{"id":20285,"date":"2026-10-02T03:14:30","date_gmt":"2026-10-02T07:14:30","guid":{"rendered":"https:\/\/wp.glbgpt.com\/?p=20285"},"modified":"2026-10-02T03:14:31","modified_gmt":"2026-10-02T07:14:31","slug":"claude-sonnet-5-5-vs-opus-5-5","status":"publish","type":"post","link":"https:\/\/wp.glbgpt.com\/th\/hub\/claude-sonnet-5-5-vs-opus-5-5","title":{"rendered":"Claude Sonnet 5.5 vs Opus 5.5: \u0e23\u0e32\u0e04\u0e32, \u0e1c\u0e25\u0e17\u0e14\u0e2a\u0e2d\u0e1a\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e, \u0e41\u0e25\u0e30\u0e01\u0e32\u0e23\u0e17\u0e14\u0e2a\u0e2d\u0e1a\u0e08\u0e23\u0e34\u0e07"},"content":{"rendered":"<style>.sonnet-opus{--ink:#172033;--muted:#64748b;--line:#dfe5ef;--navy:#111a36;--purple:#635bdb;--teal:#167d7b;max-width:1120px;margin:28px auto 72px;color:var(--ink);font:16px\/1.72 Inter,ui-sans-serif,-apple-system,BlinkMacSystemFont,\"Segoe UI\",Arial,sans-serif}.sonnet-opus h2{margin:54px 0 14px;padding-left:14px;border-left:5px solid var(--purple);font-size:30px;line-height:1.2}.sonnet-opus h3{line-height:1.25}.sonnet-opus p{margin:0 0 18px}.sonnet-opus a{color:#3557b7}.so-hero{padding:48px 54px 42px;border-radius:22px;background:linear-gradient(135deg,#111a36,#302d76 58%,#168482);color:#fff}.so-hero h1{margin:16px 0 12px;color:#fff;font-size:clamp(34px,5vw,56px);line-height:1.08}.so-hero p{color:#e8ebff;font-size:18px}.so-kicker{display:inline-block;padding:6px 10px;border:1px solid #ffffff4d;border-radius:999px;color:#e9eaff;font-size:12px;font-weight:800;letter-spacing:.08em;text-transform:uppercase}.so-quick,.so-method{margin:24px 0;padding:22px 25px;border:1px solid var(--line);border-radius:16px;background:#f4f2ff}.so-quick{border-left:6px solid var(--purple)}.so-quick h2{margin:0 0 10px;padding:0;border:0;font-size:24px}.so-quick li{margin:7px 0}.so-method{background:#eef6ff;border-color:#cbdcf8}.so-verdict{margin:24px 0;padding:24px 26px;border-radius:16px;background:var(--navy);color:#eff3ff}.so-verdict h2{margin:0 0 10px;padding:0;border:0;color:#fff;font-size:24px}.so-verdict p{color:#e8edff}.so-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:24px 0}.so-stat{padding:18px;border:1px solid var(--line);border-radius:14px;background:#fbfcff}.so-stat strong{display:block;font-size:28px;color:var(--purple)}.so-stat span{display:block;color:var(--muted);font-size:13px}.so-task{margin:28px 0;padding:22px;border:1px solid var(--line);border-radius:18px;background:#fff;box-shadow:0 10px 26px #10182812}.so-task-head{display:flex;justify-content:space-between;gap:16px;align-items:flex-start;margin-bottom:14px}.so-task h3{margin:3px 0;font-size:23px}.so-task-kicker{color:#5c55bd;font-size:11px;font-weight:800;letter-spacing:.08em;text-transform:uppercase}.so-pill{display:inline-flex;padding:5px 10px;border-radius:999px;background:#eef3ff;color:#334c9f;font-size:12px;font-weight:800}.so-observation{margin:14px 0;padding:12px 14px;border-left:4px solid var(--purple);background:#f5f3ff}.so-note{color:var(--muted);font-size:13px}.so-faq h3{margin-top:26px;font-size:20px}@media(max-width:780px){.so-hero{padding:34px 22px}.so-grid{grid-template-columns:1fr}.so-task-head{display:block}.so-pill{margin-top:10px}}<\/style>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><header class=\"so-hero\"><span class=\"so-kicker\">Claude 5.5 comparison \u00b7 matched API tasks \u00b7 October 1, 2026<\/span><h1>Claude Sonnet 5.5 vs Opus 5.5: \u0e23\u0e32\u0e04\u0e32, \u0e1c\u0e25\u0e17\u0e14\u0e2a\u0e2d\u0e1a\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e, \u0e41\u0e25\u0e30\u0e01\u0e32\u0e23\u0e17\u0e14\u0e2a\u0e2d\u0e1a\u0e08\u0e23\u0e34\u0e07<\/h1><p>Sonnet 5.5 and Opus 5.5 share the Claude 5.5 generation, but they are priced and positioned differently. This comparison puts both through the same five prompts and reports what the route actually returned.<\/p><\/header><\/div>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><nav aria-label=\"\u0e2a\u0e32\u0e23\u0e1a\u0e31\u0e0d\" style=\"margin:24px 0;padding:18px 22px;border:1px solid #dfe5ef;border-radius:14px;background:#fbfcff\"><strong>\u0e1a\u0e19\u0e2b\u0e19\u0e49\u0e32\u0e19\u0e35\u0e49<\/strong><ul style=\"margin:10px 0 0;padding-left:20px\"><li><a href=\"#quick-answer\">\u0e04\u0e33\u0e15\u0e2d\u0e1a\u0e2a\u0e31\u0e49\u0e19\u0e46<\/a><\/li><li><a href=\"#specs\">Official price and model specs<\/a><\/li><li><a href=\"#benchmarks\">Official benchmark context<\/a><\/li><li><a href=\"#method\">Same-prompt API method<\/a><\/li><li><a href=\"#aggregate\">Time, tokens, and estimated cost<\/a><\/li><li><a href=\"#task-cards\">Five task cards<\/a><\/li><li><a href=\"#api-caveats\">\u0e02\u0e49\u0e2d\u0e04\u0e27\u0e23\u0e23\u0e30\u0e27\u0e31\u0e07\u0e40\u0e01\u0e35\u0e48\u0e22\u0e27\u0e01\u0e31\u0e1a API \u0e41\u0e25\u0e30\u0e01\u0e32\u0e23\u0e22\u0e49\u0e32\u0e22\u0e23\u0e30\u0e1a\u0e1a<\/a><\/li><li><a href=\"#verdict\">\u0e2a\u0e34\u0e48\u0e07\u0e17\u0e35\u0e48\u0e2b\u0e25\u0e31\u0e01\u0e10\u0e32\u0e19\u0e2a\u0e19\u0e31\u0e1a\u0e2a\u0e19\u0e38\u0e19<\/a><\/li><li><a href=\"#faq\">\u0e04\u0e33\u0e16\u0e32\u0e21\u0e17\u0e35\u0e48\u0e1e\u0e1a\u0e1a\u0e48\u0e2d\u0e22<\/a><\/li><\/ul><\/nav><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The measured gap is mostly about cost and speed.<\/strong> In this five-prompt pack, Sonnet 5.5 finished faster, used fewer output tokens, and produced the lower arithmetic request-cost estimate. Opus 5.5 used more tokens and time, while its answers were often more expansive. The task cards show where that extra text changed the useful result and where both models simply passed.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The prompts, request ceiling, effort setting, endpoint, and one-run-per-model design were kept aligned with the previous <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-opus-5-5-review\/\">\u0e23\u0e35\u0e27\u0e34\u0e27 Claude Opus 5.5<\/a>.<\/p>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<figure class=\"wp-block-image size-large\"><img alt=\"\" fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"583\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/Claude-sonnet-5.5-1024x583.png\" class=\"wp-image-20335\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/Claude-sonnet-5.5-1024x583.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/Claude-sonnet-5.5-300x171.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/Claude-sonnet-5.5-768x437.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/Claude-sonnet-5.5-18x10.png 18w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/Claude-sonnet-5.5-1536x874.png 1536w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/Claude-sonnet-5.5.jpg 1699w\" 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-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/www.glbgpt.com\/home\/claude-sonnet-5-5?inviter=hub_sonnet55&amp;login=1\" style=\"background:linear-gradient(135deg,rgb(255,203,112) 0%,rgb(199,81,192) 78%,rgb(65,88,208) 100%)\"><strong>Try Claude Sonnet 5.5 Now on GlobalGPT<\/strong><\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><section class=\"so-quick\" id=\"quick-answer\"><h2>\u0e04\u0e33\u0e15\u0e2d\u0e1a\u0e2a\u0e31\u0e49\u0e19\u0e46<\/h2><ul><li><strong>Speed in this matched run:<\/strong> Sonnet 5.5 finished five sequential requests in 31.930 seconds locally; Opus 5.5 took 47.725 seconds.<\/li><li><strong>Route-reported output tokens:<\/strong> Sonnet 5.5 used 2,686; Opus 5.5 used 3,952. Thinking fields were 987 and 2,214 respectively.<\/li><li><strong>Arithmetic list-price estimate:<\/strong> the five Sonnet requests came to about $0.02826; the five Opus requests came to about $0.08184, using official standard token rates and excluding cache, credits, tax, and markup.<\/li><li><strong>Task result:<\/strong> both models passed the extraction, JSON, and math prompts. Sonnet kept the requested two-paragraph Chinese format more cleanly; the coding outputs were both usable but differed in depth and edge-case explanation.<\/li><li><strong>API migration:<\/strong> thinking cannot be disabled; forced tool choice is rejected, and token budgets include thinking. Check the <a href=\"#api-caveats\">migration caveats<\/a> before switching.<\/li><li><strong>Decision boundary:<\/strong> this is one run per task through a third-party route. It measures the observed prompt responses, local wall time, and route-reported usage\u2014not a universal capability score.<\/li><\/ul><\/section><\/div>\n\n\n\n<h2 id=\"specs\" class=\"wp-block-heading\">Official price and model specs<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic\u2019s current model cards list both models with a 1M-token context window and a 128K maximum output. Sonnet 5.5 is labelled <strong>\u0e23\u0e27\u0e14\u0e40\u0e23\u0e47\u0e27<\/strong> with high default effort; Opus 5.5 is labelled <strong>\u0e1b\u0e32\u0e19\u0e01\u0e25\u0e32\u0e07<\/strong> with medium default effort. The standard token rates make Sonnet 5.5 half the input and output price of Opus 5.5.<\/p>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;border-collapse:collapse;min-width:680px\"><thead><tr><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">ID API<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Official latency label<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e40\u0e02\u0e49\u0e32 \/ \u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e2d\u0e2d\u0e01<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e1a\u0e23\u0e34\u0e1a\u0e17 \/ \u0e01\u0e33\u0e25\u0e31\u0e07\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e04\u0e27\u0e32\u0e21\u0e1e\u0e22\u0e32\u0e22\u0e32\u0e21\u0e15\u0e32\u0e21\u0e04\u0e48\u0e32\u0e40\u0e23\u0e34\u0e48\u0e21\u0e15\u0e49\u0e19<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e01\u0e32\u0e23\u0e2d\u0e48\u0e32\u0e19\u0e41\u0e04\u0e0a<\/th><\/tr><\/thead><tbody><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Sonnet 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><code>claude-sonnet-5-5<\/code><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Active \u00b7 Fast<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">$2 \/ $10 \u0e15\u0e48\u0e2d MTok<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">1M \/ 128K<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">\u0e2a\u0e39\u0e07<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">$0.20 \/ MTok<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Opus 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><code>claude-opus-5-5<\/code><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Active \u00b7 Moderate<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">$4 \/ $20 \u0e15\u0e48\u0e2d MTok<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">1M \/ 128K<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e01\u0e25\u0e32\u0e07<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">$0.20 \/ MTok<\/td><\/tr><\/tbody><\/table><\/div><\/div>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"922\" height=\"244\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/official-sonnet-55-price-specs.png\" alt=\"Claude Sonnet 5.5 official documentation: 1M context, 128K output, $2 input and $10 output per million tokens\" class=\"wp-image-20318\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/official-sonnet-55-price-specs.png 922w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/official-sonnet-55-price-specs-300x79.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/official-sonnet-55-price-specs-768x203.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/official-sonnet-55-price-specs-18x5.png 18w\" sizes=\"(max-width: 922px) 100vw, 922px\" \/><figcaption class=\"wp-element-caption\"><a href=\"https:\/\/platform.claude.com\/docs\/en\/models\/sonnet-5-5\/overview\">Anthropic\u2019s Sonnet 5.5 model card<\/a>. Standard API prices per million tokens; captured October 2, 2026.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"922\" height=\"244\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/official-opus-55-price-specs.png\" alt=\"Claude Opus 5.5 official documentation: 1M context, 128K output, $4 input and $20 output per million tokens\" class=\"wp-image-20319\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/official-opus-55-price-specs.png 922w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/official-opus-55-price-specs-300x79.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/official-opus-55-price-specs-768x203.png 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/10\/official-opus-55-price-specs-18x5.png 18w\" sizes=\"(max-width: 922px) 100vw, 922px\" \/><figcaption class=\"wp-element-caption\"><a href=\"https:\/\/platform.claude.com\/docs\/en\/models\/opus-5-5\/overview\">Anthropic\u2019s Opus 5.5 model card<\/a>. Standard API prices per million tokens; captured October 2, 2026.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">For a simple uncached example of 100,000 input tokens plus 20,000 output tokens, the listed token rates work out to about $0.40 on Sonnet 5.5 and $0.80 on Opus 5.5. See the <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-ai-pricing-2026-the-ultimate-guide-to-plans-api-costs-and-limits\/\">Claude pricing and limits guide<\/a> for plan context.<\/p>\n\n\n\n<h2 id=\"benchmarks\" class=\"wp-block-heading\">Official benchmark context<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Anthropic\u2019s <a href=\"https:\/\/www.anthropic.com\/claude-sonnet-5-5\">Sonnet 5.5 announcement<\/a> places both models in one provider-reported table. The row pattern is mixed by benchmark and effort setting.<\/p>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;border-collapse:collapse;min-width:680px\"><thead><tr><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Anthropic-reported benchmark<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Sonnet 5.5<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Opus 5.5<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e27\u0e31\u0e14<\/th><\/tr><\/thead><tbody><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Terminal-Bench 4.0<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">70.6%<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">66.4%\u00b9<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Agentic terminal coding<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">FrontierCode v1.1 (Main)<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">46.2% Max\u00b2 \/ 52.1% Xhigh<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">54.4%<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Whether code changes would be merged<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">CursorBench 4.0<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">55.5%<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">57.8%<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">\u0e07\u0e32\u0e19\u0e01\u0e32\u0e23\u0e40\u0e02\u0e49\u0e32\u0e23\u0e2b\u0e31\u0e2a\u0e2b\u0e25\u0e32\u0e22\u0e44\u0e1f\u0e25\u0e4c\u0e17\u0e35\u0e48\u0e21\u0e35\u0e04\u0e27\u0e32\u0e21\u0e04\u0e25\u0e38\u0e21\u0e40\u0e04\u0e23\u0e37\u0e2d<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">GDPval-AA v2.1<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">1844<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">1846<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Knowledge work across occupations<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">\u0e01\u0e32\u0e23\u0e2a\u0e2d\u0e1a\u0e04\u0e23\u0e31\u0e49\u0e07\u0e2a\u0e38\u0e14\u0e17\u0e49\u0e32\u0e22\u0e02\u0e2d\u0e07\u0e21\u0e19\u0e38\u0e29\u0e22\u0e0a\u0e32\u0e15\u0e34<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">64.5% with tools<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">67.7% with tools<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">\u0e01\u0e32\u0e23\u0e04\u0e34\u0e14\u0e27\u0e34\u0e40\u0e04\u0e23\u0e32\u0e30\u0e2b\u0e4c\u0e41\u0e1a\u0e1a\u0e2a\u0e2b\u0e2a\u0e32\u0e02\u0e32\u0e27\u0e34\u0e0a\u0e32<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">OSWorld 2.1<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">80.1% partial<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">81.8% partial<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">\u0e07\u0e32\u0e19\u0e17\u0e35\u0e48\u0e40\u0e01\u0e35\u0e48\u0e22\u0e27\u0e02\u0e49\u0e2d\u0e07\u0e01\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e43\u0e0a\u0e49\u0e04\u0e2d\u0e21\u0e1e\u0e34\u0e27\u0e40\u0e15\u0e2d\u0e23\u0e4c<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Chartography<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">61.6% no tools<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">64.4% no tools<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Visual chart recognition<\/td><\/tr><\/tbody><\/table><\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">\u00b9 Opus 5.5 Terminal-Bench uses xhigh effort; \u00b2 Sonnet 5.5 FrontierCode is 46.2% at Max and 52.1% at Xhigh effort. OSWorld values are marked partial. These are provider context, not an equal-effort independent rerun.<\/p>\n\n\n\n<h2 id=\"method\" class=\"wp-block-heading\">Same-prompt API method<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Each model received the same five prompts through <code>POST https:\/\/anywhere.broly.ai\/v1\/messages<\/code>. The client sent one user message, <code>max_tokens: 8192<\/code>, <code>output_config.effort: \u0e2a\u0e39\u0e07<\/code>, and no tools. Every request returned HTTP 200 and <code>\u0e08\u0e1a\u0e40\u0e17\u0e34\u0e23\u0e4c\u0e19<\/code>.<\/p>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><div class=\"so-method\"><strong>\u0e02\u0e2d\u0e1a\u0e40\u0e02\u0e15\u0e01\u0e32\u0e23\u0e27\u0e31\u0e14:<\/strong> elapsed time is local end-to-end wall-clock time, not provider latency. Token counts and the thinking field are route-reported usage.<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">The testing approach follows the broader <a href=\"https:\/\/www.glbgpt.com\/hub\/how-globalgpt-tests-ai-models\/\">GlobalGPT model-testing workflow<\/a>. A separate <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-api-guide\/\">\u0e04\u0e39\u0e48\u0e21\u0e37\u0e2d API Claude<\/a> covers request setup and token accounting.<\/p>\n\n\n\n<h2 id=\"aggregate\" class=\"wp-block-heading\">Time, tokens, and estimated cost<\/h2>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;border-collapse:collapse;min-width:680px\"><thead><tr><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e40\u0e2a\u0e49\u0e19\u0e17\u0e32\u0e07<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Five-run local time<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e42\u0e17\u0e40\u0e04\u0e47\u0e19\u0e2d\u0e34\u0e19\u0e1e\u0e38\u0e15<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e42\u0e17\u0e40\u0e04\u0e47\u0e19\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Reported thinking<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Estimated request cost*<\/th><\/tr><\/thead><tbody><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Sonnet 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">31.930s<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">700<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">2,686<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">987<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>$0.02826<\/strong><\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Opus 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">47.725 \u0e27\u0e34\u0e19\u0e32\u0e17\u0e35<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">700<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">3,952<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">2,214<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>$0.08184<\/strong><\/td><\/tr><\/tbody><\/table><\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">* Estimate from route-reported tokens and official standard rates. Sonnet 5.5 was 33.1% lower on local elapsed time and 32.0% lower on output-token count in this pack.<\/p>\n\n\n\n<h2 id=\"task-cards\" class=\"wp-block-heading\">\u0e1a\u0e31\u0e15\u0e23\u0e07\u0e32\u0e19 5 \u0e43\u0e1a\u0e17\u0e35\u0e48\u0e15\u0e23\u0e07\u0e01\u0e31\u0e19<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Each card keeps the task, observation, local time, route-reported tokens, status, and boundary together.<\/p>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><section class=\"so-task\" id=\"coding-debug\"><div class=\"so-task-head\"><div><span class=\"so-task-kicker\">\u0e07\u0e32\u0e19 01 \u00b7 \u0e01\u0e32\u0e23\u0e41\u0e01\u0e49\u0e44\u0e02\u0e02\u0e49\u0e2d\u0e1c\u0e34\u0e14\u0e1e\u0e25\u0e32\u0e14\u0e43\u0e19 Python<\/span><h3>\u0e42\u0e21\u0e40\u0e14\u0e25\u0e40\u0e2b\u0e25\u0e48\u0e32\u0e19\u0e35\u0e49\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e41\u0e01\u0e49\u0e44\u0e02\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19\u0e01\u0e32\u0e23\u0e25\u0e1a\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e0b\u0e49\u0e33\u0e41\u0e1a\u0e1a\u0e1c\u0e2a\u0e21\u0e44\u0e14\u0e49\u0e2b\u0e23\u0e37\u0e2d\u0e44\u0e21\u0e48?<\/h3><\/div><span class=\"so-pill\">\u0e04\u0e33\u0e2a\u0e31\u0e48\u0e07\u0e17\u0e35\u0e48\u0e15\u0e23\u0e07\u0e01\u0e31\u0e19 \u00b7 \u0e14\u0e33\u0e40\u0e19\u0e34\u0e19\u0e01\u0e32\u0e23\u0e04\u0e23\u0e31\u0e49\u0e07\u0e25\u0e30\u0e2b\u0e19\u0e36\u0e48\u0e07\u0e04\u0e23\u0e31\u0e49\u0e07<\/span><\/div><p><strong>\u0e01\u0e32\u0e23\u0e15\u0e31\u0e49\u0e07\u0e04\u0e48\u0e32\u0e07\u0e32\u0e19:<\/strong> The exact prompt was copied from the earlier <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-opus-5-5-review\/\">\u0e23\u0e35\u0e27\u0e34\u0e27 Claude Opus 5.5<\/a> test pack.<\/p><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;border-collapse:collapse;min-width:680px\"><thead><tr><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Time \/ route usage<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Short result label<\/th><\/tr><\/thead><tbody><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Sonnet 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">8.679s<br>145 input \/ 830 output<br>0 thinking<br>HTTP 200 \u00b7 end_turn<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Corrected function plus two tests; shorter explanation.<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Opus 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">15.844s<br>145 input \/ 1478 output<br>700 thinking<br>HTTP 200 \u00b7 end_turn<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Corrected function plus two tests; more edge-case discussion.<\/td><\/tr><\/tbody><\/table><\/div><p class=\"so-observation\"><strong>Observed comparison:<\/strong> Both returned a corrected function and two tests. Sonnet 5.5 used type-tagged keys, <code>casefold()<\/code>, and a list fallback in a shorter response; Opus 5.5 spent more output tokens explaining additional edge cases. Neither run establishes a general coding winner.<\/p><p class=\"so-note\"><strong>\u0e02\u0e2d\u0e1a\u0e40\u0e02\u0e15:<\/strong> A single small Python repair checks concrete edge cases, not reliability across repositories or tool-driven coding.<\/p><\/section><\/div>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><section class=\"so-task\" id=\"long-context-extract\"><div class=\"so-task-head\"><div><span class=\"so-task-kicker\">\u0e07\u0e32\u0e19 02 \u00b7 \u0e01\u0e32\u0e23\u0e2a\u0e01\u0e31\u0e14\u0e41\u0e1a\u0e1a\u0e22\u0e36\u0e14\u0e15\u0e34\u0e14\u0e01\u0e31\u0e1a\u0e1e\u0e37\u0e49\u0e19<\/span><h3>\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07\u0e40\u0e2b\u0e25\u0e48\u0e32\u0e19\u0e35\u0e49\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e23\u0e31\u0e01\u0e29\u0e32\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e17\u0e31\u0e49\u0e07\u0e2b\u0e49\u0e32\u0e02\u0e49\u0e2d\u0e17\u0e35\u0e48\u0e43\u0e2b\u0e49\u0e44\u0e27\u0e49\u0e44\u0e14\u0e49\u0e2b\u0e23\u0e37\u0e2d\u0e44\u0e21\u0e48 \u0e42\u0e14\u0e22\u0e44\u0e21\u0e48\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e02\u0e49\u0e2d\u0e2d\u0e49\u0e32\u0e07\u0e43\u0e14\u0e46?<\/h3><\/div><span class=\"so-pill\">\u0e04\u0e33\u0e2a\u0e31\u0e48\u0e07\u0e17\u0e35\u0e48\u0e15\u0e23\u0e07\u0e01\u0e31\u0e19 \u00b7 \u0e14\u0e33\u0e40\u0e19\u0e34\u0e19\u0e01\u0e32\u0e23\u0e04\u0e23\u0e31\u0e49\u0e07\u0e25\u0e30\u0e2b\u0e19\u0e36\u0e48\u0e07\u0e04\u0e23\u0e31\u0e49\u0e07<\/span><\/div><p><strong>\u0e01\u0e32\u0e23\u0e15\u0e31\u0e49\u0e07\u0e04\u0e48\u0e32\u0e07\u0e32\u0e19:<\/strong> The exact prompt was copied from the earlier <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-opus-5-5-review\/\">\u0e23\u0e35\u0e27\u0e34\u0e27 Claude Opus 5.5<\/a> test pack.<\/p><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;border-collapse:collapse;min-width:680px\"><thead><tr><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Time \/ route usage<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Short result label<\/th><\/tr><\/thead><tbody><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Sonnet 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">3.569s<br>210 input \/ 209 output<br>0 thinking<br>HTTP 200 \u00b7 end_turn<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Five numbered bullets; supplied facts preserved.<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Opus 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">4.374s<br>210 input \/ 312 output<br>101 thinking<br>HTTP 200 \u00b7 end_turn<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Five numbered bullets; supplied facts preserved.<\/td><\/tr><\/tbody><\/table><\/div><p class=\"so-observation\"><strong>Observed comparison:<\/strong> Both returned exactly five numbered bullets and stayed within the supplied facts. Sonnet 5.5 used 209 output tokens versus Opus 5.5&#8217;s 312, but the prompt was a short note rather than a large-context input.<\/p><p class=\"so-note\"><strong>\u0e02\u0e2d\u0e1a\u0e40\u0e02\u0e15:<\/strong> This is a grounded extraction and formatting check; it is not evidence of million-token context performance.<\/p><\/section><\/div>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><section class=\"so-task\" id=\"structured-json\"><div class=\"so-task-head\"><div><span class=\"so-task-kicker\">\u0e07\u0e32\u0e19 03 \u00b7 \u0e04\u0e27\u0e32\u0e21\u0e2a\u0e2d\u0e14\u0e04\u0e25\u0e49\u0e2d\u0e07\u0e01\u0e31\u0e1a JSON<\/span><h3>\u0e42\u0e21\u0e40\u0e14\u0e25\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e04\u0e37\u0e19\u0e42\u0e04\u0e23\u0e07\u0e2a\u0e23\u0e49\u0e32\u0e07\u0e17\u0e35\u0e48\u0e15\u0e23\u0e07\u0e01\u0e31\u0e1a\u0e17\u0e35\u0e48\u0e02\u0e2d\u0e21\u0e32\u0e44\u0e14\u0e49\u0e2b\u0e23\u0e37\u0e2d\u0e44\u0e21\u0e48?<\/h3><\/div><span class=\"so-pill\">\u0e04\u0e33\u0e2a\u0e31\u0e48\u0e07\u0e17\u0e35\u0e48\u0e15\u0e23\u0e07\u0e01\u0e31\u0e19 \u00b7 \u0e14\u0e33\u0e40\u0e19\u0e34\u0e19\u0e01\u0e32\u0e23\u0e04\u0e23\u0e31\u0e49\u0e07\u0e25\u0e30\u0e2b\u0e19\u0e36\u0e48\u0e07\u0e04\u0e23\u0e31\u0e49\u0e07<\/span><\/div><p><strong>\u0e01\u0e32\u0e23\u0e15\u0e31\u0e49\u0e07\u0e04\u0e48\u0e32\u0e07\u0e32\u0e19:<\/strong> The exact prompt was copied from the earlier <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-opus-5-5-review\/\">\u0e23\u0e35\u0e27\u0e34\u0e27 Claude Opus 5.5<\/a> test pack.<\/p><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;border-collapse:collapse;min-width:680px\"><thead><tr><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Time \/ route usage<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Short result label<\/th><\/tr><\/thead><tbody><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Sonnet 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">5.052s<br>128 input \/ 260 output<br>0 thinking<br>HTTP 200 \u00b7 end_turn<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Parseable JSON; requested keys and item counts.<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Opus 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">8.276s<br>128 input \/ 622 output<br>390 thinking<br>HTTP 200 \u00b7 end_turn<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Parseable JSON; requested keys and item counts.<\/td><\/tr><\/tbody><\/table><\/div><p class=\"so-observation\"><strong>Observed comparison:<\/strong> Both returned parseable JSON with the requested keys and two pros\/two cons. Sonnet 5.5 was more concise at 260 output tokens; the strings describe a fictional review setup and are not product facts.<\/p><p class=\"so-note\"><strong>\u0e02\u0e2d\u0e1a\u0e40\u0e02\u0e15:<\/strong> Passing this schema once does not measure structured-output reliability under tool calls or long conversations.<\/p><\/section><\/div>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><section class=\"so-task\" id=\"math-reasoning\"><div class=\"so-task-head\"><div><span class=\"so-task-kicker\">\u0e07\u0e32\u0e19 04 \u00b7 \u0e04\u0e13\u0e34\u0e15\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c<\/span><h3>\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07\u0e40\u0e2b\u0e25\u0e48\u0e32\u0e19\u0e35\u0e49\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e19\u0e33\u0e25\u0e33\u0e14\u0e31\u0e1a\u0e0a\u0e38\u0e14\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e17\u0e35\u0e48\u0e01\u0e25\u0e21\u0e01\u0e25\u0e37\u0e19\u0e44\u0e1b\u0e08\u0e19\u0e16\u0e36\u0e07\u0e04\u0e33\u0e15\u0e2d\u0e1a\u0e2a\u0e38\u0e14\u0e17\u0e49\u0e32\u0e22\u0e44\u0e14\u0e49\u0e2b\u0e23\u0e37\u0e2d\u0e44\u0e21\u0e48?<\/h3><\/div><span class=\"so-pill\">\u0e04\u0e33\u0e2a\u0e31\u0e48\u0e07\u0e17\u0e35\u0e48\u0e15\u0e23\u0e07\u0e01\u0e31\u0e19 \u00b7 \u0e14\u0e33\u0e40\u0e19\u0e34\u0e19\u0e01\u0e32\u0e23\u0e04\u0e23\u0e31\u0e49\u0e07\u0e25\u0e30\u0e2b\u0e19\u0e36\u0e48\u0e07\u0e04\u0e23\u0e31\u0e49\u0e07<\/span><\/div><p><strong>\u0e01\u0e32\u0e23\u0e15\u0e31\u0e49\u0e07\u0e04\u0e48\u0e32\u0e07\u0e32\u0e19:<\/strong> The exact prompt was copied from the earlier <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-opus-5-5-review\/\">\u0e23\u0e35\u0e27\u0e34\u0e27 Claude Opus 5.5<\/a> test pack.<\/p><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;border-collapse:collapse;min-width:680px\"><thead><tr><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Time \/ route usage<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Short result label<\/th><\/tr><\/thead><tbody><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Sonnet 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">2.947s<br>89 input \/ 163 output<br>0 thinking<br>HTTP 200 \u00b7 end_turn<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Correct result: 67; final answer on its own line.<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Opus 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">3.830s<br>89 input \/ 257 output<br>74 thinking<br>HTTP 200 \u00b7 end_turn<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Correct result: 67; final answer on its own line.<\/td><\/tr><\/tbody><\/table><\/div><p class=\"so-observation\"><strong>Observed comparison:<\/strong> Both calculated 67 and placed the final answer on its own line. Sonnet 5.5 used 163 output tokens versus Opus 5.5&#8217;s 257, with no observed correctness difference on this prompt.<\/p><p class=\"so-note\"><strong>\u0e02\u0e2d\u0e1a\u0e40\u0e02\u0e15:<\/strong> One arithmetic sequence cannot estimate broad reasoning reliability.<\/p><\/section><\/div>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><section class=\"so-task\" id=\"chinese-seo-outline\"><div class=\"so-task-head\"><div><span class=\"so-task-kicker\">\u0e07\u0e32\u0e19 05 \u00b7 \u0e01\u0e32\u0e23\u0e08\u0e31\u0e14\u0e23\u0e39\u0e1b\u0e41\u0e1a\u0e1a\u0e15\u0e32\u0e21\u0e41\u0e1a\u0e1a\u0e08\u0e35\u0e19<\/span><h3>\u0e40\u0e2a\u0e49\u0e19\u0e17\u0e32\u0e07\u0e19\u0e35\u0e49\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e23\u0e31\u0e01\u0e29\u0e32\u0e42\u0e04\u0e23\u0e07\u0e2a\u0e23\u0e49\u0e32\u0e07\u0e2a\u0e2d\u0e07\u0e22\u0e48\u0e2d\u0e2b\u0e19\u0e49\u0e32\u0e17\u0e35\u0e48\u0e01\u0e33\u0e2b\u0e19\u0e14\u0e44\u0e27\u0e49\u0e44\u0e14\u0e49\u0e2b\u0e23\u0e37\u0e2d\u0e44\u0e21\u0e48?<\/h3><\/div><span class=\"so-pill\">\u0e04\u0e33\u0e2a\u0e31\u0e48\u0e07\u0e17\u0e35\u0e48\u0e15\u0e23\u0e07\u0e01\u0e31\u0e19 \u00b7 \u0e14\u0e33\u0e40\u0e19\u0e34\u0e19\u0e01\u0e32\u0e23\u0e04\u0e23\u0e31\u0e49\u0e07\u0e25\u0e30\u0e2b\u0e19\u0e36\u0e48\u0e07\u0e04\u0e23\u0e31\u0e49\u0e07<\/span><\/div><p><strong>\u0e01\u0e32\u0e23\u0e15\u0e31\u0e49\u0e07\u0e04\u0e48\u0e32\u0e07\u0e32\u0e19:<\/strong> The exact prompt was copied from the earlier <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-opus-5-5-review\/\">\u0e23\u0e35\u0e27\u0e34\u0e27 Claude Opus 5.5<\/a> test pack.<\/p><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;border-collapse:collapse;min-width:680px\"><thead><tr><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Time \/ route usage<\/th><th style=\"background:#f1f3fa;border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top;font-size:13px\">Short result label<\/th><\/tr><\/thead><tbody><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Sonnet 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">11.683s<br>128 input \/ 1224 output<br>987 thinking<br>HTTP 200 \u00b7 end_turn<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Two Chinese paragraphs; no extra framing.<\/td><\/tr><tr><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\"><strong>Claude Opus 5.5<\/strong><\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">15.401s<br>128 input \/ 1283 output<br>949 thinking<br>HTTP 200 \u00b7 end_turn<\/td><td style=\"border:1px solid #dfe5ef;padding:11px 12px;text-align:left;vertical-align:top\">Covered the points; added Markdown and an English note.<\/td><\/tr><\/tbody><\/table><\/div><p class=\"so-observation\"><strong>Observed comparison:<\/strong> Sonnet 5.5 returned the requested two Chinese paragraphs without extra framing. Opus 5.5 also covered the requested points but added Markdown framing and an English note. This records format following in one route run, not overall Chinese quality.<\/p><p class=\"so-note\"><strong>\u0e02\u0e2d\u0e1a\u0e40\u0e02\u0e15:<\/strong> The prompt asks for an opening about Opus 5.5, so the text itself is not a neutral language benchmark.<\/p><\/section><\/div>\n\n\n\n<h2 id=\"api-caveats\" class=\"wp-block-heading\">\u0e02\u0e49\u0e2d\u0e04\u0e27\u0e23\u0e23\u0e30\u0e27\u0e31\u0e07\u0e40\u0e01\u0e35\u0e48\u0e22\u0e27\u0e01\u0e31\u0e1a API \u0e41\u0e25\u0e30\u0e01\u0e32\u0e23\u0e22\u0e49\u0e32\u0e22\u0e23\u0e30\u0e1a\u0e1a<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Sonnet 5.5 runs adaptive thinking by default; thinking: disabled returns a 400 error, and max_tokens covers thinking plus response text. Forced tool_choice any\/tool is rejected. Parse content blocks by type and re-baseline token budgets before switching.<\/p>\n\n\n\n<h2 id=\"verdict\" class=\"wp-block-heading\">\u0e2a\u0e34\u0e48\u0e07\u0e17\u0e35\u0e48\u0e2b\u0e25\u0e31\u0e01\u0e10\u0e32\u0e19\u0e2a\u0e19\u0e31\u0e1a\u0e2a\u0e19\u0e38\u0e19<\/h2>\n\n\n\n<div class=\"sonnet-opus\" style=\"margin:0;max-width:none\"><div class=\"so-verdict\"><h2>\u0e04\u0e33\u0e15\u0e31\u0e14\u0e2a\u0e34\u0e19\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e07\u0e32\u0e19<\/h2><p><strong>Sonnet 5.5:<\/strong> lower measured cost and time on this route, with clean format compliance on the Chinese task.<\/p><p><strong>Opus 5.5:<\/strong> more expensive and more expansive here; official benchmark rows remain ahead on several open-ended tasks.<\/p><p>Use the task-level evidence to choose a starting point, then validate the workload that matters to you.<\/p><\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">For adjacent context, see the <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-opus-5-vs-fable-5-vs-sonnet-5\/\">Claude family comparison<\/a>, <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-opus-5-review\/\">Opus 5 review<\/a>, \u0e41\u0e25\u0e30 <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-fable-5-1\/\">Fable 5.1 review<\/a>.<\/p>\n\n\n\n<h2 id=\"faq\" class=\"wp-block-heading\">\u0e04\u0e33\u0e16\u0e32\u0e21\u0e17\u0e35\u0e48\u0e1e\u0e1a\u0e1a\u0e48\u0e2d\u0e22<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Which model was faster in the matched test?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Sonnet 5.5 had the lower local total: 31.930 seconds versus 47.725 seconds for Opus 5.5 across five sequential requests. This includes the route and network path used here, so it is not provider-side latency.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Which model used fewer output tokens?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Sonnet 5.5 used 2,686 route-reported output tokens across the five tasks, compared with 3,952 for Opus 5.5. Token count alone does not prove answer quality.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Which model was cheaper in this run?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Using official standard API rates and the returned input\/output counts, the five Sonnet 5.5 requests estimate to $0.02826, versus $0.08184 for Opus 5.5. The calculation excludes cache charges, platform credits, tax, and markup.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Does Sonnet 5.5 beat Opus 5.5 on every benchmark?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No. Anthropic\u2019s own table is mixed by benchmark and effort setting: Sonnet 5.5 is higher on Terminal-Bench 4.0, while Opus 5.5 is higher on FrontierCode, CursorBench, GDPval-AA, Humanity\u2019s Last Exam, OSWorld, and Chartography. These are provider-reported results, not an independent rerun.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is Sonnet 5.5 a drop-in API replacement?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No. The migration guide says thinking runs by default, thinking: disabled returns a 400 error, forced tool choice any\/tool is rejected, and clients should parse content blocks by type. Re-baseline token budgets and tool loops before switching.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u0e19\u0e35\u0e48\u0e40\u0e1b\u0e47\u0e19\u0e01\u0e32\u0e23\u0e17\u0e14\u0e2a\u0e2d\u0e1a\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e\u0e43\u0e19\u0e1a\u0e23\u0e34\u0e1a\u0e17\u0e17\u0e35\u0e48\u0e01\u0e27\u0e49\u0e32\u0e07\u0e2b\u0e23\u0e37\u0e2d\u0e44\u0e21\u0e48?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No. The extraction prompt contains a short passage. It checks factual grounding and exact formatting; it does not measure behavior near the documented 1M-token context window.<\/p>\n\n\n\n<script type=\"application\/ld+json\">{\n    \"@context\": \"https:\\\/\\\/schema.org\",\n    \"@type\": \"FAQPage\",\n    \"mainEntity\": [\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Which model was faster in the matched test?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"Sonnet 5.5 had the lower local total: 31.930 seconds versus 47.725 seconds for Opus 5.5 across five sequential requests. 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These are provider-reported results, not an independent rerun.\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Is Sonnet 5.5 a drop-in API replacement?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"No. The migration guide says thinking runs by default, thinking: disabled returns a 400 error, forced tool choice any\\\/tool is rejected, and clients should parse content blocks by type. Re-baseline token budgets and tool loops before switching.\"\n            }\n        },\n        {\n            \"@type\": \"Question\",\n            \"name\": \"Was this a long-context benchmark?\",\n            \"acceptedAnswer\": {\n                \"@type\": \"Answer\",\n                \"text\": \"No. The extraction prompt contains a short passage. It checks factual grounding and exact formatting; it does not measure behavior near the documented 1M-token context window.\"\n            }\n        }\n    ]\n}<\/script>","protected":false},"excerpt":{"rendered":"<p>Claude 5.5 comparison \u00b7 matched API tasks \u00b7 October 1, 2026 Claude Sonnet 5.5 vs Opus 5.5: Price, Benchmarks, and Real Tests Sonnet 5.5 and Opus 5.5 share the Claude 5.5 generation, but they are priced and positioned differently. This comparison puts both through the same five prompts and reports what the route actually returned. [&hellip;]<\/p>","protected":false},"author":13,"featured_media":20295,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_seopress_robots_primary_cat":"","_seopress_titles_title":"Claude Sonnet 5.5 vs Opus 5.5: Price & Real Tests","_seopress_titles_desc":"Claude Sonnet 5.5 vs Opus 5.5: compare official prices, benchmark caveats, local response time, token use, and five same-prompt API tasks.","_seopress_robots_index":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-20285","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-chat"],"acf":[],"_links":{"self":[{"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/posts\/20285","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/comments?post=20285"}],"version-history":[{"count":3,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/posts\/20285\/revisions"}],"predecessor-version":[{"id":20337,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/posts\/20285\/revisions\/20337"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/media\/20295"}],"wp:attachment":[{"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/media?parent=20285"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/categories?post=20285"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/th\/wp-json\/wp\/v2\/tags?post=20285"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}