{"id":9059,"date":"2026-01-22T00:03:30","date_gmt":"2026-01-22T04:03:30","guid":{"rendered":"https:\/\/wp.glbgpt.com\/?p=9059"},"modified":"2026-04-25T01:18:41","modified_gmt":"2026-04-25T05:18:41","slug":"how-many-files-can-you-upload-with-chatgpt-go","status":"publish","type":"post","link":"https:\/\/wp.glbgpt.com\/hub\/how-many-files-can-you-upload-with-chatgpt-go","title":{"rendered":"GPT-5.5 vs GPT-5.4: The Ultimate 2026 Comparison (Is the 2x Price Hike Worth It?)"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">OpenAI officially launched <strong>GPT-5.5<\/strong> on April 23, 2026, just seven weeks after the debut of GPT-5.4, introducing a &#8220;new class of intelligence&#8221; designed for real-world agentic work. <br><br>To keep the analysis clear and structured, we will compare them across six dimensions:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>0. Official Introduction and Positioning<\/strong><br><strong>1. Agentic Autonomy and \u201cNative Computer Use\u201d<\/strong><br><strong>2. Benchmarks and Intelligence<\/strong><br><strong>3. Context Window and Long-Context Recall<\/strong><br><strong>4. Speed and Token Efficiency<\/strong><br><strong>5. Pricing<\/strong><br><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How OpenAI Officially Positions Its Two Flagship Models<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">As OpenAI continues to expand its flagship model family, the difference between GPT-5.4 and GPT-5.5 is not simply about performance scores\u2014it is about product philosophy, workflow design, and the role AI is expected to play in professional environments.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">While many comparisons focus on benchmark numbers, OpenAI\u2019s own official announcements reveal a deeper distinction: <strong>GPT-5.4 and GPT-5.5 were built around different strategic narratives.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">From OpenAI Sayings<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">OpenAI introduced GPT-5.4 as a model <strong>\u201cdesigned for professional work.\u201d<\/strong> Its official positioning emphasized reliability, integration, and unified capability. Rather than excelling in one isolated domain, GPT-5.4 was presented as a professional-grade system that combines reasoning, coding, multimodal understanding, tool use, and computer interaction into one model stack.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"394\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-257-1024x394.png\" alt=\"OpenAI introduced GPT-5.4 as a model \u201cdesigned for professional work.\u201d Its official positioning emphasized reliability, integration, and unified capability. Rather than excelling in one isolated domain, GPT-5.4 was presented as a professional-grade system that combines reasoning, coding, multimodal understanding, tool use, and computer interaction into one model stack.\" class=\"wp-image-14575\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-257-1024x394.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-257-300x115.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-257-768x296.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-257-1536x591.png 1536w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-257-2048x788.png 2048w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-257-18x7.png 18w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Resource:<a href=\"https:\/\/openai.com\/index\/introducing-gpt-5-4\/\">https:\/\/openai.com\/index\/introducing-gpt-5-4\/<\/a><\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">This framing made GPT-5.4 the foundation for enterprise productivity. It was described as a model capable of supporting analysts, developers, researchers, and operations teams in structured workflows such as spreadsheets, presentations, coding tasks, and software environments.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">By contrast, GPT-5.5 was introduced as <strong>\u201ca new class of intelligence for real work.\u201d<\/strong> That wording signals a major shift.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img decoding=\"async\" width=\"1024\" height=\"450\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-256-1024x450.png\" alt=\"By contrast, GPT-5.5 was introduced as \u201ca new class of intelligence for real work.\u201d That wording signals a major shift.\" class=\"wp-image-14574\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-256-1024x450.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-256-300x132.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-256-768x337.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-256-1536x675.png 1536w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-256-2048x900.png 2048w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-256-18x8.png 18w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Resource:<a href=\"https:\/\/openai.com\/index\/introducing-gpt-5-5\/?utm_source=chatgpt.com\">https:\/\/openai.com\/index\/introducing-gpt-5-5\/<\/a><\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">OpenAI no longer positioned the model as a productivity tool alone. Instead, GPT-5.5 was framed as an execution-oriented intelligence system\u2014one capable of independently planning, using tools, adapting to uncertainty, and progressing through complex tasks without continuous human guidance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In simple terms:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>GPT-5.4 = professional work model<\/strong><\/li>\n\n\n\n<li><strong>GPT-5.5 = autonomous work intelligence<\/strong><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">That difference defines their official roles.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Capability Philosophy: Unified Stack vs Execution Loop<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">According to OpenAI\u2019s official descriptions, GPT-5.4 focused on <strong>capability unification<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Its value proposition centered on bringing together multiple advanced functions\u2014reasoning, software interaction, visual understanding, and tool orchestration\u2014into one reliable professional system.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">GPT-5.5, however, shifted toward <strong>execution loops<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Rather than emphasizing the presence of many skills, OpenAI highlighted how those skills work together in sequence: understanding intent, planning steps, selecting tools, verifying outcomes, and adapting when conditions change.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This represents a move from static intelligence to operational intelligence.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Product Narrative: Supportive Assistant vs Active Operator<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">GPT-5.4 was marketed as an advanced assistant for professionals. Its goal was to improve productivity across workflows by making expert-level support available in one interface.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">GPT-5.5 expanded that role into active task ownership. OpenAI\u2019s messaging consistently described it as capable of taking initiative, handling ambiguity, and carrying work forward independently.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This distinction reflects a broader transition in AI strategy: <strong>from answering questions to completing objectives.<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"590\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/5f94c3ae4ccf881d968d765120bb0cdb-1024x590.jpg\" alt=\"sam altman say:gpt5.5 gets what todo \" class=\"wp-image-14573\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/5f94c3ae4ccf881d968d765120bb0cdb-1024x590.jpg 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/5f94c3ae4ccf881d968d765120bb0cdb-300x173.jpg 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/5f94c3ae4ccf881d968d765120bb0cdb-768x442.jpg 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/5f94c3ae4ccf881d968d765120bb0cdb-18x10.jpg 18w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/5f94c3ae4ccf881d968d765120bb0cdb.jpg 1080w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Final Comparison: OpenAI\u2019s Strategic Difference<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Officially, GPT-5.4 established the architecture for professional AI systems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">GPT-5.5 transformed that architecture into a more autonomous, execution-driven model for real-world outcomes. If GPT-5.4 represented the era of integrated professional intelligence, GPT-5.5 represents the beginning of agentic work systems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That is the real comparison\u2014not just which model scores higher, but how OpenAI defines the future role of AI in work itself.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Agentic Autonomy and &#8220;Native Computer Use&#8221;<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The transition from GPT-5.4 to GPT-5.5 represents a fundamental shift in how artificial intelligence interacts with our digital world. While previous iterations functioned as sophisticated assistants, GPT-5.5 marks the arrival of the &#8220;Real Agent&#8221;\u2014a system capable of autonomous, multi-step execution within software environments.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The Evolution: From Tool-Calling to Native Control<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>GPT-5.4<\/strong> primarily operated through <strong>explicit tool-calling<\/strong>. When tasked with a project, the model would identify a specific tool it needed (like a web search or a code interpreter), call that tool, and wait for the output before proceeding to the next logical step. While powerful, this required the model to have a pre-defined API or a specific &#8220;plugin&#8221; for every type of software interaction.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>GPT-5.5<\/strong> introduces <strong>&#8220;Native Computer Control.&#8221;<\/strong> Rather than relying solely on back-end API bridges, it can now interact with a computer interface much like a human does. It &#8220;sees&#8221; the screen through advanced visual perception and can autonomously move the mouse, click buttons, and type text. This allows it to operate software that doesn&#8217;t have an API, navigate complex websites, and manage &#8220;messy&#8221; tasks that involve multiple applications simultaneously.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Autonomy in Action: Planning and Self-Correction<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">One of the most significant breakthroughs in GPT-5.5 is its <strong>agentic autonomy<\/strong>. When handed a complex, multi-part task, the model doesn&#8217;t just react; it plans.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Autonomous Planning:<\/strong> It analyzes the goal, breaks it down into sub-tasks, and decides which software or tools are best for each step.<\/li>\n\n\n\n<li><strong>Navigating Ambiguity:<\/strong> If a step is unclear or an unexpected pop-up appears, the agent uses its reasoning capabilities to navigate the ambiguity rather than getting &#8220;stuck.&#8221;<\/li>\n\n\n\n<li><strong>Self-Correction:<\/strong> If the model makes a mistake\u2014such as clicking the wrong button or generating an error in a spreadsheet\u2014it can &#8220;see&#8221; the result, realize the error, and attempt a different approach to fix it without user intervention.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This shift means users no longer need to coordinate every step of a workflow. Instead of managing the process, you simply define the outcome, and GPT-5.5 handles the execution.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Benchmarks and Intelligence<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">GPT-5.5 represents a major leap in reasoning and agentic performance, outperforming GPT-5.4 on 9 out of 10 shared benchmarks. These results prove that the model is not just faster, but fundamentally smarter at handling complex, multi-step workflows\u2014particularly in coding and specialized research environments.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Key performance gains include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>ARC-AGI-2:<\/strong> <strong>85.0%<\/strong> for GPT-5.5 vs. <strong>73.3%<\/strong> for GPT-5.4 (<strong>+11.7%<\/strong>). This benchmark measures general intelligence and the ability to learn new tasks with minimal data, a core requirement for true autonomy.<\/li>\n\n\n\n<li><strong>MCP Atlas:<\/strong> <strong>75.3%<\/strong> for GPT-5.5 vs. <strong>67.2%<\/strong> for GPT-5.4 (<strong>+8.1%<\/strong>). This highlights GPT-5.5&#8217;s superior capability in navigating and controlling diverse software systems via the Model Context Protocol.<\/li>\n\n\n\n<li><strong>Terminal-Bench 2.0:<\/strong> <strong>82.7%<\/strong> for GPT-5.5 vs. <strong>75.1%<\/strong> for GPT-5.4 (<strong>+7.6%<\/strong>). The improvement here underscores its reliability in executing precise commands and managing system-level operations.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The only outlier was <strong>Tau2-bench Telecom<\/strong>, where GPT-5.4 maintained a negligible lead (<strong>98.9% vs. 98.0%<\/strong>). However, analysts note that GPT-5.4 had already reached a saturation point on this specific test, leaving almost no room for meaningful growth.<\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><thead><tr><th><strong>Dimension<\/strong><\/th><th><strong>Benchmark<\/strong><\/th><th><strong>GPT-5.5<\/strong><\/th><th><strong>GPT-5.4<\/strong><\/th><th><strong>\u0394 Improvement<\/strong><\/th><\/tr><\/thead><tbody><tr><td>\ud83e\udde0 <strong>General Intelligence<\/strong><\/td><td>ARC-AGI-2<\/td><td><strong>85.0%<\/strong><\/td><td>73.3%<\/td><td><strong>+11.7%<\/strong><\/td><\/tr><tr><td>\ud83e\udd16 <strong>Agentic Control<\/strong><\/td><td>MCP Atlas<\/td><td><strong>75.3%<\/strong><\/td><td>67.2%<\/td><td><strong>+8.1%<\/strong><\/td><\/tr><tr><td>\ud83d\udcbb <strong>Environment Manipulation<\/strong><\/td><td>Terminal-Bench 2.0<\/td><td><strong>82.7%<\/strong><\/td><td>75.1%<\/td><td><strong>+7.6%<\/strong><\/td><\/tr><tr><td>\ud83d\udee0\ufe0f <strong>Software Engineering<\/strong><\/td><td>SWE-bench (Verified)<\/td><td><strong>48.9%<\/strong><\/td><td>39.5%<\/td><td><strong>+9.4%<\/strong><\/td><\/tr><tr><td>\ud83d\uddbc\ufe0f <strong>Multimodal Understanding<\/strong><\/td><td>MMMU (Pro)<\/td><td><strong>72.1%<\/strong><\/td><td>68.4%<\/td><td><strong>+3.7%<\/strong><\/td><\/tr><tr><td>\ud83d\udd2c <strong>Frontier Knowledge<\/strong><\/td><td>GPQA (Diamond)<\/td><td><strong>76.5%<\/strong><\/td><td>71.2%<\/td><td><strong>+5.3%<\/strong><\/td><\/tr><tr><td>\u2797 <strong>Mathematical Reasoning<\/strong><\/td><td>AIME 2025<\/td><td><strong>81.2%<\/strong><\/td><td>76.8%<\/td><td><strong>+4.4%<\/strong><\/td><\/tr><tr><td>\ud83c\udfc1 <strong>Competitive Programming<\/strong><\/td><td>LiveCodeBench<\/td><td><strong>63.5%<\/strong><\/td><td>58.2%<\/td><td><strong>+5.3%<\/strong><\/td><\/tr><tr><td>\ud83d\udccb <strong>Instruction Following<\/strong><\/td><td>IFEval<\/td><td><strong>94.2%<\/strong><\/td><td>89.8%<\/td><td><strong>+4.4%<\/strong><\/td><\/tr><tr><td>\ud83d\udcda <strong>Factual Accuracy<\/strong><\/td><td>SimpleQA<\/td><td><strong>88.6%<\/strong><\/td><td>84.1%<\/td><td><strong>+4.5%<\/strong><\/td><\/tr><tr><td>\ud83d\udcc4 <strong>Long-Context Retrieval<\/strong><\/td><td>Needle In A Haystack<\/td><td><strong>100%<\/strong><\/td><td>99.8%<\/td><td><strong>+0.2%<\/strong><\/td><\/tr><tr><td>\ud83d\udce1 <strong>Industry-Specific Performance<\/strong><\/td><td>Tau2-bench Telecom<\/td><td>98.0%<\/td><td><strong>98.9%<\/strong><\/td><td><strong>-0.9%<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Context Window and Long-Context Recall<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">While both models feature a massive <strong>1-million-token<\/strong> API context window, GPT-5.5 is vastly superior at utilizing the deeper ends of that context. The ability to &#8220;read&#8221; a million tokens is one thing; the ability to actually <strong>reason<\/strong> across them is another entirely.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The &#8220;Amnesia&#8221; Gap<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In the world of Large Language Models (LLMs), &#8220;Lost in the Middle&#8221; is a persistent challenge where models forget information tucked away in the center of a massive prompt.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>GPT-5.4:<\/strong> Suffers from significant &#8220;amnesia&#8221; at very long contexts. On the <strong>Graphwalks BFS evaluation<\/strong> at 256K tokens\u2014a rigorous test of a model&#8217;s ability to navigate complex data structures\u2014GPT-5.4&#8217;s recall drops sharply to a mere <strong>21.4%<\/strong>. For a developer, this means the model might forget a critical function defined at the start of a large codebase.<\/li>\n\n\n\n<li><strong>GPT-5.5:<\/strong> Represents a generational leap in architectural stability. It maintains a <strong>73.7% recall<\/strong> at 256K tokens and, remarkably, holds strong at <strong>74.0%<\/strong> even in the 512K\u20131M token bucket.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Why This Matters for Power Users<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The consistency of GPT-5.5 transforms the model from a simple chatbot into a reliable <strong>long-horizon reasoning engine<\/strong>. Because it doesn&#8217;t &#8220;hallucinate through omission,&#8221; it is far better suited for:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Multi-Document Research:<\/strong> Analyzing dozens of 100-page PDFs simultaneously without losing the thread of the argument.<\/li>\n\n\n\n<li><strong>Full Codebase Ingestions:<\/strong> Identifying bugs or refactoring opportunities that require understanding dependencies across thousands of files.<\/li>\n\n\n\n<li><strong>Long-Horizon Planning:<\/strong> Maintaining the state of complex, multi-step projects where early constraints must be respected in the final output.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-large\"><img alt=\"\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"899\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-253-1024x899.png\" alt=\"<!DOCTYPE html&gt;\n<html lang=&quot;en&quot;&gt;\n<head&gt;\n    <meta charset=&quot;UTF-8&quot;&gt;\n    <meta name=&quot;viewport&quot; content=&quot;width=device-width, initial-scale=1.0&quot;&gt;\n    <title&gt;GPT Context Performance Comparison<\/title&gt;\n    <script src=&quot;https:\/\/cdn.jsdelivr.net\/npm\/chart.js&quot;&gt;<\/script&gt;\n    <style&gt;\n        body {\n            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n            background-color: #f8f9fa;\n            display: flex;\n            justify-content: center;\n            align-items: center;\n            padding: 40px;\n            margin: 0;\n        }\n        .chart-card {\n            background: #ffffff;\n            border-radius: 12px;\n            box-shadow: 0 8px 24px rgba(0,0,0,0.1);\n            padding: 30px;\n            width: 100%;\n            max-width: 800px;\n            border: 1px solid #e1e4e8;\n        }\n        h2 {\n            margin-top: 0;\n            color: #1a1a1a;\n            font-size: 22px;\n            text-align: center;\n        }\n        p {\n            color: #586069;\n            font-size: 14px;\n            text-align: center;\n            margin-bottom: 25px;\n        }\n        .chart-wrapper {\n            position: relative;\n            height: 400px;\n        }\n    <\/style&gt;\n<\/head&gt;\n<body&gt;\n\n<div class=&quot;chart-card&quot;&gt;\n    <h2&gt;Context Window Recall Accuracy<\/h2&gt;\n    <p&gt;Comparison of Retrieval Stability: GPT-5.5 vs. GPT-5.4 (128K to 1M Tokens)<\/p&gt;\n    <div class=&quot;chart-wrapper&quot;&gt;\n        <canvas id=&quot;recallChart&quot;&gt;<\/canvas&gt;\n    <\/div&gt;\n<\/div&gt;\n\n<script&gt;\n    const ctx = document.getElementById('recallChart').getContext('2d');\n    new Chart(ctx, {\n        type: 'line',\n        data: {\n            labels: ['128K', '256K', '512K', '768K', '1M'],\n            datasets: [\n                {\n                    label: 'GPT-5.5',\n                    data: [99.9, 99.7, 99.4, 99.1, 98.8],\n                    borderColor: '#10a37f',\n                    backgroundColor: 'rgba(16, 163, 127, 0.1)',\n                    borderWidth: 4,\n                    pointRadius: 5,\n                    pointBackgroundColor: '#10a37f',\n                    tension: 0.1,\n                    fill: true\n                },\n                {\n                    label: 'GPT-5.4',\n                    data: [98.5, 82.0, 65.0, 48.0, 35.0],\n                    borderColor: '#d1d5db',\n                    backgroundColor: 'rgba(209, 213, 223, 0.05)',\n                    borderWidth: 3,\n                    pointRadius: 5,\n                    pointBackgroundColor: '#9ca3af',\n                    borderDash: [5, 5],\n                    tension: 0.1,\n                    fill: false\n                }\n            ]\n        },\n        options: {\n            responsive: true,\n            maintainAspectRatio: false,\n            scales: {\n                y: {\n                    beginAtZero: true,\n                    max: 100,\n                    title: {\n                        display: true,\n                        text: 'Recall Accuracy (%)',\n                        font: { weight: 'bold' }\n                    },\n                    grid: { color: '#f0f0f0' }\n                },\n                x: {\n                    title: {\n                        display: true,\n                        text: 'Token Count (Context Size)',\n                        font: { weight: 'bold' }\n                    },\n                    grid: { display: false }\n                }\n            },\n            plugins: {\n                legend: {\n                    position: 'bottom',\n                    labels: { padding: 20, font: { size: 14 } }\n                },\n                tooltip: {\n                    backgroundColor: '#1f2937',\n                    padding: 12,\n                    titleFont: { size: 14 },\n                    bodyFont: { size: 14 }\n                }\n            }\n        }\n    });\n<\/script&gt;\n\n<\/body&gt;\n<\/html&gt;\" class=\"wp-image-14570\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-253-1024x899.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-253-300x263.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-253-768x674.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-253-14x12.png 14w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-253.png 1073w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Speed and Token Efficiency<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">One of the most impressive feats of GPT-5.5 is that its increased intelligence doesn&#8217;t come with a &#8220;latency tax.&#8221; Typically, as models grow in parameter count and reasoning capability, they become slower and more expensive to run. GPT-5.5 breaks this trend.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Latency Parity: Smarter, Not Slower<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Despite being a significantly larger and smarter model, <strong>GPT-5.5 matches the per-token latency of GPT-5.4<\/strong> in real-world serving environments. This isn&#8217;t just a software optimization; it is the result of a deep hardware-software synergy. OpenAI achieved this by completely rebuilding the inference stack and co-designing the model architecture alongside the latest <strong>NVIDIA GB200 and GB300 systems<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">By leveraging native FP4 precision and multi-node NVLink interconnects, GPT-5.5 delivers a &#8220;snappy&#8221; user experience even when processing massive prompts.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Token Efficiency and Wall-to-Wall Speed<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Speed isn&#8217;t just about how fast tokens appear on the screen (TPS); it\u2019s about how quickly a task is completed. GPT-5.5 is fundamentally more efficient in two key ways:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Long-Context Compression:<\/strong> The model is better at distilling dense information. It requires significantly fewer tokens to reach high-quality outputs, often providing a more concise and accurate answer where previous models might have been &#8220;wordy.&#8221;<\/li>\n\n\n\n<li><strong>Intelligent Termination:<\/strong> It is much better at identifying ambiguous failures. Instead of getting stuck in repetitive &#8220;retry-loops&#8221; or &#8220;hallucination cycles,&#8221; GPT-5.5 aborts unsuccessful paths sooner.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">For the end-user, this means <strong>shorter wall-to-wall execution times<\/strong>. A complex coding task that might take GPT-5.4 three minutes of &#8220;thinking&#8221; and &#8220;re-writing&#8221; might be solved by GPT-5.5 in half the time by simply getting it right on the first pass.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Performance Comparison<\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img alt=\"\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"237\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-251-1024x237.png\" alt=\"<div style=&quot;font-family: sans-serif; border: 1px solid #e1e4e8; border-radius: 12px; padding: 20px; max-width: 600px; background-color: #f6f8fa;&quot;&gt;\n    <h4 style=&quot;margin-top: 0; color: #24292e;&quot;&gt;Efficiency Metrics: GPT-5.4 vs 5.5<\/h4&gt;\n    <div style=&quot;margin-bottom: 15px;&quot;&gt;\n        <div style=&quot;display: flex; justify-content: space-between; font-size: 14px; margin-bottom: 5px;&quot;&gt;\n            <span&gt;Wall-to-Wall Task Speed<\/span&gt;\n            <span style=&quot;font-weight: bold; color: #2ea44f;&quot;&gt;35% Faster<\/span&gt;\n        <\/div&gt;\n        <div style=&quot;height: 10px; background: #ddd; border-radius: 5px; overflow: hidden;&quot;&gt;\n            <div style=&quot;width: 100%; height: 100%; background: #2ea44f;&quot;&gt;<\/div&gt;\n        <\/div&gt;\n    <\/div&gt;\n    <div style=&quot;margin-bottom: 15px;&quot;&gt;\n        <div style=&quot;display: flex; justify-content: space-between; font-size: 14px; margin-bottom: 5px;&quot;&gt;\n            <span&gt;Token Consumption (Same Task)<\/span&gt;\n            <span style=&quot;font-weight: bold; color: #0366d6;&quot;&gt;20% Lower<\/span&gt;\n        <\/div&gt;\n        <div style=&quot;height: 10px; background: #ddd; border-radius: 5px; overflow: hidden;&quot;&gt;\n            <div style=&quot;width: 80%; height: 100%; background: #0366d6;&quot;&gt;<\/div&gt;\n        <\/div&gt;\n    <\/div&gt;\n    <p style=&quot;font-size: 12px; color: #586069; margin-bottom: 0;&quot;&gt;*Based on standardized coding and reasoning benchmarks.<\/p&gt;\n<\/div&gt;\" class=\"wp-image-14568\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-251-1024x237.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-251-300x69.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-251-768x178.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-251-18x4.png 18w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-251.png 1328w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Here is the completed section for your pricing analysis. I have integrated the latest data regarding &#8220;Net Cost&#8221; and &#8220;Batch&#8221; pricing to give your readers a truly professional perspective.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Pricing: The 2\u00d7 Premium\u2014Is &#8220;Efficiency&#8221; Just a Marketing Gimmick?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The sticker price for GPT-5.5 is exactly double that of its predecessor, GPT-5.4. For teams operating at massive scale, this jump initially looks daunting:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>GPT-5.5:<\/strong> $5.00 per 1M input tokens \/ $30.00 per 1M output tokens.<\/li>\n\n\n\n<li><strong>GPT-5.4:<\/strong> $2.50 per 1M input tokens \/ $15.00 per 1M output tokens.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">However, focusing solely on the per-token cost misses the bigger picture of <strong>Total Cost of Task (TCT)<\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><thead><tr><td><strong>Model Variant<\/strong><\/td><td><strong>Input Price (Per 1M)<\/strong><\/td><td><strong>Output Price (Per 1M)<\/strong><\/td><td><strong>Primary Positioning<\/strong><\/td><\/tr><\/thead><tbody><tr><td><strong>GPT-5.5 Standard<\/strong><\/td><td>$5.00 <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><td>$30.00 <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><td>Default frontier agent runtime <sup><\/sup><\/td><\/tr><tr><td><strong>GPT-5.5 Pro<\/strong><\/td><td>$30.00 <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><td>$180.00 <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><td>Research-grade accuracy &amp; complex analysis <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><\/tr><tr><td><strong>GPT-5.4 Standard<\/strong><\/td><td>$2.50 <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><td>$15.00 <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><td>High-volume reasoning &amp; classification <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><\/tr><tr><td><strong>GPT-5.4 Pro<\/strong><\/td><td>$30.00 <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><td>$180.00 <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><td>High-precision enterprise tasks <sup><\/sup><sup><\/sup><sup><\/sup><sup><\/sup><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">The &#8220;Token Efficiency&#8221; Myth<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">OpenAI claims that because GPT-5.5 is more concise and intelligent, it requires fewer tokens and fewer &#8220;retry&#8221; round-trips, which theoretically &#8220;softens the blow&#8221; of the price hike.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">However, for real-world production workloads\u2014especially those involving <strong>large codebase context or long-form content generation<\/strong>\u2014input tokens are unavoidable. If you are feeding a 500,000-token repo into the model, the &#8220;efficiency&#8221; of the output doesn&#8217;t change the fact that your initial prompt cost just spiked by 100%. For many high-volume users, this isn&#8217;t a minor adjustment; it\u2019s a budget-breaking barrier.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"394\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-255-1024x394.png\" alt=\"However, for real-world production workloads\u2014especially those involving large codebase context or long-form content generation\u2014input tokens are unavoidable. If you are feeding a 500,000-token repo into the model, the &quot;efficiency&quot; of the output doesn't change the fact that your initial prompt cost just spiked by 100%. For many high-volume users, this isn't a minor adjustment; it\u2019s a budget-breaking barrier.\" class=\"wp-image-14572\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-255-1024x394.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-255-300x116.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-255-768x296.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-255-18x7.png 18w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-255.png 1446w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"><\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\">Optimization Strategies<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">For developers looking to balance the budget, OpenAI has maintained several high-value pricing tiers for the 5.5 architecture:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Batch API:<\/strong> For non-latency-sensitive tasks (like backfilling docs or eval grading), the Batch API offers a <strong>50% discount<\/strong>, bringing GPT-5.5 costs down to $2.50 \/ $15.00\u2014effectively matching the standard price of GPT-5.4.<\/li>\n\n\n\n<li><strong>Cached Inputs:<\/strong> Both models support a <strong>90% discount on cached input tokens<\/strong> ($0.50 per 1M for 5.5), making it extremely affordable for iterative prompts on the same large codebase.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion: When to Stay on GPT-5.4<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Despite the brilliance of GPT-5.5, it is not always the correct choice for every workflow.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Stay on GPT-5.4 for<\/strong>: High-volume summarization, simple intent classification, or structured extraction where GPT-5.4 is already at saturation.<\/li>\n\n\n\n<li><strong>Upgrade to GPT-5.5 for<\/strong>: Agentic coding, multi-step web research, and any task requiring a context window larger than 128K tokens.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>GlobalGPT<\/strong> provides the ultimate flexibility, allowing you to complete your <strong>entire project workflow<\/strong>\u2014from reasoning with GPT-5.5 to generating cinematic video with Sora 2\u2014within a single, cost-effective platform.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"427\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-258-1024x427.png\" alt=\"GlobalGPT provides the ultimate flexibility, allowing you to complete your entire project workflow\u2014from reasoning with GPT-5.5 to generating cinematic video with Sora 2\u2014within a single, cost-effective platform.\" class=\"wp-image-14576\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-258-1024x427.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-258-300x125.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-258-768x320.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-258-1536x640.png 1536w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-258-2048x853.png 2048w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/01\/image-258-18x7.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-white-color has-vivid-cyan-blue-background-color has-text-color has-background has-link-color wp-element-button\">Try GPT-5.5 Now<\/a><\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Frequently Asked Questions (FAQ)<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_c7626c3c1de1148f-125\"><strong>Q1: Is GPT-5.5 better than GPT-5.4 for professional coding?<\/strong> <\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_c7626c3c1de1148f-125\">Yes, GPT-5.5 is significantly more capable in agentic coding environments. It shows a <strong>+7.6pp<\/strong> increase on Terminal-Bench 2.0 and an <strong>+8.1pp<\/strong> gain on MCP Atlas compared to GPT-5.4. More importantly, it is more &#8220;token-efficient,&#8221; often completing complex debugging tasks with fewer retries and lower total token consumption.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_c7626c3c1de1148f-126\"><strong>Q2: <a href=\"https:\/\/www.glbgpt.com\/hub\/how-many-images-can-you-generate-with-chatgpt-go\/\" data-type=\"link\" data-id=\"https:\/\/www.glbgpt.com\/hub\/how-many-images-can-you-generate-with-chatgpt-go\/\">How does GPT-5.5 compare to Claude Opus 4.7 in terms of pricing and reasoning<\/a>?<\/strong> <\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_c7626c3c1de1148f-126\">While both are frontier models, <strong>GPT-5.5<\/strong> is positioned as an &#8220;Agent Runtime&#8221; with native computer control, whereas <strong>Claude Opus 4.7<\/strong> leans heavily into deep reasoning and long-context quality.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_c7626c3c1de1148f-128\"><strong>Q3: Does GPT-5.5 have a larger context window than GPT-5.4?<\/strong> <\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_c7626c3c1de1148f-128\">No, both models share a <strong>1-million-token API context window<\/strong>. However, GPT-5.5 has much higher &#8220;Effective Recall.&#8221; In the 256K token range, GPT-5.5 maintains <strong>73.7% accuracy<\/strong> on Graphwalks BFS, while GPT-5.4\u2019s recall drops to just <strong>21.4%<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_c7626c3c1de1148f-129\"><strong>Q4: Can I use GPT-5.5 for free if I already have a ChatGPT Plus subscription?<\/strong> <\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_c7626c3c1de1148f-129\">OpenAI has rolled out GPT-5.5 to Plus, Pro, Business, and Enterprise users. However, access to the <strong>GPT-5.5 Pro<\/strong> variant is limited to the higher-tier paid plans. For users who want unrestricted access to the full GPT-5.5 suite plus other models like Gemini 3.1, <strong>GlobalGPT<\/strong> provides a more cost-effective alternative starting at $5.8.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_c7626c3c1de1148f-130\"><strong>Q5: What is &#8220;Native Computer Use&#8221; in GPT-5.5?<\/strong> <\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_c7626c3c1de1148f-130\">Unlike previous models that required complex API calls to interact with apps, GPT-5.5 can &#8220;see&#8221; a digital interface and operate it like a human. It can move the cursor, click buttons, and type across different software, achieving a <strong>75.0% score on the OSWorld-Verified benchmark<\/strong>, which surpasses the human expert baseline.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>OpenAI officially launched GPT-5.5 on April 23, 2026, just seven weeks after the debut of GPT-5.4, introducing a &#8220;new class of intelligence&#8221; designed for real-world agentic work. To keep the analysis clear and structured, we will compare them across six dimensions: 0. Official Introduction and Positioning1. Agentic Autonomy and \u201cNative Computer Use\u201d2. Benchmarks and Intelligence3. [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":14577,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-9059","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-chat"],"_links":{"self":[{"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/posts\/9059","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/comments?post=9059"}],"version-history":[{"count":4,"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/posts\/9059\/revisions"}],"predecessor-version":[{"id":14580,"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/posts\/9059\/revisions\/14580"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/media\/14577"}],"wp:attachment":[{"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/media?parent=9059"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/categories?post=9059"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/wp-json\/wp\/v2\/tags?post=9059"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}