12px 0 2px;color:#155eef;font-size:.85rem}.evidence-block{margin-top:10px;padding:12px 14px;background:#f5f7fa;border:1px solid #e4e7ec;border-radius:6px}.evidence-block strong{display:block;margin-bottom:6px;font-size:.76rem;color:#475467;text-transform:uppercase;letter-spacing:.04em}.evidence-block pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<div class=\"chart\" aria-label=\"End-to-end test latency chart\"><strong>End-to-end Broly route time<\/strong>\n<div class=\"bar-row\"><span>Structured extraction<\/span><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:72%\"><\/div><\/div><strong>3.30s<\/strong><\/div>\n<div class=\"bar-row\"><span>Long document<\/span><div class=\"bar-track\"><div class=\"bar-fill warn\" style=\"width:100%\"><\/div><\/div><strong>4.60s<\/strong><\/div>\n<div class=\"bar-row\"><span>Localization<\/span><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:77%\"><\/div><\/div><strong>3.52s<\/strong><\/div>\n<div class=\"bar-row\"><span>Python debuggen<\/span><div class=\"bar-track\"><div class=\"bar-fill\" style=\"width:78%\"><\/div><\/div><strong>3.57s<\/strong><\/div>\n<p class=\"data-note\">Request-to-response measurements include gateway, network and provider overhead. They are not Google-native output-token speed.<\/p><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Across the four first outputs, Gemini 3.5 Flash-Lite averaged 3.748 seconds. The tasks used 1,708 input and 1,278 output tokens. At the official reference rates, their combined API-equivalent cost was $0.0037074. This illustrates token economics; it is not a GlobalGPT subscription charge and does not include paid tool calls.<\/p>\n\n\n\n<h2 id=\"pricing\" class=\"wp-block-heading\">Gemini 3.5 Flash-Lite Pricing and Value<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Official Gemini API pricing<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Gebruik<\/th><th>Official rate<\/th><th>What drives the bill<\/th><\/tr><\/thead><tbody><tr><td>Invoertokens<\/td><td><strong>$0.30 \/ 1M<\/strong><\/td><td>Prompts, documents and other context sent to the model<\/td><\/tr><tr><td>Uitgangstokens<\/td><td><strong>$2.50 \/ 1M<\/strong><\/td><td>Answer tokens, including thinking tokens<\/td><\/tr><tr><td>Gereedschap<\/td><td>Verschilt<\/td><td>Search or other paid tool calls can add separate charges<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 14px;margin:12px 0}summary{cursor:pointer;font-weight:700}.toc{background:#f8fafc;padding:18px 22px;border-radius:8px}.small{font-size:.92rem;color:#667085}.metric-grid{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px;margin:22px 0}.metric{border:1px solid #dbe3f0;border-radius:8px;padding:16px;background:linear-gradient(145deg,#fff,#f7f9ff)}.metric strong{display:block;font-size:1.65rem;color:#1849a9;line-height:1.1}.metric span{font-size:.82rem;color:#667085}.chart{border:1px solid #d0d5dd;border-radius:8px;padding:20px;margin:22px 0;background:#fff}.bar-row{display:grid;grid-template-columns:170px 1fr 60px;gap:10px;align-items:center;margin:13px 0}.bar-track{height:14px;background:#eef2f6;border-radius:4px;overflow:hidden}.bar-fill{height:100%;background:linear-gradient(90deg,#155eef,#7f56d9)}.bar-fill.warn{background:linear-gradient(90deg,#f79009,#fdb022)}.test-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px;margin:22px 0}.test-card{border:1px solid #d0d5dd;border-radius:8px;overflow:hidden;background:#fff}.test-head{display:flex;justify-content:space-between;gap:12px;padding:16px 18px;background:#f8fafc;border-bottom:1px solid #e4e7ec}.score{font-size:1.45rem;font-weight:800;color:#067647}.score.warn{color:#b54708}.test-body{padding:16px 18px}.mini-stats{display:grid;grid-template-columns:repeat(3,1fr);gap:8px;margin:0 0 14px}.mini-stats div{background:#f5f7fa;border-radius:6px;padding:9px}.mini-stats strong{display:block;font-size:1rem}.mini-stats span{font-size:.72rem;color:#667085}.check-list{margin:10px 0 0;padding-left:20px}.check-list li{margin:6px 0}.experience{background:#f9fafb;border-left:3px solid #98a2b3;padding:11px 13px;margin-top:14px}.test-card details.evidence-toggle{margin:15px 0 0;padding:0;border:0;border-top:1px solid #e4e7ec;border-radius:0}.test-card details.evidence-toggle summary{padding:12px 0 2px;color:#155eef;font-size:.85rem}.evidence-block{margin-top:10px;padding:12px 14px;background:#f5f7fa;border:1px solid #e4e7ec;border-radius:6px}.evidence-block strong{display:block;margin-bottom:6px;font-size:.76rem;color:#475467;text-transform:uppercase;letter-spacing:.04em}.evidence-block pre{margin:0;white-space:pre-wrap;overflow-wrap:anywhere;font:12px\/1.55 ui-monospace,SFMono-Regular,Menlo,monospace;color:#344054}.evidence-note{margin:7px 0 0;font-size:.78rem;color:#667085}.price-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0}.price-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px}.price-card.featured{border:2px solid #e2b93b;background:#fffdf5}.price-card .price{font-size:2rem;font-weight:800;line-height:1}.price-card .old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}.badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}.badge.warn{background:#fffaeb;color:#b54708}.decision{display:grid;grid-template-columns:155px 1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<div class=\"chart\" aria-label=\"API cost examples\"><strong>What the API rates mean in practice<\/strong>\n<div class=\"decision\"><strong>Short extraction<\/strong><span>10K input + 1K output \u2248 <strong>$0.0055<\/strong><\/span><\/div>\n<div class=\"decision\"><strong>Large document<\/strong><span>100K input + 5K output \u2248 <strong>$0.0425<\/strong><\/span><\/div>\n<div class=\"decision\"><strong>1,000 short jobs<\/strong><span>10M input + 1M output \u2248 <strong>$5.50<\/strong><\/span><\/div>\n<p class=\"data-note\">Estimates exclude caching, search, other tools and platform fees.<\/p><\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Output is about 8.3 times more expensive per token than input.<\/li>\n\n\n\n<li>Strict formats and short answers can materially reduce high-volume costs.<\/li>\n\n\n\n<li>API billing fits developers who need metered programmatic access and detailed usage control.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Calling this model \u201ccheap\u201d also needs context. Artificial Analysis described its $2.50 output rate as expensive relative to an average of $0.87 among its selected comparable models, even while ranking the model near the top for speed. The value proposition is therefore speed plus useful intelligence, not the absolute lowest token price in the market.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Using Gemini 3.5 Flash-Lite with GlobalGPT subscription plans<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">GlobalGPT uses a subscription-and-credits model rather than exposing the Gemini API token bill directly. The prices below were visible on July 22, 2026 with annual billing selected.<\/p>\n\n\n\n<!-- Paste this entire block into one WordPress Custom HTML block. -->\n<style>\n.g35-plan-grid,.g35-plan-grid *{box-sizing:border-box}\n.g35-plan-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:22px 0;font:17px\/1.58 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033}\n.g35-plan-card{border:1px solid #d0d5dd;border-radius:8px;padding:18px;background:#fff}\n.g35-plan-card.is-featured{border:2px solid #e2b93b;background:#fffdf5}\n.g35-plan-badge{display:inline-block;padding:3px 8px;border-radius:999px;background:#ecfdf3;color:#067647;font-size:.76rem;font-weight:700}\n.g35-plan-price{margin:15px 0 5px}\n.g35-plan-price strong{font-size:2rem;line-height:1;color:#172033}\n.g35-plan-old{font-size:.9rem;color:#98a2b3;text-decoration:line-through}\n.g35-plan-credits{display:block;margin:16px 0 10px}\n.g35-plan-card ul{margin:10px 0 16px;padding-left:20px}\n.g35-plan-card li{margin:7px 0}\n.g35-plan-best{margin:14px 0 0}\n@media(max-width:760px){.g35-plan-grid{grid-template-columns:1fr}}\n<\/style>\n\n<div class=\"g35-plan-grid\" aria-label=\"GlobalGPT annual plans\">\n  <section class=\"g35-plan-card\">\n    <span class=\"g35-plan-badge\">Basis<\/span>\n    <p class=\"g35-plan-price\"><strong>$5.8<\/strong> \/maand<br><span class=\"g35-plan-old\">$11.9 monthly rate<\/span><\/p>\n    <strong class=\"g35-plan-credits\">144.000 studiepunten per jaar<\/strong>\n    <ul>\n      <li>Access to all chat models<\/li>\n      <li>Unikorn V7 (MJ-like), 10+ advanced image models and Image Editor<\/li>\n      <li>Access to advanced video models<\/li>\n      <li>Eleven Lab v3, Deep Research, AI Detector and ChatPDF<\/li>\n    <\/ul>\n    <p class=\"g35-plan-best\"><strong>Geschikt voor:<\/strong> occasional chat and lighter creative workflows that do not need Pro&#8217;s full image, video, audio and agent access.<\/p>\n  <\/section>\n\n  <section class=\"g35-plan-card is-featured\">\n    <span class=\"g35-plan-badge\">Pro \u00b7 46% off<\/span>\n    <p class=\"g35-plan-price\"><strong>$10.8<\/strong> \/maand<br><span class=\"g35-plan-old\">$19.9 monthly rate<\/span><\/p>\n    <strong class=\"g35-plan-credits\">240.000 studiepunten per jaar<\/strong>\n    <ul>\n      <li>Access to all chat models<\/li>\n      <li>Perplexity Pro Search<\/li>\n      <li>Access to all AI image models and the full Image Editor<\/li>\n      <li>Access to 13 AI video models<\/li>\n      <li>Full access to AI audio models and agents<\/li>\n    <\/ul>\n    <p class=\"g35-plan-best\"><strong>Geschikt voor:<\/strong> regular multi-model and creative work.<\/p>\n  <\/section>\n\n  <section class=\"g35-plan-card\">\n    <span class=\"g35-plan-badge\">Unlimited \u00b7 50% off<\/span>\n    <p class=\"g35-plan-price\"><strong>$25.0<\/strong> \/maand<br><span class=\"g35-plan-old\">$49.9 monthly rate<\/span><\/p>\n    <strong class=\"g35-plan-credits\">624,000 credits\/year<\/strong>\n    <ul>\n      <li>Everything in Pro<\/li>\n      <li>Unlimited use on selected AI models<\/li>\n      <li>Premium models may still consume credits<\/li>\n    <\/ul>\n    <p class=\"g35-plan-best\"><strong>Geschikt voor:<\/strong> frequent users who want the broadest workflow and selected unlimited models.<\/p>\n  <\/section>\n<\/div>\n\n\n\n\n<h3 class=\"wp-block-heading\">Gemini 3.5 Flash-Lite API or GlobalGPT: which is better value?<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Kies<\/th><th>When it fits<\/th><th>Trade-off<\/th><\/tr><\/thead><tbody><tr><td><strong>Gemini API<\/strong><\/td><td>You are building an app, automating a pipeline or need exact usage logs.<\/td><td>Metered billing is efficient, but you manage integration, keys and other model providers yourself.<\/td><\/tr><tr><td><strong>GlobalGPT Basis<\/strong><\/td><td>You want inexpensive access for occasional chat and lighter AI workflows.<\/td><td>The credit pool is smaller.<\/td><\/tr><tr><td><strong>GlobalGPT Pro<\/strong><\/td><td>You regularly use leading language models together with research, image, audio or agent tools.<\/td><td>Credits and per-tool costs still need monitoring.<\/td><\/tr><tr><td><strong>GlobalGPT Unlimited<\/strong><\/td><td>You use several model families frequently and value one workspace.<\/td><td>\u201cUnlimited\u201d applies only to selected models; premium models can still use credits.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">For most individual reviewers and content teams, Pro is the most balanced GlobalGPT plan: it keeps the multi-model workflow while avoiding the higher Unlimited price. Developers calling Gemini 3.5 Flash-Lite thousands of times from a product will usually prefer the official API because token-level billing and automation matter more than a shared visual workspace. Check the <a href=\"https:\/\/www.glbgpt.com\/order?inviter=hub_blog_top_pricing&amp;login=1\">latest plan details<\/a> before paying because promotions, credit allocations and model access can change.<\/p>\n\n\n\n<h2 id=\"pros-cons\" class=\"wp-block-heading\">Pros and Cons<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Voordelen<\/th><th>Nadelen<\/th><\/tr><\/thead><tbody><tr><td><ul><li>Extremely high measured output speed<\/li><li>1M-token input context<\/li><li>Multimodal input and structured output<\/li><li>Strong first-output test performance<\/li><li>Low cost for short, repeated tasks<\/li><\/ul><\/td><td><ul><li>Text output only<\/li><li>Exact long-document fields can still drift<\/li><li>Output price is not the market&#8217;s lowest<\/li><li>Computer Use documentation is inconsistent<\/li><li>Route latency varies by provider and platform<\/li><\/ul><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 id=\"who-should-use\" class=\"wp-block-heading\">Who Should Use Gemini 3.5 Flash-Lite?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Gemini 3.5 Flash-Lite<\/strong> makes the most sense for teams processing many documents, support records, translations or structured messages; developers assigning bounded sub-tasks to an agent; and products where latency is visible to the user. It is less compelling when a task needs the deepest available reasoning, native media generation or zero-tolerance extraction without validation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The safest operating pattern is simple: use the model for the fast first pass, add objective schema and business-rule checks, and hand failed or high-risk cases to a stronger model or human reviewer. GlobalGPT fits that pattern by keeping leading models and the wider research, writing, coding and creative workflow in one place.<\/p>\n\n\n\n<h2 id=\"verdict\" class=\"wp-block-heading\">Final Verdict: Is Gemini 3.5 Flash-Lite Worth Using?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Yes\u2014when speed, throughput and controllable cost matter more than squeezing maximum reasoning depth from every call. In my tests, <strong>Gemini 3.5 Flash-Lite<\/strong> was excellent at JSON extraction, constrained localization and a bounded debugging task. It also understood the central facts in a long packet, but the owner-field error repeated in two of three runs. That is a useful boundary, not a deal-breaker.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The model is best treated as an efficient worker with validation around important fields. To judge it fairly, try a prompt from your own workflow rather than a trivia question.<\/p>\n\n\n\n<style>\nbody{font:17px\/1.68 system-ui,-apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif;color:#172033;max-width:980px;margin:40px auto;padding:0 22px}h1,h2,h3{line-height:1.22;color:#101828}h1{font-size:2.35rem}h2{margin-top:2.6rem;font-size:1.7rem}h3{margin-top:1.8rem;font-size:1.22rem}a{color:#155eef}table{width:100%;border-collapse:collapse;margin:18px 0 26px;display:block;overflow-x:auto}th,td{border:1px solid #d0d5dd;padding:10px 12px;text-align:left;vertical-align:top}th{background:#f2f4f7}.callout{border-left:4px solid #155eef;background:#f5f8ff;padding:18px 20px;margin:22px 0}.good{color:#067647;font-weight:700}.watch{color:#b54708;font-weight:700}figure{margin:28px 0}figure img{max-width:100%;height:auto;border:1px solid #e4e7ec;border-radius:8px}figcaption{font-size:.9rem;color:#667085;margin-top:8px}details{border:1px solid #d0d5dd;border-radius:6px;padding:12px 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1fr;gap:10px;border-top:1px solid #e4e7ec;padding:10px 0}.decision:first-child{border-top:0}.data-note{font-size:.84rem;color:#667085;margin-top:10px}@media(max-width:760px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:120px 1fr 52px}}@media(max-width:520px){.metric-grid,.test-grid,.price-grid{grid-template-columns:1fr}.mini-stats{grid-template-columns:1fr 1fr}.bar-row{grid-template-columns:1fr}.bar-row strong{text-align:left}}\n.cta-box{margin:24px 0;padding:22px;border:1px solid #b2ccff;border-radius:10px;background:linear-gradient(135deg,#f5f8ff,#f9f5ff)}.cta-box strong{display:block;font-size:1.18rem;color:#101828}.cta-box p{margin:7px 0 15px}.cta-actions{display:flex;flex-wrap:wrap;gap:10px}.cta-button{display:inline-block;padding:10px 16px;border-radius:7px;background:#155eef;color:#fff;text-decoration:none;font-weight:700}.cta-button.secondary{background:#fff;color:#155eef;border:1px solid #84adff}.cta-link{font-weight:700}\n<\/style>\n<div class=\"cta-box\"><strong>Use Gemini 3.5 Flash-Lite without breaking up your workflow<\/strong><p>Open it in GlobalGPT for fast document processing, localization and structured tasks, then move into research, writing, coding or content creation with other leading models and tools in the same workspace.<\/p><div class=\"cta-actions\"><a class=\"cta-button\" href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_content_home&amp;login=1\">Try Gemini 3.5 Flash-Lite<\/a><a class=\"cta-button secondary\" href=\"https:\/\/www.glbgpt.com\/order?inviter=hub_blog_top_pricing&amp;login=1\">View GlobalGPT plans<\/a><\/div><\/div>\n\n\n\n<h2 id=\"faq\" class=\"wp-block-heading\">Veelgestelde vragen<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">How much does Gemini 3.5 Flash-Lite cost?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The official reference price is $0.30 per million input tokens and $2.50 per million output tokens, including thinking tokens. Tool calls can create additional charges. These API rates are separate from third-party platform subscriptions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How fast is it?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Google&#8217;s launch post cited 350 output tokens per second from Artificial Analysis. The live Artificial Analysis model page showed 388.8 tokens per second when checked on July 22, 2026. Actual end-to-end time depends on prompt size, output length, thinking level, network and provider route.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is Gemini 3.5 Flash-Lite better than 3.1 Flash-Lite?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Google reports large gains on Terminal-Bench 2.1, GDM-MRCR v2 and GDPval-AA v2. That supports a clear generational improvement, although workload-specific testing is still necessary.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Does it accept images, audio, video and PDFs?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. The official reference lists text, image, video, audio and PDF inputs. The model outputs text and does not natively generate images, audio or video.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is \u201cGemini 3.5 Lite\u201d the correct name?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No. The official name is <strong>Gemini 3.5 Flash-Lite<\/strong>. \u201cGemini 3.5 Lite\u201d is an informal abbreviation that can be confused with other Flash-Lite generations.<\/p>\n\n\n\n<p class=\"small wp-block-paragraph\"><strong>Sources:<\/strong> <a href=\"https:\/\/blog.google\/innovation-and-ai\/models-and-research\/gemini-models\/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber\/\">Google launch announcement<\/a>; <a href=\"https:\/\/ai.google.dev\/gemini-api\/docs\/models\/gemini-3.5-flash-lite\">Gemini API model reference<\/a>; <a href=\"https:\/\/ai.google.dev\/gemini-api\/docs\/pricing\">Prijzen voor de Gemini API<\/a>; <a href=\"https:\/\/artificialanalysis.ai\/models\/gemini-3-5-flash-lite\">Kunstmatige analyse<\/a>; <a href=\"https:\/\/www.reddit.com\/r\/GeminiAI\/comments\/1v2m26s\/gemini_35_flash_lite_is_a_step_backwards_in_every\/\">Reddit discussion<\/a>; <a href=\"https:\/\/x.com\/_philschmid\/status\/2079640419285995550\">X commentary<\/a>. Official benchmark figures are vendor-reported; hands-on figures come from the controlled July 22 test package described above.<\/p>","protected":false},"excerpt":{"rendered":"<p>Is Gemini 3.5 Flash-Lite worth using? Yes\u2014especially for fast, repeatable work such as document processing, structured extraction, translation and bounded coding tasks. It combines a 1-million-token input window with low API pricing and some of the fastest measured output speeds in its class. In my four controlled tests, Gemini 3.5 Flash-Lite completed every task on [&hellip;]<\/p>","protected":false},"author":7,"featured_media":16952,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"Gemini 3.5 Flash-Lite Review: Pricing, Tests & Benchmarks","_seopress_titles_desc":"Our Gemini 3.5 Flash-Lite review covers hands-on tests, speed, pricing, benchmarks and real-world performance. See where it works best and try it on GlobalGPT.","_seopress_robots_index":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-16929","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-chat"],"_links":{"self":[{"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/posts\/16929","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/comments?post=16929"}],"version-history":[{"count":1,"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/posts\/16929\/revisions"}],"predecessor-version":[{"id":16953,"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/posts\/16929\/revisions\/16953"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/media\/16952"}],"wp:attachment":[{"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/media?parent=16929"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/categories?post=16929"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/nl\/wp-json\/wp\/v2\/tags?post=16929"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}