{"id":19108,"date":"2026-09-10T03:17:19","date_gmt":"2026-09-10T07:17:19","guid":{"rendered":"https:\/\/wp.glbgpt.com\/?p=19108"},"modified":"2026-09-10T03:32:53","modified_gmt":"2026-09-10T07:32:53","slug":"gpt-image-2-5-vs-nano-banana-2-5","status":"publish","type":"post","link":"https:\/\/wp.glbgpt.com\/ru\/hub\/gpt-image-2-5-vs-nano-banana-2-5","title":{"rendered":"\u0421\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435 GPT Image 2.5 \u0438 Nano Banana: \u0442\u0435\u0441\u0442\u044b \u0441 \u043e\u0434\u0438\u043d\u0430\u043a\u043e\u0432\u044b\u043c\u0438 \u043f\u0440\u043e\u043c\u043f\u0442\u0430\u043c\u0438 \u0432 \u0441\u043e\u043e\u0442\u043d\u043e\u0448\u0435\u043d\u0438\u0438 2:5"},"content":{"rendered":"<p class=\"wp-block-paragraph\"><strong>GPT Image 2.5 handled our strict infographic brief better in the matched second pair.<\/strong> Nano Banana 2 produced readable posters and useful label-color edits. The better choice depends on the asset you need.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Infographics:<\/strong> GPT preserved the five-step sequence; Nano added unwanted content in the second pair.<\/li>\n\n\n\n<li><strong>Local edits:<\/strong> both changed the label color while retaining its wording in the matched first pair.<\/li>\n\n\n\n<li><strong>Product images:<\/strong> convincing materials from both, with camera-angle and lighting trade-offs.<\/li>\n<\/ul>\n\n\n\n<div aria-label=\"Test sample\" class=\"evidence-strip\" style=\"display:flex;flex-wrap:wrap;gap:18px;border-top:1px solid #d6dedd;border-bottom:1px solid #d6dedd;padding:18px 0;margin:24px 0;\"><div style=\"flex:1 1 120px;min-width:0;\"><strong style=\"display:block;font-size:30px;line-height:1.2;color:#11685d;\">21<\/strong><span style=\"display:block;font-size:14px;margin-top:5px;\">Original outputs<\/span><\/div><div style=\"flex:1 1 120px;min-width:0;\"><strong style=\"display:block;font-size:30px;line-height:1.2;color:#11685d;\">5<\/strong><span style=\"display:block;font-size:14px;margin-top:5px;\">Design briefs<\/span><\/div><div style=\"flex:1 1 120px;min-width:0;\"><strong style=\"display:block;font-size:30px;line-height:1.2;color:#11685d;\">5<\/strong><span style=\"display:block;font-size:14px;margin-top:5px;\">Native-size-matched pairs<\/span><\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Explore image creation and focused editing through <a href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_popup&amp;login=1\">GlobalGPT<\/a>, or compare the original outputs below against your own brief.<\/p>\n\n\n\n<nav aria-label=\"\u0421\u043e\u0434\u0435\u0440\u0436\u0430\u043d\u0438\u0435 \u0441\u0442\u0430\u0442\u044c\u0438\" style=\"margin:22px 0;padding-bottom:16px;border-bottom:1px solid #d6dedd\"><a href=\"#at-a-glance\" style=\"color:#11685d;text-decoration:underline;display:inline-block;margin:4px 14px 4px 0;font-size:14px;color:#11685d\">\u041a\u0440\u0430\u0442\u043a\u0438\u0439 \u043e\u0431\u0437\u043e\u0440<\/a><a href=\"#benchmarks\" style=\"color:#11685d;text-decoration:underline;display:inline-block;margin:4px 14px 4px 0;font-size:14px;color:#11685d\">Public benchmarks<\/a><a href=\"#same-prompt-tests\" style=\"color:#11685d;text-decoration:underline;display:inline-block;margin:4px 14px 4px 0;font-size:14px;color:#11685d\">Same-prompt tests<\/a><a href=\"#token-cost\" style=\"color:#11685d;text-decoration:underline;display:inline-block;margin:4px 14px 4px 0;font-size:14px;color:#11685d\">\u0426\u0435\u043d\u043e\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 API<\/a><a href=\"#quality-value\" style=\"color:#11685d;text-decoration:underline;display:inline-block;margin:4px 14px 4px 0;font-size:14px;color:#11685d\">Quality vs. value<\/a><a href=\"#faq\" style=\"color:#11685d;text-decoration:underline;display:inline-block;margin:4px 14px 4px 0;font-size:14px;color:#11685d\">\u0427\u0410\u0421\u0422\u041e \u0417\u0410\u0414\u0410\u0412\u0410\u0415\u041c\u042b\u0415 \u0412\u041e\u041f\u0420\u041e\u0421\u042b<\/a><\/nav>\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=\"540\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/image-6-1024x540.png\" class=\"wp-image-19115\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/image-6-1024x540.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/image-6-300x158.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/image-6-18x9.png 18w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/image-6-768x405.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/image-6-1536x810.png 1536w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/image-6.png 1812w\" 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 style=\"--wp--block-button--width: 50;\" class=\"wp-block-button has-custom-width wp-block-button__width wp-block-button__width-50\"><a class=\"wp-block-button__link has-white-color has-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/www.glbgpt.com\/image-generator\/gpt-image-2-5?inviter=hub_content_gptimage25&amp;login=1\" style=\"background:linear-gradient(135deg,rgb(252,185,0) 33%,rgb(255,105,0) 74%)\"><strong>Try GPT Image 2.5 on GlobalGPT Now!<\/strong><\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<h2 id=\"at-a-glance\" class=\"wp-block-heading\">GPT Image 2.5 vs Nano Banana 2 at a Glance<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>GPT Image 2.5:<\/strong> \u0414\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u044b OpenAI <a href=\"https:\/\/developers.openai.com\/api\/docs\/models\/gpt-image-2.5-sunburst\">\u00ab\u0421\u043e\u043b\u043d\u0435\u0447\u043d\u044b\u0439 \u043b\u0443\u0447\u00bb<\/a> for image capability and editing precision, and <a href=\"https:\/\/developers.openai.com\/api\/docs\/models\/gpt-image-2.5-flare\">\u0420\u0430\u043a\u0435\u0442\u0430<\/a> for fast everyday generation.<\/li>\n\n\n\n<li><strong>\u00ab\u041d\u0430\u043d\u043e-\u0431\u0430\u043d\u0430\u043d 2\u00bb:<\/strong> Google <a href=\"https:\/\/ai.google.dev\/gemini-api\/docs\/image-generation\">Gemini 3.1 Flash Image<\/a>, with generation, editing, reference inputs, and search grounding.<\/li>\n\n\n\n<li><strong>Names matter:<\/strong> Nano Banana Pro is a separate model. Our GPT tests use Broly\u2019s <code>gpt-image-2.5<\/code> alias; the Sunburst\/Flare mapping is unspecified.<\/li>\n<\/ul>\n\n\n\n<div class=\"table-wrap\" style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;border-collapse:collapse!important;border-spacing:0!important;border:1px solid #d6dedd!important;border-radius:0;overflow:hidden;background:#fff!important;font-size:15px;color:#242729!important\"><thead><tr><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">\u0425\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a\u0430<\/th><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">GPT Image 2.5 Sunburst<\/th><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">\u041d\u0430\u043d\u043e-\u0431\u0430\u043d\u0430\u043d 2<\/th><\/tr><\/thead><tbody>\n<tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Developer and exact API model<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">OpenAI; <code>gpt-image-2.5-sunburst<\/code><\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Google; <code>gemini-3.1-flash-image<\/code><\/td><\/tr>\n<tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Creation and editing<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Text-to-image, image editing, and inpainting<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Text-to-image, image editing, and reference-based generation<\/td><\/tr>\n<tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Size controls<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Common sizes include 1024 \u00d7 1024 and 1536 \u00d7 1024; custom dimensions follow pixel and aspect-ratio constraints<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">0.5K, 1K, 2K, and 4K resolution options<\/td><\/tr>\n<tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">\u0418\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u0441\u043f\u0440\u0430\u0432\u043a\u0438<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Supports image inputs for editing and reference tasks<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Google documents up to 14 references, with model-specific limits for objects and characters<\/td><\/tr>\n<tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">\u041f\u043e\u0438\u0441\u043a \u0437\u0430\u0437\u0435\u043c\u043b\u0435\u043d\u0438\u044f<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">No equivalent search feature assumed in the direct Image API comparison<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Google Web and Image Search grounding is documented; optional for the comparison<\/td><\/tr>\n<\/tbody><\/table><\/div>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">The table describes documented features, not test scores. <a href=\"https:\/\/developers.openai.com\/api\/docs\/guides\/image-generation#customize-image-output\">OpenAI size controls<\/a> \u0438 <a href=\"https:\/\/ai.google.dev\/gemini-api\/docs\/image-generation\">Google resolution options<\/a> differ; native dimensions are labeled on every test image.<\/p>\n\n\n\n<h2 id=\"benchmarks\" class=\"wp-block-heading\">Public Benchmarks: How Do They Compare?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Sunburst leads both cited Arena rankings.<\/strong> Its preliminary status and smaller vote sample are part of that result.<\/p>\n\n\n\n<figure class=\"data-figure\" id=\"arena-data\" style=\"margin:24px 0;padding:20px 0;border-top:1px solid #d6dedd;border-bottom:1px solid #d6dedd;color:#242729;font-family:Arial,Helvetica,sans-serif;line-height:1.6;\"><h3 style=\"font-size:19px;line-height:1.4;margin:0 0 6px;color:#242729;\">Arena: generation and editing<\/h3><p style=\"font-size:14px;margin:7px 0 16px;\">September 7, 2026 snapshot \u00b7 Scores on a 0\u20131,600 display scale<\/p><div class=\"chart-group\" style=\"margin-top:22px;\"><h3 style=\"font-size:19px;line-height:1.4;margin:0 0 6px;color:#242729;\"><a href=\"https:\/\/arena.ai\/leaderboard\/text-to-image\" style=\"color:#11685d;text-decoration:underline;\">\u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0442\u0435\u043a\u0441\u0442\u0430 \u0432 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0435<\/a><\/h3><div class=\"chart-row\" style=\"margin:18px 0;\"><div class=\"chart-label\" style=\"display:flex;justify-content:space-between;align-items:baseline;flex-wrap:wrap;gap:5px 16px;font-size:15px;\"><span>GPT Image 2.5 Sunburst \u00b7 #1<\/span><strong style=\"white-space:nowrap;\">1,421 \u00b1 13<\/strong><\/div><div aria-hidden=\"true\" class=\"chart-track\" style=\"height:14px;background:#edf0ef;margin:8px 0 5px;width:100%;\"><span class=\"chart-fill\" style=\"display:block;height:100%;background:#187b70;width:88.8125%\"><\/span><\/div><small style=\"display:block;font-size:13px;color:#59616a;\">3,149 model votes \u00b7 Preliminary<\/small><\/div><div class=\"chart-row\" style=\"margin:18px 0;\"><div class=\"chart-label\" style=\"display:flex;justify-content:space-between;align-items:baseline;flex-wrap:wrap;gap:5px 16px;font-size:15px;\"><span>Nano Banana 2 [web-search] \u00b7 #9<\/span><strong style=\"white-space:nowrap;\">1,261 \u00b1 5<\/strong><\/div><div aria-hidden=\"true\" class=\"chart-track\" style=\"height:14px;background:#edf0ef;margin:8px 0 5px;width:100%;\"><span class=\"chart-fill nano\" style=\"background:#ce654c;display:block;height:100%;background:#187b70;width:78.8125%;background:#ce654c\"><\/span><\/div><small style=\"display:block;font-size:13px;color:#59616a;\">41,957 model votes<\/small><\/div><\/div><div class=\"chart-group\" style=\"margin-top:22px;\"><h3 style=\"font-size:19px;line-height:1.4;margin:0 0 6px;color:#242729;\"><a href=\"https:\/\/arena.ai\/leaderboard\/image-edit\" style=\"color:#11685d;text-decoration:underline;\">Single-image editing<\/a><\/h3><div class=\"chart-row\" style=\"margin:18px 0;\"><div class=\"chart-label\" style=\"display:flex;justify-content:space-between;align-items:baseline;flex-wrap:wrap;gap:5px 16px;font-size:15px;\"><span>GPT Image 2.5 Sunburst \u00b7 #1<\/span><strong style=\"white-space:nowrap;\">1,520 \u00b1 9<\/strong><\/div><div aria-hidden=\"true\" class=\"chart-track\" style=\"height:14px;background:#edf0ef;margin:8px 0 5px;width:100%;\"><span class=\"chart-fill\" style=\"display:block;height:100%;background:#187b70;width:95.0%\"><\/span><\/div><small style=\"display:block;font-size:13px;color:#59616a;\">6,704 model votes \u00b7 Preliminary<\/small><\/div><div class=\"chart-row\" style=\"margin:18px 0;\"><div class=\"chart-label\" style=\"display:flex;justify-content:space-between;align-items:baseline;flex-wrap:wrap;gap:5px 16px;font-size:15px;\"><span>Nano Banana 2 [web-search] \u00b7 #12<\/span><strong style=\"white-space:nowrap;\">1,387 \u00b1 4<\/strong><\/div><div aria-hidden=\"true\" class=\"chart-track\" style=\"height:14px;background:#edf0ef;margin:8px 0 5px;width:100%;\"><span class=\"chart-fill nano\" style=\"background:#ce654c;display:block;height:100%;background:#187b70;width:86.6875%;background:#ce654c\"><\/span><\/div><small style=\"display:block;font-size:13px;color:#59616a;\">157,693 model votes<\/small><\/div><\/div><figcaption style=\"font-size:13px;color:#59616a;margin-top:8px;\">Retrieved September 9, 2026. Separate category leaderboards; votes are model totals, not direct head-to-head counts. Uncertainty is reproduced as displayed. Score differences are not percentage improvements in image quality.<\/figcaption><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it measures:<\/strong> <a href=\"https:\/\/arena.ai\/how-it-works\">anonymous user preferences<\/a>, rather than exact spelling or cost per usable image.<\/li>\n\n\n\n<li><strong>Configuration:<\/strong> the Nano entry uses web search; our supplied-content tests did not request it.<\/li>\n\n\n\n<li><strong>\u041e\u0431\u043b\u0430\u0441\u0442\u044c \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u044f:<\/strong> Sunburst\u2019s rank does not identify the Broly alias tested below. Arena\u2019s <a href=\"https:\/\/arena.ai\/blog\/policy\/\">preliminary-result policy<\/a> also applies.<\/li>\n<\/ul>\n\n\n\n<h2 id=\"same-prompt-tests\" class=\"wp-block-heading\">Same-Prompt Tests: Five Real Design Tasks<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>\u041e\u0431\u044a\u0435\u043c \u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f:<\/strong> five briefs, identical prompts, and the same reference-image bytes. Tests ran through Broly on September 9, 2026.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Requested settings:<\/strong> 1024 \u00d7 1024, medium provider label, no search requested. \u201cMedium\u201d is not a verified equivalent quality tier across models.<\/li>\n\n\n\n<li><strong>Sample:<\/strong> 10 GPT and 11 Nano outputs, including a new two-model poster replacement pair. This is a small sample, not a reliability estimate.<\/li>\n\n\n\n<li><strong>Fair comparisons:<\/strong> five pairs returned 1024 \u00d7 1024 on both sides. Five GPT originals returned 1254 \u00d7 1254; those pairs are excluded from quality ranking.<\/li>\n\n\n\n<li><strong>\u0421\u0441\u044b\u043b\u043a\u0438:<\/strong> a Nano-generated bottle with locally added label text and a separately generated fictional adult. Identical inputs control the source, but do not remove source-model bias.<\/li>\n\n\n\n<li><strong>Judging criteria:<\/strong> wording, omissions, invented content, composition, edit preservation, and resemblance. Original PNGs are linked under the same WebP display treatment.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">1. Text-Heavy Posters and Small Print<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted\">Create a polished square poster for a fictional design event. White background, charcoal typography, cobalt blue and coral accents. Use crisp editorial typography and generous margins. Include exactly these text strings, with no additions or substitutions:\nDESIGN AFTER HOURS\nIdeas worth staying up for\nFRIDAY, OCTOBER 16\n6:30 PM - 9:00 PM\nNORTH HALL, STUDIO 08\nTALKS \/ DEMOS \/ OPEN CRITIQUE\nEARLY ENTRY $18\nReserve your seat at designafterhours.example\nMake the event name largest, the date and time easy to find, and the URL small but legible. Use abstract geometric shapes only. No logos, photos, extra text, or decorative fake lettering.<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pair 1: native dimensions differ; excluded from head-to-head ranking.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>New replacement pair: native dimensions differ; excluded from head-to-head ranking.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>No matched-size winner.<\/strong> GPT run 1 and the new GPT\/Nano replacement pair reproduce all eight required strings legibly. GPT uses a flatter geometric layout; Nano often presents a sheet with a shadow. These are useful design differences, but the GPT originals are 1254 pixels square, so we do not use small-print detail to rank them against 1024-pixel Nano outputs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The original second Nano attempt remains below: its wording is intact, but the time separator changes from the specified hyphen to a longer dash. The corresponding GPT run ended with an upstream 524 and no image. We kept the failure record and generated a new pair on both models, rather than replacing only the weaker or failed side.<\/p>\n\n\n\n\n\n<p class=\"wp-block-paragraph\">More layouts are covered in the <a href=\"https:\/\/www.glbgpt.com\/hub\/nano-banana-2-prompt-guide\/\">\u041e\u043f\u0435\u0440\u0430\u0442\u0438\u0432\u043d\u043e\u0435 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u043e \u043f\u043e Nano Banana 2<\/a>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Product Photography and Material Detail<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted\">Create a square commercial studio photograph of one unbranded cylindrical clear-glass perfume bottle with a brushed silver metal cap and a blank matte white rectangular paper label. The bottle contains pale green liquid. Place it upright on a pale gray stone surface against a soft white background. Use one large softbox on the left, a narrow rim reflection on the right, and a believable contact shadow. Eye-level three-quarter view, entire bottle visible, precise edges, realistic glass refraction, fine brushed-metal texture. No other objects, no lettering, no logos, no watermark-like graphics.<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pair 1: native dimensions match.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pair 2: native dimensions match.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>No overall product winner.<\/strong> Both pairs match at 1024 \u00d7 1024. GPT gives the glass edges, metal brushing, and stone texture a crisp treatment; Nano also separates glass, metal, and paper convincingly. Nano run 1 includes an unwanted dark diagonal in the background, while run 2 is cleaner.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Neither model consistently demonstrates the requested three-quarter angle: the bottles look largely frontal. GPT run 2 also places stronger light on the right and shadow toward the left, which weakens compliance with the specified left softbox. A premium-looking bottle is useful for concepts, but exact camera and lighting direction still need checking.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Precise Edits Without Changing the Rest<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted\">Edit the supplied product photograph. Change only the label's background color to a muted teal. Preserve every printed word and its exact spelling, font, size, placement, and color. Keep the bottle, cap, liquid, reflections, lighting, shadows, background, camera angle, crop, and image dimensions unchanged. Do not redesign the label, add objects, sharpen the whole image, or restyle the photograph.<\/pre>\n\n\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pair 1: native dimensions match.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pair 2: native dimensions differ; excluded from head-to-head ranking.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>No clear winner in the matched first pair.<\/strong> Both models turn the label muted teal and retain FIELD NOTES, EAU DE PARFUM, and 50 mL. The bottle, cap, crop, and broad composition remain close to the source. Neither result is certified pixel-identical outside the label.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The second GPT edit preserves the wording but returns 1254 \u00d7 1254 despite the instruction to keep image dimensions unchanged. That is a concrete dimension-compliance failure, not evidence of superior detail. Nano retains 1024 \u00d7 1024 and produces a lighter teal. An exact brand-color workflow would benefit from a supplied color reference.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u0421\u0430\u0439\u0442 <a href=\"https:\/\/www.glbgpt.com\/hub\/globalgpt-image-edit\/\">GlobalGPT Image Edit guide<\/a> explains how to specify a controlled change.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Character Consistency Across Scenes<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted\">Use the supplied fictional adult reference as the same person in both panels of one square image. Create two equal vertical panels with a clean narrow white divider. Left panel: the person standing outside a modern bookstore in daylight, waist-up, holding a closed notebook. Right panel: the same person seated at a cafe table in soft evening light, waist-up, with the notebook on the table. Preserve the reference face, hairline, hairstyle, glasses, jacket color, and shirt details in both panels. Keep a realistic photographic style. No captions, lettering, extra people, or changes of identity.<\/pre>\n\n\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pair 1: native dimensions differ; excluded from head-to-head ranking.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pair 2: native dimensions match.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>GPT follows the exclusions better in the matched second pair.<\/strong> Both retain the main reference features: short black hair, round glasses, a green jacket, and a white shirt. GPT shows the notebook held in the first panel and on the cafe table in the second, with no obvious storefront lettering or extra people. Nano adds shop text and background pedestrians despite explicit exclusions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is an instruction-following advantage in one matched pair, not a biometric identity score. GPT also introduces a visible shoulder bag in the bookstore panel, which the brief did not require. The first pair is shown for completeness but excluded from ranking because its native sizes differ. Both references originated with Nano, so the test does not eliminate source-model bias.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u0421\u0430\u0439\u0442 <a href=\"https:\/\/www.glbgpt.com\/hub\/nano-banana-2-subject-consistency\/\">subject consistency guide<\/a> covers the wider reference-image workflow.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">5. Infographics and Complex Layouts<\/h3>\n\n\n\n<pre class=\"wp-block-preformatted\">Create a square, professional infographic on a white background titled exactly \"FROM BRIEF TO LAUNCH\". Below it, arrange five equally sized steps in one clear left-to-right sequence, connected by four right-pointing arrows. Use charcoal text with teal and coral accents, simple familiar icons, and clean sans-serif typography. Render these exact step titles and supporting lines:\n1. DEFINE \/ Set one measurable goal\n2. RESEARCH \/ Check audience needs\n3. CREATE \/ Build the first draft\n4. REVIEW \/ Fix factual and visual errors\n5. LAUNCH \/ Publish and measure results\nInclude every word exactly once. The arrows must connect 1 to 2 to 3 to 4 to 5, with no loops or branches. Keep every supporting line readable. Do not add charts, percentages, extra steps, decorative text, or a footer.<\/pre>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pair 1: native dimensions differ; excluded from head-to-head ranking.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Pair 2: native dimensions match.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>GPT wins content compliance in the matched second pair.<\/strong> Its five numbered steps, supporting lines, and four connecting arrows form the requested sequence without an extra row. Nano includes the main sequence but then adds duplicate REVIEW and LAUNCH blocks and invented text, violating the instruction to include every word exactly once.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The first pair tells a more limited story: both models omit the required step numbers, and GPT returns 1254 \u00d7 1254. It cannot support a matched-size win. The useful finding is therefore specific: GPT solved the strict content brief in the second 1024-pixel pair; Nano required correction in both displayed attempts. Two attempts do not establish a general success rate.<\/p>\n\n\n\n<h2 id=\"token-cost\" class=\"wp-block-heading\">Official API Pricing and Recorded Tokens<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>GPT has the lower image-output token rate; Nano has the lower input rate.<\/strong> The rates below come from <a href=\"https:\/\/developers.openai.com\/api\/docs\/guides\/image-generation#cost-and-latency\">OpenAI\u2019s pricing documentation<\/a> \u0438 <a href=\"https:\/\/ai.google.dev\/gemini-api\/docs\/pricing#gemini-3.1-flash-image\">Google\u2019s API pricing<\/a>, checked September 9, 2026.<\/p>\n\n\n\n<figure class=\"data-figure\" id=\"output-rate-data\" style=\"margin:24px 0;padding:20px 0;border-top:1px solid #d6dedd;border-bottom:1px solid #d6dedd;color:#242729;font-family:Arial,Helvetica,sans-serif;line-height:1.6;\"><h3 style=\"font-size:19px;line-height:1.4;margin:0 0 6px;color:#242729;\">Official image-output token rate<\/h3><p style=\"font-size:14px;margin:7px 0 16px;\">USD per 1 million image output tokens \u00b7 Standard API rates<\/p><div class=\"chart-row\" style=\"margin:18px 0;\"><div class=\"chart-label\" style=\"display:flex;justify-content:space-between;align-items:baseline;flex-wrap:wrap;gap:5px 16px;font-size:15px;\"><span>GPT Image 2.5 Sunburst<\/span><strong style=\"white-space:nowrap;\">$30<\/strong><\/div><div aria-hidden=\"true\" class=\"chart-track\" style=\"height:14px;background:#edf0ef;margin:8px 0 5px;width:100%;\"><span class=\"chart-fill\" style=\"display:block;height:100%;background:#187b70;width:50%\"><\/span><\/div><\/div><div class=\"chart-row\" style=\"margin:18px 0;\"><div class=\"chart-label\" style=\"display:flex;justify-content:space-between;align-items:baseline;flex-wrap:wrap;gap:5px 16px;font-size:15px;\"><span>\u041d\u0430\u043d\u043e-\u0431\u0430\u043d\u0430\u043d 2<\/span><strong style=\"white-space:nowrap;\">$60<\/strong><\/div><div aria-hidden=\"true\" class=\"chart-track\" style=\"height:14px;background:#edf0ef;margin:8px 0 5px;width:100%;\"><span class=\"chart-fill nano\" style=\"background:#ce654c;display:block;height:100%;background:#187b70;width:100%;background:#ce654c\"><\/span><\/div><\/div><figcaption style=\"font-size:13px;color:#59616a;margin-top:8px;\">Token unit prices, not prices per image. Token consumption differs between models.<\/figcaption><\/figure>\n\n\n\n<div class=\"table-wrap\" style=\"overflow-x:auto;margin:20px 0\"><table class=\"usage-table\" style=\"width:100%;border-collapse:collapse!important;border-spacing:0!important;border:1px solid #d6dedd!important;border-radius:0;overflow:hidden;background:#fff!important;font-size:15px;color:#242729!important\"><thead><tr><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Per 1M tokens<\/th><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">GPT Image 2.5<\/th><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">\u041d\u0430\u043d\u043e-\u0431\u0430\u043d\u0430\u043d 2<\/th><\/tr><\/thead><tbody><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">\u0412\u0432\u043e\u0434 \u0442\u0435\u043a\u0441\u0442\u0430<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">$5.00<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">$0.50<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">\u0412\u0432\u043e\u0434 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">$8.00<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">$0.50<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">\u0412\u044b\u0432\u043e\u0434 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u044f<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">$30.00<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">$60.00<\/td><\/tr><\/tbody><\/table><\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Additional categories:<\/strong> OpenAI cached text\/image inputs are $1.25\/$2 per million tokens when applicable. Google text\/thinking output is $3 per million tokens.<\/li>\n\n\n\n<li><strong>Google\u2019s 1K example:<\/strong> 1,120 image output tokens \u00d7 $60 \/ 1M = <strong>$0.0672 for output alone<\/strong>, before input and any separately billed text\/thinking. This is a documentation example, not our test bill.<\/li>\n\n\n\n<li><strong>Compare complete jobs:<\/strong> resolution, quality, input references, and additional attempts determine cost per usable image. A lower token rate alone does not make one model cheaper.<\/li>\n<\/ul>\n\n\n\n<details class=\"usage-details\" style=\"margin:24px 0;padding:14px 0;border-top:1px solid #d6dedd;border-bottom:1px solid #d6dedd;\"><summary style=\"cursor:pointer;font-weight:700;\">Recorded GPT Image 2.5 token usage \u00b7 10 outputs<\/summary><p class=\"method-note\" style=\"font-size:14px;color:#59616a;\">Broly-reported input and image-output totals. These are usage records, not verified official billing units or a cross-model cost comparison.<\/p><div style=\"overflow-x:auto;margin:20px 0\"><table class=\"usage-table\" style=\"width:100%;border-collapse:collapse!important;border-spacing:0!important;border:1px solid #d6dedd!important;border-radius:0;overflow:hidden;background:#fff!important;font-size:15px;color:#242729!important\"><thead><tr><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Test \/ run<\/th><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">\u0412\u0432\u043e\u0434\u043d\u044b\u0435 \u0442\u043e\u043a\u0435\u043d\u044b<\/th><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Image output tokens<\/th><\/tr><\/thead><tbody><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Character \/ 01<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">238<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">765<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Character \/ 02<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">330<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">198<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Edit \/ 01<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">287<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">203<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Edit \/ 02<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">188<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">765<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Infographic \/ 01<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">200<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">765<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Infographic \/ 02<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">173<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">210<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Poster \/ 01<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">164<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">765<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Poster \/ 03<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">152<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">2058<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Product \/ 01<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">115<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">210<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Product \/ 02<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">115<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">198<\/td><\/tr><\/tbody><\/table><\/div><\/details>\n\n\n\n<h2 id=\"quality-value\" class=\"wp-block-heading\">What the Results Mean: Quality vs. Value<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Accurate text and accurate information design are different strengths.<\/strong> Both models rendered the event wording well, but neither the posters nor their small print can establish a size-matched winner. The infographic offers a firmer distinction: GPT&#8217;s second 1024-pixel output preserves the sequence and content, while Nano&#8217;s adds invented material. If the asset explains a process, those additions create more editing work than a minor layout preference.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Product photography is less one-sided. GPT&#8217;s crisp glass and metal can help a concept feel finished, while Nano&#8217;s cleaner second background is easier to use. Both missed the clearly requested camera angle, and GPT&#8217;s second lighting direction needs correction. For a specific product campaign, compliance with the shot brief should matter more than surface polish alone.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The narrow label edit was a practical success for both models in the first matched pair. Character continuity exposed a different trade-off: GPT avoided Nano&#8217;s unwanted shop text and people in the second pair, while adding a shoulder bag. Resemblance, preservation, and exclusion compliance should be checked separately. A recognizable person does not automatically make the whole composition usable.<\/p>\n\n\n\n<div class=\"table-wrap\" style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;border-collapse:collapse!important;border-spacing:0!important;border:1px solid #d6dedd!important;border-radius:0;overflow:hidden;background:#fff!important;font-size:15px;color:#242729!important\"><thead><tr><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">\u0417\u0430\u0434\u0430\u043d\u0438\u0435<\/th><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Evidence-based conclusion<\/th><th style=\"background:#edf3f2!important;color:#242729!important;font-weight:700!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Value implication<\/th><\/tr><\/thead><tbody><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Dense poster text<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Both readable; native-size comparison not assigned<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Do not infer small-print superiority from the larger output<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Five-step infographic<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">GPT better in matched pair 2; both omit numbers in unmatched pair 1<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Nano needs content correction; no measured dollar ranking<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Product rendering<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Trade-offs; camera-angle brief not consistently satisfied<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Count revision work before choosing on appearance<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Label edit<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">No clear winner in matched pair 1<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:1px solid #d6dedd!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Both useful for this specific color change<\/td><\/tr><tr><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Character scenes<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">GPT better on exclusions in matched pair 2<\/td><td style=\"background:#fff!important;color:#242729!important;border:0!important;border-bottom:0!important;padding:12px!important;text-align:left!important;vertical-align:top!important\">Fewer observed unwanted details, not a general identity score<\/td><\/tr><\/tbody><\/table><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Value depends on the finished asset.<\/strong> GPT\u2019s cleaner second infographic could reduce content correction; both models produced useful label edits. Official token rates add pricing context, but these results do not establish a measured cost-per-usable-image winner.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>\u0427\u0442\u043e \u0432\u044b\u0431\u0440\u0430\u0442\u044c?<\/strong> Start with the tested GPT route for this strict infographic brief; either model is a reasonable candidate for the narrow label edit. For posters and product shots, choose against your exact layout and lighting requirements. These findings do not establish a universal quality or price winner.<\/p>\n\n\n\n<h2 id=\"faq\" class=\"wp-block-heading\">\u0427\u0430\u0441\u0442\u043e \u0437\u0430\u0434\u0430\u0432\u0430\u0435\u043c\u044b\u0435 \u0432\u043e\u043f\u0440\u043e\u0441\u044b<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Is GPT Image 2.5 better than Nano Banana 2?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In our matched second infographic pair, GPT followed the content requirements better. It also avoided unwanted lettering and people in the matched second character pair. Product and label-edit results do not support a clear overall winner.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Which GPT Image 2.5 version was tested?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">We tested Broly&#8217;s gpt-image-2.5 alias. The route returned images successfully but did not disclose a Sunburst or Flare mapping. The Sunburst leaderboard is separate context, not verified identification of the tested model.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Were the two models tested with identical settings?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The prompts, 1024 \u00d7 1024 target, medium provider label, and reference-image bytes matched. Five pairs also match at native 1024 \u00d7 1024. Five GPT results returned 1254 \u00d7 1254; they are excluded from paired ranking. The medium label is not a verified equivalent quality tier.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u041c\u043e\u0434\u0435\u043b\u044c Nano Banana 2 \u2014 \u044d\u0442\u043e \u0442\u043e \u0436\u0435 \u0441\u0430\u043c\u043e\u0435, \u0447\u0442\u043e \u0438 Nano Banana Pro?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No. Google identifies Nano Banana 2 as Gemini 3.1 Flash Image. Nano Banana Pro is Gemini 3 Pro Image; they are separate models.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Which model is better for text-heavy infographics?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">GPT performed better in the second native-size-matched pair: all five numbered steps and supporting lines remained in sequence without extra blocks. Nano added duplicate steps and invented text. This is a small-sample result, not a general success-rate claim.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is GPT Image 2.5 cheaper?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Its official image-output token rate is lower: $30 versus $60 per million tokens. Nano Banana 2 has lower input rates. Total image cost depends on token consumption, resolution, quality, references, and any additional attempts.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Are these original model outputs?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. The article shows 10 GPT Image 2.5 route outputs and 11 Nano Banana 2 outputs, including a fresh poster replacement pair. Original PNGs are linked; two prepared references and the official Google screenshot are labeled separately.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u041c\u043e\u0436\u043d\u043e \u043b\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043e\u0431\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u043d\u0430 GlobalGPT?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">We have not verified access to both exact models on GlobalGPT. Its Nano Banana 2 content and a GPT Image 2 listing do not establish availability of GPT Image 2.5.<\/p>","protected":false},"excerpt":{"rendered":"<p>GPT Image 2.5 handled our strict infographic brief better in the matched second pair. Nano Banana 2 produced readable posters and useful label-color edits. The better choice depends on the asset you need. 21Original outputs 5Design briefs 5Native-size-matched pairs Explore image creation and focused editing through GlobalGPT, or compare the original outputs below against your [&hellip;]<\/p>","protected":false},"author":13,"featured_media":19111,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"GPT Image 2.5 vs Nano Banana 2: 5 Same-Prompt Tests","_seopress_titles_desc":"GPT Image 2.5 vs Nano Banana 2: see 21 real outputs, matched-prompt tests, text and editing results, plus recorded tokens and official API rates.","_seopress_robots_index":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-19108","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-chat"],"_links":{"self":[{"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/posts\/19108","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/comments?post=19108"}],"version-history":[{"count":2,"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/posts\/19108\/revisions"}],"predecessor-version":[{"id":19121,"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/posts\/19108\/revisions\/19121"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/media\/19111"}],"wp:attachment":[{"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/media?parent=19108"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/categories?post=19108"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/ru\/wp-json\/wp\/v2\/tags?post=19108"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}