{"id":19790,"date":"2026-09-24T15:41:11","date_gmt":"2026-09-24T19:41:11","guid":{"rendered":"https:\/\/wp.glbgpt.com\/?p=19790"},"modified":"2026-09-24T15:41:13","modified_gmt":"2026-09-24T19:41:13","slug":"claude-opus-5-5-vs-gpt-6-sol","status":"publish","type":"post","link":"https:\/\/wp.glbgpt.com\/fr\/hub\/claude-opus-5-5-vs-gpt-6-sol","title":{"rendered":"Claude Opus 5.5 vs GPT-6 Sol: Which Model Should You Use?"},"content":{"rendered":"<p class=\"wp-block-paragraph\"><strong>R\u00e9ponse rapide :<\/strong> GPT-6 Sol completed the tested five-section brief under a 2,400-token output cap; Claude Opus 5.5 gave the more precise diagnosis in our short code-review task. Neither result establishes a permanent overall winner.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The useful comparison is broader than a score. Claude Opus 5.5 and GPT-6 Sol differ in how they handle writing, research synthesis, code review, structured responses, access routes, and day-to-day workflow. The task results below show where those differences matter.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_content_home&amp;login=1\">GlobalGPT<\/a> gives you both models in one affordable multi-model subscription, alongside other models and AI capabilities. One dashboard can carry a project from research and writing into coding, images, and video, while its CLI connects the same working context to terminal and production tools.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img alt=\"\" fetchpriority=\"high\" decoding=\"async\" width=\"2558\" height=\"1366\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/\u5fae\u4fe1\u622a\u56fe_20260923225247.jpg\" class=\"wp-image-19726\"\/><\/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-background wp-element-button\" href=\"https:\/\/www.glbgpt.com\/home\/gpt-6-sol?inviter=hub_corner_popup_gpt-6-sol&amp;login=1\" style=\"background:linear-gradient(135deg,rgb(6,147,227) 54%,rgb(155,81,224) 100%)\"><strong>Essayez GPT-6 Sol sur GlobalGPT<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<style>.copus-toc{box-sizing:border-box;max-width:100%;color:#203238;font:16px\/1.6 system-ui,sans-serif}.copus-toc a{color:#0b6372}.copus-toc ol{padding-left:24px}<\/style><nav class=\"copus-toc\" aria-label=\"Table des mati\u00e8res\" style=\"padding:18px;background:#f1f7f3;border:1px solid #d5e3dc;border-radius:7px\"><p><strong>Dans ce guide<\/strong><\/p><ol><li><a href=\"#quick-answer\">R\u00e9ponse rapide<\/a><\/li><li><a href=\"#what-each-model-does\">What each model is good at<\/a><\/li><li><a href=\"#capability-comparison\">Feature and capability comparison<\/a><\/li><li><a href=\"#writing-research\">R\u00e9daction et recherche<\/a><\/li><li><a href=\"#coding-structured\">Coding and structured output<\/a><\/li><li><a href=\"#price-access\">Price and access<\/a><\/li><li><a href=\"#api-calculator\">Estimate direct API cost<\/a><\/li><li><a href=\"#who-should-choose\">Who should choose which model<\/a><\/li><li><a href=\"#globalgpt-workflow\">A complete GlobalGPT workflow<\/a><\/li><li><a href=\"#method\">How the comparison was run<\/a><\/li><li><a href=\"#t01-boundary\">Why the first prompt did not pick a winner<\/a><\/li><li><a href=\"#t01r\">R\u00e9daction longue<\/a><\/li><li><a href=\"#t02\">Source synthesis<\/a><\/li><li><a href=\"#t03\">Coding and structured output<\/a><\/li><li><a href=\"#verdict\">Verdict final<\/a><\/li><li><a href=\"#faq\">Foire aux questions<\/a><\/li><\/ol><\/nav>\n\n\n\n<h2 id=\"quick-answer\" class=\"wp-block-heading\">R\u00e9ponse rapide<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Choose GPT-6 Sol<\/strong> first for the constrained writing workflow tested here; it completed all five sections within the shared 2,400-token output cap. <strong>Choose Claude Opus 5.5<\/strong> when diagnosis, explicit risk analysis, and strict JSON matter more. Use both when the work changes shape during the day.<\/p>\n\n\n\n<h2 id=\"what-each-model-does\" class=\"wp-block-heading\">What each model is good at<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Claude Opus 5.5<\/strong> fits deliberate work: reviewing a code change, teasing apart a messy requirement, naming risks, and preserving a strict output contract. Anthropic positions it around coding, computer use, knowledge work, safety, and more natural long-session communication. See the <a href=\"https:\/\/www.glbgpt.com\/hub\/claude-opus-5-5-review\/\">Claude Opus 5.5 review<\/a> for a separate model-focused view. It is a natural choice when a careful second pass is worth more than a shorter first answer.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>GPT-6 Sol<\/strong> fits throughput with constraints: a long brief, a decision memo, a structured plan, or a draft that must include many requested sections. OpenAI describes it as a more affordable GPT-6 model for professional work, factuality, coding, computer use, and collaboration. The <a href=\"https:\/\/www.glbgpt.com\/hub\/gpt-6-sol-review\/\">GPT-6 Sol review<\/a> covers the model in its own right; in the long-form task here it completed the contract in the first output, which is useful when the cost of a missing section is high.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"740\" height=\"650\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/anthropic-opus-5-5-capabilities-verified_a913f7bbf5114691950289d91d24d7a7.webp\" alt=\"Claude Opus 5.5 official introduction and performance discussion\" class=\"wp-image-19796\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/anthropic-opus-5-5-capabilities-verified_a913f7bbf5114691950289d91d24d7a7.webp 740w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/anthropic-opus-5-5-capabilities-verified_a913f7bbf5114691950289d91d24d7a7-300x264.webp 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/anthropic-opus-5-5-capabilities-verified_a913f7bbf5114691950289d91d24d7a7-14x12.webp 14w\" sizes=\"(max-width: 740px) 100vw, 740px\" \/><figcaption class=\"wp-element-caption\">Anthropic describes Opus 5.5\u2019s performance and coding improvements in its release announcement.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">This is Anthropic\u2019s own account of the release, including examples from early testers. It supports the product\u2019s intended direction; it does not establish a head-to-head result against GPT-6 Sol. Our matched prompts below address the narrower question of what each model returned in this run.<\/p>\n\n\n\n<h2 id=\"capability-comparison\" class=\"wp-block-heading\">Feature and capability comparison<\/h2>\n\n\n\n<style>.copus-portable-comparison{box-sizing:border-box;max-width:100%;color:#203238;font:16px\/1.6 system-ui,sans-serif;overflow-wrap:anywhere}.copus-portable-comparison *{box-sizing:border-box}.copus-portable-comparison h3{font-size:24px;line-height:1.3}.copus-portable-comparison a{color:#0b6372}@media(max-width:640px){.copus-portable-comparison{padding:17px!important}}<\/style><section class=\"copus-portable-comparison\" style=\"margin:26px 0;padding:22px;border:1px solid #cfddd6;border-radius:8px;background:#f7faf8\"><h3 style=\"margin-top:0;color:#143e49\">Claude Opus 5.5 vs GPT-6 Sol: capability map<\/h3><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;min-width:720px;border-collapse:collapse;background:#fff;border:1px solid #d8e2dd\"><thead><tr><th style=\"padding:11px;text-align:left;background:#e4efeb;border-bottom:1px solid #c9d9d3\">Domaine de d\u00e9cision<\/th><th style=\"padding:11px;text-align:left;background:#e4efeb;border-bottom:1px solid #c9d9d3\">Claude Opus 5.5<\/th><th style=\"padding:11px;text-align:left;background:#e4efeb;border-bottom:1px solid #c9d9d3\">GPT-6 Sol<\/th><\/tr><\/thead><tbody><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">R\u00e9daction longue<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Strong at careful, nuanced drafting and explicit caveats<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Strong at completing a long, multi-constraint brief in one pass<\/td><\/tr><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Synth\u00e8se de recherche<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Detailed reconciliation and risk explanation<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Compact synthesis with a clear recommendation<\/td><\/tr><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Coding and review<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Precise diagnosis and structured risk reporting<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Useful minimal patches; validate edge-case explanations<\/td><\/tr><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Sortie structur\u00e9e<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Reliable strict JSON in the tested task<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Valid JSON with all requested keys in the tested task<\/td><\/tr><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Multimodal and tools<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Check the route and model access for the specific task<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Check the route and model access for the specific task<\/td><\/tr><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Best working style<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Deliberate review, complex reasoning, and risk-sensitive work<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Completed the tested constrained brief under the fixed output cap<\/td><\/tr><\/tbody><\/table><\/div><p style=\"margin-bottom:0\"><strong>Comment lire ce texte :<\/strong> these are workflow tendencies, not a universal leaderboard. The right choice depends on whether you value deliberate review or completion of this dense brief under the same output cap.<\/p><\/section>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"850\" height=\"718\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-automationbench-verified_8e4859ba143c4f5fada1abca0b22e42a.webp\" alt=\"OpenAI AutomationBench chart with model versions and cost-per-task axes\" class=\"wp-image-19794\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-automationbench-verified_8e4859ba143c4f5fada1abca0b22e42a.webp 850w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-automationbench-verified_8e4859ba143c4f5fada1abca0b22e42a-300x253.webp 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-automationbench-verified_8e4859ba143c4f5fada1abca0b22e42a-768x649.webp 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-automationbench-verified_8e4859ba143c4f5fada1abca0b22e42a-14x12.webp 14w\" sizes=\"(max-width: 850px) 100vw, 850px\" \/><figcaption class=\"wp-element-caption\">OpenAI\u2019s AutomationBench chart plots task score against cost, with each model version shown in the legend.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Read both axes: moving upward indicates a higher score, while moving left indicates lower cost per task. The Claude series here is Opus 5, not Opus 5.5. This publisher-reported chart helps explain Sol\u2019s cost-efficiency positioning; it cannot settle the comparison in this article. The footnote also says the Fable datapoint omits some fallback costs.<\/p>\n\n\n\n<h2 id=\"writing-research\" class=\"wp-block-heading\">Writing, long documents, and research synthesis<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">For writing, this test distinguishes completion under a fixed output cap from the quality of editorial scrutiny. GPT-6 Sol made the stronger showing on the long-form contract. Claude Opus 5.5 made the stronger showing when the job was to diagnose, qualify, and explain. For research notes, both models reached the same practical resolution, so the quality of the source material and review process still matter more than a headline winner.<\/p>\n\n\n\n<ul><li>Use GPT-6 Sol for a first draft with many constraints, a decision memo, or a structured content plan.<\/li><li>Use Claude Opus 5.5 to challenge assumptions, surface risks, and tighten a draft before it reaches a client or production system.<\/li><li>When facts matter, give either model the source material and keep a human review step.<\/li><\/ul>\n\n\n\n<h2 id=\"coding-structured\" class=\"wp-block-heading\">Coding, structured output, and tool-ready work<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Both models returned valid JSON with all requested keys and the same minimal patch. Claude described the existing filter correctly; GPT-6 Sol incorrectly said a None element would raise an error. That supports Claude for diagnosis in this particular task. The risk-field type was not specified in the prompt, so Sol\u2019s risk string is not a contract violation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For tool-ready work, judge the output contract you actually need: valid JSON shape, complete fields, reproducible commands, and a patch that can be applied without interpretation. Both models can be part of that pipeline, with tests and review kept in the loop.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"850\" height=\"675\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-frontiercode-verified_a2b8ed5c277f4d4e841cf9f008e409fd.webp\" alt=\"OpenAI FrontierCode chart including model labels and mergeability methodology\" class=\"wp-image-19795\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-frontiercode-verified_a2b8ed5c277f4d4e841cf9f008e409fd.webp 850w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-frontiercode-verified_a2b8ed5c277f4d4e841cf9f008e409fd-300x238.webp 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-frontiercode-verified_a2b8ed5c277f4d4e841cf9f008e409fd-768x610.webp 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-frontiercode-verified_a2b8ed5c277f4d4e841cf9f008e409fd-15x12.webp 15w\" sizes=\"(max-width: 850px) 100vw, 850px\" \/><figcaption class=\"wp-element-caption\">FrontierCode evaluates correctness and mergeability, including tests, scope and code style.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">This benchmark covers broader agentic coding than our short code-review prompt. Its Claude comparator is Opus 5, so it is context for Sol\u2019s coding positioning rather than evidence that Sol beats Opus 5.5. In our own task, both patches were correct; the useful distinction was the accuracy of the diagnosis.<\/p>\n\n\n\n<h2 id=\"price-access\" class=\"wp-block-heading\">Price and access<\/h2>\n\n\n\n<style>.copus-portable-price{box-sizing:border-box;max-width:100%;color:#203238;font:16px\/1.6 system-ui,sans-serif;overflow-wrap:anywhere}.copus-portable-price *{box-sizing:border-box}.copus-portable-price h3{font-size:24px;line-height:1.3}.copus-portable-price a{color:#0b6372}@media(max-width:640px){.copus-portable-price{padding:17px!important}}<\/style><section class=\"copus-portable-price\" style=\"margin:26px 0;padding:22px;border:1px solid #cfddd6;border-radius:8px;background:#f7faf8\"><h3 style=\"margin-top:0;color:#143e49\">Price and access: compare the route, not only the model<\/h3><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;min-width:720px;border-collapse:collapse;background:#fff;border:1px solid #d8e2dd\"><thead><tr><th style=\"padding:11px;text-align:left;background:#e4efeb;border-bottom:1px solid #c9d9d3\">Itin\u00e9raire<\/th><th style=\"padding:11px;text-align:left;background:#e4efeb;border-bottom:1px solid #c9d9d3\">Ce pour quoi vous payez<\/th><th style=\"padding:11px;text-align:left;background:#e4efeb;border-bottom:1px solid #c9d9d3\">Quand cela s'av\u00e8re judicieux<\/th><\/tr><\/thead><tbody><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Claude Opus 5.5 API<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Anthropic lists $4 per million input tokens, $20 per million output tokens, and $0.20 per million cache-read tokens on its September 22, 2026 release page<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">You are building a product or automated pipeline and want direct token billing<\/td><\/tr><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">GPT-6 Sol API<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">OpenAI lists $2 per million input tokens and $10 per million output tokens, with a 50% reduction from GPT-5.6 Sol promotional pricing; cached input reads receive a 90% discount<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">You need a direct provider API contract and want lower metered cost for sustained work<\/td><\/tr><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">GlobalGPT<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">One affordable subscription with both models plus many other models and AI functions<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">You want research, writing, coding, image, and video work in one dashboard<\/td><\/tr><\/tbody><\/table><\/div><p>These figures are API token rates, not subscription prices. OpenAI positions GPT-6 Sol as the lower-cost member of the GPT-6 family for professional work, factuality, coding, computer use, and collaboration, while Anthropic positions Claude Opus 5.5 around coding, computer use, knowledge work, safety, and communication. GlobalGPT keeps both models and the rest of the toolchain available through one affordable subscription, while its CLI connects that access to a terminal, development workflow, and existing production tools.<\/p><p style=\"font-size:14px;color:#52686c\">Sources primaires : <a href=\"https:\/\/www.anthropic.com\/claude-opus-5-5\">Anthropic&#8217;s Claude Opus 5.5 release page<\/a> et <a href=\"https:\/\/openai.com\/index\/introducing-gpt-6-sol-and-luna\/\">OpenAI&#8217;s GPT-6 Sol and Luna release page<\/a>.<\/p><\/section>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1440\" height=\"900\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-api-pricing-official_82a665171d654628b1d64c0f36883037.webp\" alt=\"OpenAI GPT-6 Sol input and output API rates per million tokens\" class=\"wp-image-19797\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-api-pricing-official_82a665171d654628b1d64c0f36883037.webp 1440w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-api-pricing-official_82a665171d654628b1d64c0f36883037-300x188.webp 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-api-pricing-official_82a665171d654628b1d64c0f36883037-1024x640.webp 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-api-pricing-official_82a665171d654628b1d64c0f36883037-768x480.webp 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/openai-gpt-6-sol-api-pricing-official_82a665171d654628b1d64c0f36883037-18x12.webp 18w\" sizes=\"(max-width: 1440px) 100vw, 1440px\" \/><figcaption class=\"wp-element-caption\">OpenAI lists GPT-6 Sol at $2 per million input tokens and $10 per million output tokens.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">These rates apply to API tokens. They do not quote a monthly consumer subscription or a GlobalGPT plan.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1440\" height=\"900\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/anthropic-opus-5-5-api-pricing-verified_20f22371fab44cbd809cf39c75353f9b.webp\" alt=\"Claude Opus 5.5 API input, output and prompt-cache rates\" class=\"wp-image-19793\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/anthropic-opus-5-5-api-pricing-verified_20f22371fab44cbd809cf39c75353f9b.webp 1440w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/anthropic-opus-5-5-api-pricing-verified_20f22371fab44cbd809cf39c75353f9b-300x188.webp 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/anthropic-opus-5-5-api-pricing-verified_20f22371fab44cbd809cf39c75353f9b-1024x640.webp 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/anthropic-opus-5-5-api-pricing-verified_20f22371fab44cbd809cf39c75353f9b-768x480.webp 768w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/09\/anthropic-opus-5-5-api-pricing-verified_20f22371fab44cbd809cf39c75353f9b-18x12.webp 18w\" sizes=\"(max-width: 1440px) 100vw, 1440px\" \/><figcaption class=\"wp-element-caption\">Anthropic lists Opus 5.5 at $4\/M input, $20\/M output and $0.20\/M cache reads.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Opus 5.5\u2019s standard input and output rates are twice Sol\u2019s, while both published cached-read rates are $0.20\/M. A heavily cached workflow can therefore have a different cost gap from an uncached one; compare the same token mix before choosing on price.<\/p>\n\n\n\n<style>.evidence-api-cost-comparison{box-sizing:border-box;max-width:100%;margin:28px 0;padding:22px;border:1px solid #cadbd5;border-radius:12px;background:#f4f9f7;color:#173b3b;font:16px\/1.6 system-ui,sans-serif}.evidence-api-cost-comparison *{box-sizing:border-box}.evidence-api-cost-comparison h3{margin:0 0 14px;color:#143c40;font-size:24px;line-height:1.3}.evidence-api-cost-comparison p,.evidence-api-cost-comparison li{color:#25474b}.evidence-api-cost-comparison .e-row{margin:18px 0}.evidence-api-cost-comparison .e-label{display:flex;justify-content:space-between;gap:12px;flex-wrap:wrap;font-weight:700}.evidence-api-cost-comparison .e-track{height:25px;border-radius:4px;background:#e0eae7;margin:8px 0;overflow:hidden}.evidence-api-cost-comparison .e-stack{display:flex;height:100%}.evidence-api-cost-comparison .e-in{display:block;height:100%;background:#176e75}.evidence-api-cost-comparison .e-out{display:block;height:100%;background:#b9643d}.evidence-api-cost-comparison small{display:block;color:#355a60;font-size:14px}.evidence-api-cost-comparison pre{white-space:pre-wrap;overflow-wrap:anywhere;background:#17383e;color:#f6fcff;padding:16px;border-radius:6px}.evidence-api-cost-comparison table{width:100%;border-collapse:collapse;font-size:14px}.evidence-api-cost-comparison th,.evidence-api-cost-comparison td{text-align:left;vertical-align:top;padding:10px;border-bottom:1px solid #c9dad3}.evidence-api-cost-comparison th{color:#143c40;background:#e2efea}.evidence-api-cost-comparison .e-scroll{overflow-x:auto}.evidence-api-cost-comparison a{color:#075a83}@media(max-width:600px){.evidence-api-cost-comparison{padding:16px}.evidence-api-cost-comparison h3{font-size:21px}}<\/style><section class=\"evidence-api-cost-comparison\" aria-label=\"What the rate difference means for the same workload\"><h3>What the rate difference means for the same workload<\/h3><p>Calculated example: <strong>1 million uncached input tokens + 100,000 output tokens<\/strong>, standard API rates. Teal is input cost; rust is output cost.<\/p><div class=\"e-row\"><div class=\"e-label\"><span>Claude Opus 5.5<\/span><span>$6.00<\/span><\/div><div class=\"e-track\"><span class=\"e-stack\" style=\"width:100%\"><span class=\"e-in\" style=\"width:66.6667%\"><\/span><span class=\"e-out\" style=\"width:33.333299999999994%\"><\/span><\/span><\/div><small>$4 input + $2 output<\/small><\/div><div class=\"e-row\"><div class=\"e-label\"><span>GPT-6 Sol<\/span><span>$3.00<\/span><\/div><div class=\"e-track\"><span class=\"e-stack\" style=\"width:50%\"><span class=\"e-in\" style=\"width:66.6667%\"><\/span><span class=\"e-out\" style=\"width:33.333299999999994%\"><\/span><\/span><\/div><small>$2 input + $1 output<\/small><\/div><p>Sol costs half as much for this identical token mix. That is a rate-card calculation, not a claim that every completed task costs half as much: retries, output length, cache hits, tool charges and reasoning settings can change the bill.<\/p><small>Sources : <a href=\"https:\/\/claude.com\/pricing#api\">Anthropic API pricing<\/a> et <a href=\"https:\/\/openai.com\/index\/introducing-gpt-6-sol-and-luna\/\">OpenAI release pricing<\/a>, checked September 25, 2026.<\/small><\/section>\n\n\n\n<h2 id=\"api-calculator\" class=\"wp-block-heading\">Estimate direct API cost<\/h2>\n\n\n\n<section class=\"copus-module-api-calculator\" aria-label=\"Calculateur de co\u00fbts API\">\n<style>\n.copus-module-api-calculator{box-sizing:border-box;margin:26px 0;padding:22px;border:1px solid #cfddd6;border-radius:8px;background:#f7faf8;color:#203238;font:16px\/1.6 -apple-system,BlinkMacSystemFont,\"Segoe UI\",sans-serif}\n.copus-module-api-calculator *{box-sizing:border-box}.copus-module-api-calculator h3{margin:0 0 8px;color:#143e49;font:700 24px\/1.25 Georgia,serif}.copus-module-api-calculator p{margin:8px 0;color:#31474c}.copus-module-api-calculator .calc-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:14px;margin:18px 0}.copus-module-api-calculator label{display:block;color:#174f58;font-weight:700;font-size:14px}.copus-module-api-calculator input{display:block;width:100%;margin-top:6px;padding:10px 11px;border:1px solid #b9cec6;border-radius:5px;background:#fff;color:#203238;font:16px\/1.3 ui-monospace,SFMono-Regular,Menlo,monospace}.copus-module-api-calculator table{width:100%;border-collapse:collapse;background:#fff}.copus-module-api-calculator th,.copus-module-api-calculator td{padding:10px 11px;text-align:left;border-bottom:1px solid #dce5e1;vertical-align:top}.copus-module-api-calculator th{background:#e4efeb;color:#163f46}.copus-module-api-calculator .calc-note{color:#52686c;font-size:13px}.copus-module-api-calculator .calc-total{font-weight:750;color:#143e49;overflow-wrap:anywhere}.copus-module-api-calculator .calc-error{color:#a02727;font-weight:700}@media(max-width:640px){.copus-module-api-calculator{padding:17px;margin:22px 0}.copus-module-api-calculator .calc-grid{grid-template-columns:1fr}.copus-module-api-calculator table{font-size:14px}}\n<\/style>\n<h3>Calculateur de co\u00fbts API<\/h3>\n<p>Enter the tokens you expect to send and receive. The estimate uses the verified API rates above: Claude Opus 5.5 at $4\/M input and $20\/M output; GPT-6 Sol at $2\/M input and $10\/M output.<\/p>\n<div class=\"calc-grid\"><label>Jetons d'entr\u00e9e<input class=\"calc-input-tokens\" type=\"number\" min=\"0\" step=\"1\" value=\"1000000\" inputmode=\"numeric\"><\/label><label>Jetons de sortie<input class=\"calc-output-tokens\" type=\"number\" min=\"0\" step=\"1\" value=\"250000\" inputmode=\"numeric\"><\/label><\/div>\n<div class=\"calc-results\" role=\"status\" aria-live=\"polite\"><table><thead><tr><th>Mod\u00e8le<\/th><th>Input rate<\/th><th>Output rate<\/th><th>Co\u00fbt estim\u00e9<\/th><\/tr><\/thead><tbody><tr><td>Claude Opus 5.5<\/td><td>$4 \/ M<\/td><td>$20 \/ M<\/td><td class=\"calc-claude calc-total\">$9.00<\/td><\/tr><tr><td>GPT-6 Sol<\/td><td>$2 \/ M<\/td><td>$10 \/ M<\/td><td class=\"calc-gpt calc-total\">$4.50<\/td><\/tr><\/tbody><\/table><\/div>\n<p class=\"calc-error\" role=\"alert\"><\/p>\n<p class=\"calc-note\">This is a token-billing estimate for direct API use. It does not calculate a consumer subscription or GlobalGPT plan price, and it excludes cache discounts, tools, taxes, and other provider-specific charges.<\/p>\n<script>(function(){var root=document.currentScript&&document.currentScript.closest('.copus-module-api-calculator');if(!root)return;var input=root.querySelector('.calc-input-tokens'),output=root.querySelector('.calc-output-tokens'),claude=root.querySelector('.calc-claude'),gpt=root.querySelector('.calc-gpt');function n(el){var v=Number(el.value);if(el.value.trim()===''||!Number.isSafeInteger(v)||v<0)throw new Error('Enter non-negative whole token counts, up to 9,007,199,254,740,991.');return v}function money(v){return new Intl.NumberFormat('en-US',{style:'currency',currency:'USD',maximumFractionDigits:8}).format(v)}function update(){var error=root.querySelector('.calc-error');try{var i=n(input)\/1000000,o=n(output)\/1000000;claude.textContent=money(i*4+o*20);gpt.textContent=money(i*2+o*10);error.textContent=''}catch(e){error.textContent=e.message;claude.textContent=gpt.textContent='\u2014'}}input.addEventListener('input',update);output.addEventListener('input',update);update()})();<\/script>\n<\/section>\n\n\n\n<h2 id=\"who-should-choose\" class=\"wp-block-heading\">Who should choose which model?<\/h2>\n\n\n\n<style>.copus-portable-choice{box-sizing:border-box;max-width:100%;color:#203238;font:16px\/1.6 system-ui,sans-serif;overflow-wrap:anywhere}.copus-portable-choice *{box-sizing:border-box}.copus-portable-choice h3{font-size:24px;line-height:1.3}.copus-portable-choice a{color:#0b6372}@media(max-width:640px){.copus-portable-choice{padding:17px!important}}<\/style><section class=\"copus-portable-choice\" style=\"margin:26px 0;padding:22px;border:1px solid #cfddd6;border-radius:8px;background:#f7faf8\"><h3 style=\"margin-top:0;color:#143e49\">Quel mod\u00e8le choisir ?<\/h3><div style=\"overflow-x:auto;margin:20px 0\"><table style=\"width:100%;min-width:720px;border-collapse:collapse;background:#fff;border:1px solid #d8e2dd\"><thead><tr><th style=\"padding:11px;text-align:left;background:#e4efeb;border-bottom:1px solid #c9d9d3\">Votre priorit\u00e9<\/th><th style=\"padding:11px;text-align:left;background:#e4efeb;border-bottom:1px solid #c9d9d3\">Pick first<\/th><th style=\"padding:11px;text-align:left;background:#e4efeb;border-bottom:1px solid #c9d9d3\">Pourquoi<\/th><\/tr><\/thead><tbody><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">A polished long memo with many constraints<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">GPT-6 Sol<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">The revised long-form task reached the requested decision rule in one output<\/td><\/tr><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Code diagnosis, strict JSON, and explicit risks<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Claude Opus 5.5<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">The coding task produced the more exact diagnosis and risk inventory<\/td><\/tr><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Research notes that need reconciliation<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Soit<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">The controlled source-synthesis task was a tie<\/td><\/tr><tr><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">A mixed weekly workflow<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">GlobalGPT with both<\/td><td style=\"padding:11px;vertical-align:top;border-bottom:1px solid #e3ebe7\">Keep model choice, writing, coding, images, and video in one dashboard; use CLI when work moves to a terminal<\/td><\/tr><\/tbody><\/table><\/div><\/section>\n\n\n\n<h2 id=\"globalgpt-workflow\" class=\"wp-block-heading\">Use both models in one complete workflow<\/h2>\n\n\n\n<style>.copus-portable-workflow{box-sizing:border-box;max-width:100%;color:#203238;font:16px\/1.6 system-ui,sans-serif;overflow-wrap:anywhere}.copus-portable-workflow *{box-sizing:border-box}.copus-portable-workflow h3{font-size:24px;line-height:1.3}.copus-portable-workflow a{color:#0b6372}@media(max-width:640px){.copus-portable-workflow{padding:17px!important}}<\/style><section class=\"copus-portable-workflow\" style=\"margin:26px 0;padding:22px;border:1px solid #cfddd6;border-radius:8px;background:#f7faf8\"><h3 style=\"margin-top:0;color:#143e49\">Un processus pratique pour le GlobalGPT<\/h3><ol><li>Start with a research brief and ask one model to expose assumptions.<\/li><li>Move the cleaned brief into the other model for drafting or a second opinion.<\/li><li>Use the stronger coding route for implementation, review, and structured output.<\/li><li>Keep the resulting work in the same dashboard, then use the <a href=\"https:\/\/www.glbgpt.com\/hub\/how-to-use-globalgpt-cli-in-claude-code\/\">GlobalGPT CLI workflow<\/a> when it needs to enter a terminal or production tool.<\/li><\/ol><p style=\"margin-bottom:0\">That workflow is the main value of access to both models: you can route the task, rather than rewrite your process around one model.<\/p><\/section>\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 loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"645\" src=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/glb-1024x645.png\" alt=\"Tableau de bord GlobalGPT\" class=\"wp-image-19280\" srcset=\"https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/glb-1024x645.png 1024w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/glb-300x189.png 300w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/glb-18x11.png 18w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/glb-767x483.png 767w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/glb-1536x967.png 1536w, https:\/\/wp.glbgpt.com\/wp-content\/uploads\/2026\/07\/glb-2048x1289.png 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-3e41869c wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-black-color has-text-color has-background has-link-color wp-element-button\" href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_popup&amp;login=1\" style=\"background:linear-gradient(135deg,rgba(250,183,0,0.55) 0%,rgba(255,106,0,0.66) 96%)\"><strong>Essayez plus de 100 mod\u00e8les phares sur GlobalGPT<\/strong><\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<h2 id=\"method\" class=\"wp-block-heading\">How the comparison was run<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Both models received the same system message, task inputs, source material, and no tools or browsing. Each result retains the first returned output, including output-limit truncation. This is a practical comparison for choosing a workflow, not a universal ranking of every capability.<\/p>\n\n\n\n<style>\n.copus-module-setup{box-sizing:border-box;margin:30px 0;padding:24px;border:1px solid #d8dedb;border-radius:7px;background:#f7faf8;color:#203238;font:16px\/1.65 -apple-system,BlinkMacSystemFont,'Segoe UI',sans-serif;overflow-wrap:break-word}\n.copus-module-setup *{box-sizing:border-box} .copus-module-setup h3{margin:0 0 12px;color:#143e49;font:700 25px\/1.25 Georgia,serif} .copus-module-setup h4{margin:20px 0 8px;color:#174f58;font-size:18px} .copus-module-setup p,.copus-module-setup li{color:#263a40} .copus-module-setup .eyebrow{margin:0 0 7px;color:#9a513d;font-size:12px;font-weight:750;letter-spacing:.08em;text-transform:uppercase} .copus-module-setup .metrics{display:grid;grid-template-columns:repeat(auto-fit,minmax(145px,1fr));gap:10px;margin:17px 0} .copus-module-setup .metric{padding:13px;background:#fff;border-left:3px solid #b65d45} .copus-module-setup .metric strong{display:block;color:#153b47;font-size:25px;line-height:1.2} .copus-module-setup .metric span{color:#4b6065;font-size:13px} .copus-module-setup .scroll{max-width:100%;overflow-x:auto} .copus-module-setup table{width:100%;min-width:620px;border-collapse:collapse;background:#fff;font-size:14px} .copus-module-setup th,.copus-module-setup td{padding:10px 11px;text-align:left;vertical-align:top;border-bottom:1px solid #dce5e1} .copus-module-setup th{background:#e4efeb;color:#163f46} .copus-module-setup details{margin:11px 0;border:1px solid #c9d9d3;border-radius:5px;background:#fff} .copus-module-setup summary{padding:12px 14px;color:#155664;cursor:pointer;font-weight:700} .copus-module-setup pre{max-height:520px;margin:0;padding:14px;border-top:1px solid #dce5e1;background:#fbfcfb;color:#263b42;white-space:pre-wrap;overflow:auto;font:13px\/1.65 ui-monospace,SFMono-Regular,Menlo,monospace} .copus-module-setup .note{color:#52686c;font-size:14px} .copus-module-setup .warning{padding:13px;background:#fff3df;border-left:3px solid #b76a2f;color:#5a3c21} @media(max-width:640px){.copus-module-setup{padding:17px;margin:22px 0;font-size:15px} .copus-module-setup h3{font-size:22px} .copus-module-setup table{min-width:600px} .copus-module-setup pre{max-height:440px;font-size:12px}}\n<\/style><section class=\"copus-module-setup\"><p class=\"eyebrow\">Comparison method<\/p><h3>How this comparison was run<\/h3><div class=\"metrics\"><div class=\"metric\"><strong>3<\/strong><span>formal workflows<\/span><\/div><div class=\"metric\"><strong>2<\/strong><span>mod\u00e8les<\/span><\/div><div class=\"metric\"><strong>1<\/strong><span>first output per task<\/span><\/div><\/div><div class=\"scroll\"><table><thead><tr><th>\u00c9tat<\/th><th>Comparison rule<\/th><\/tr><\/thead><tbody><tr><td>Mod\u00e8les<\/td><td>Claude Opus 5.5 and GPT-6 Sol, exact IDs confirmed in the live model directory<\/td><\/tr><tr><td>Prompt parity<\/td><td>Same system message, task prompt, source material, no tools or browsing<\/td><\/tr><tr><td>T\u00e2ches<\/td><td>T01 boundary check, T01R explicit long-form comparison, T02 source synthesis, T03 code\/JSON<\/td><\/tr><tr><td>Nouvelles tentatives<\/td><td>Only transport\/capacity\/empty-envelope failures; valid weak output is retained<\/td><\/tr><tr><td>Marquage<\/td><td>10 points per scored task: objective contract 4, traceability 3, usefulness 2, format 1<\/td><\/tr><\/tbody><\/table><\/div><p class=\"note\">The first prompt checks whether a model will guess when key context is missing, so it is kept separate from the winner calculation. The direct comparison names both models and gives them the same brief. No stability reruns were used.<\/p><\/section>\n\n\n\n<h2 id=\"t01-boundary\" class=\"wp-block-heading\">Why the first prompt did not pick a winner<\/h2>\n\n\n\n<style>\n.copus-module-t01-boundary{box-sizing:border-box;margin:30px 0;padding:24px;border:1px solid #d8dedb;border-radius:7px;background:#f7faf8;color:#203238;font:16px\/1.65 -apple-system,BlinkMacSystemFont,'Segoe UI',sans-serif;overflow-wrap:break-word}\n.copus-module-t01-boundary *{box-sizing:border-box} .copus-module-t01-boundary h3{margin:0 0 12px;color:#143e49;font:700 25px\/1.25 Georgia,serif} .copus-module-t01-boundary h4{margin:20px 0 8px;color:#174f58;font-size:18px} .copus-module-t01-boundary p,.copus-module-t01-boundary li{color:#263a40} .copus-module-t01-boundary .eyebrow{margin:0 0 7px;color:#9a513d;font-size:12px;font-weight:750;letter-spacing:.08em;text-transform:uppercase} .copus-module-t01-boundary .metrics{display:grid;grid-template-columns:repeat(auto-fit,minmax(145px,1fr));gap:10px;margin:17px 0} .copus-module-t01-boundary .metric{padding:13px;background:#fff;border-left:3px solid #b65d45} .copus-module-t01-boundary .metric strong{display:block;color:#153b47;font-size:25px;line-height:1.2} .copus-module-t01-boundary .metric span{color:#4b6065;font-size:13px} .copus-module-t01-boundary .scroll{max-width:100%;overflow-x:auto} .copus-module-t01-boundary table{width:100%;min-width:620px;border-collapse:collapse;background:#fff;font-size:14px} .copus-module-t01-boundary th,.copus-module-t01-boundary td{padding:10px 11px;text-align:left;vertical-align:top;border-bottom:1px solid #dce5e1} .copus-module-t01-boundary th{background:#e4efeb;color:#163f46} .copus-module-t01-boundary details{margin:11px 0;border:1px solid #c9d9d3;border-radius:5px;background:#fff} .copus-module-t01-boundary summary{padding:12px 14px;color:#155664;cursor:pointer;font-weight:700} .copus-module-t01-boundary pre{max-height:520px;margin:0;padding:14px;border-top:1px solid #dce5e1;background:#fbfcfb;color:#263b42;white-space:pre-wrap;overflow:auto;font:13px\/1.65 ui-monospace,SFMono-Regular,Menlo,monospace} .copus-module-t01-boundary .note{color:#52686c;font-size:14px} .copus-module-t01-boundary .warning{padding:13px;background:#fff3df;border-left:3px solid #b76a2f;color:#5a3c21} @media(max-width:640px){.copus-module-t01-boundary{padding:17px;margin:22px 0;font-size:15px} .copus-module-t01-boundary h3{font-size:22px} .copus-module-t01-boundary table{min-width:600px} .copus-module-t01-boundary pre{max-height:440px;font-size:12px}}\n<\/style><section class=\"copus-module-t01-boundary\"><p class=\"eyebrow\">Context handling<\/p><h3>Why the first prompt did not pick a winner<\/h3><p>Both models refused to invent model identities because the original prompt did not name them. That is the right response to missing context, but it cannot tell us which model is better at the work itself.<\/p><div class=\"metrics\"><div class=\"metric\"><strong>S\u00e9parer<\/strong><span>from the core score<\/span><\/div><div class=\"metric\"><strong>2 \/ 2<\/strong><span>correctly asked for context<\/span><\/div><\/div><details class=\"\"><summary>Claude Opus 5.5 \u00b7 full input<\/summary><pre>You are comparing two AI models for a practical buyer guide. Using only the source packet below, write 700\u2013900 words with exactly these headings: Decision, Evidence, Caveats, Next step. Preserve these facts exactly: Project North has a 12-week deadline; the migration budget is $48,000; the security review is not scheduled. If a fact is unresolved, say so explicitly. Do not invent prices, benchmarks, or product capabilities.\n\nSOURCE PACKET:\nProject North is a 12-week migration for a 40-person team. The approved migration budget is $48,000. Security review timing has not been scheduled. The team wants a model recommendation for research, writing, and code review. A stakeholder note says the current draft is too long, but gives no word count.\n\nReturn only the guide.<\/pre><\/details><details class=\"\"><summary>Claude Opus 5.5 \u00b7 full output<\/summary><pre>## Decision\n\nNo recommendation between the two models can be made from the supplied source packet. The packet does not name either model or give any prices, benchmark results, or product capabilities. Choosing one model over the other now would mean inventing the evidence this guide is supposed to rely on.\n\nThe practical decision is to hold the model selection. Treat the next phase as a structured evaluation governed by three fixed constraints:\n\n- Project North has a 12-week deadline.\n- The migration budget is $48,000.\n- The security review is not scheduled.\n\nAny model that goes forward must fit inside the 12-week deadline and the $48,000 migration budget. It must also pass a security review, and that review is not yet on the calendar. Until the two models are identified and assessed against the team&#x27;s three stated uses (research, writing, and code review), the selection remains unresolved. This guide says so plainly rather than offering a preference the evidence cannot support.\n\n## Evidence\n\nThe source packet contains the following facts, and only these facts inform this guide:\n\n- **Scope and timing:** Project North is a 12-week migration.\n- **Team size:** The migration serves a 40-person team.\n- **Budget:** The approved migration budget is $48,000.\n- **Security:** Security review timing has not been scheduled.\n- **Use cases:** The team wants a model recommendation for research, writing, and code review.\n- **Stakeholder feedback:** A stakeholder note says the current draft is too long, but it gives no word count.\n\nThe packet does not contain several things a comparison would normally rest on. It does not identify the two models being compared. It does not state per-seat, per-token, or subscription pricing for either option. It provides no benchmark scores and no results from internal testing. It does not describe what either model can or cannot do for research, writing, or code review. It says nothing about data-handling terms, deployment options, or integration requirements.\n\nBecause none of these items appear in the packet, this guide does not estimate them. Whether either model fits within the $48,000 budget for a 40-person team is unknown. Whether either model performs better at research, writing, or code review is also unknown. Any figure or capability claim added here would be fabricated.\n\n## Caveats\n\nSeveral unresolved questions affect how this decision should be read.\n\n**Model identities are unresolved.** The comparison concerns two AI models, but the packet does not say which ones. Every downstream judgment depends on answering this first.\n\n**Budget scope is unresolved.** The packet calls the $48,000 figure the &quot;approved migration budget.&quot; It does not say whether that amount is meant to cover model licensing or usage fees. It may instead cover only migration work such as staff time, tooling, or services. If model costs must come out of the same $48,000, the cost headroom for either model is narrower than the headline number suggests. This needs confirmation.\n\n**Security review timing is unresolved and is a schedule risk.** The security review is not scheduled. With a 12-week deadline, an unscheduled review is a real risk to the timeline. The packet does not say how long a review takes or whether a failed review would force a switch to the other model. Both outcomes could consume a meaningful share of the 12 weeks. No model should be treated as approved until the review is complete.\n\n**Use-case weighting is unresolved.** The team named three uses: research, writing, and code review. The packet does not say whether they matter equally or whether one dominates. A model that is stronger in one area might be weaker in another, and without a weighting there is no basis for trading these off.\n\n**Usage volume is unresolved.** The team has 40 people, but the packet does not say how many will use the model or how heavily. Usage volume could strongly affect cost under some pricing structures. That cannot be assessed without pricing data and usage estimates.\n\n**The length instruction con<\/pre><\/details><details class=\"\"><summary>GPT-6 Sol \u00b7 full input<\/summary><pre>You are comparing two AI models for a practical buyer guide. Using only the source packet below, write 700\u2013900 words with exactly these headings: Decision, Evidence, Caveats, Next step. Preserve these facts exactly: Project North has a 12-week deadline; the migration budget is $48,000; the security review is not scheduled. If a fact is unresolved, say so explicitly. Do not invent prices, benchmarks, or product capabilities.\n\nSOURCE PACKET:\nProject North is a 12-week migration for a 40-person team. The approved migration budget is $48,000. Security review timing has not been scheduled. The team wants a model recommendation for research, writing, and code review. A stakeholder note says the current draft is too long, but gives no word count.\n\nReturn only the guide.<\/pre><\/details><details class=\"\"><summary>GPT-6 Sol \u00b7 full output<\/summary><pre>## Decision\n\nDo not name a winning model yet. The source packet asks for a recommendation between two AI models, but it does not identify either model or provide results for research, writing, or code review. A claim that one is better would therefore be unsupported. The practical recommendation is to compare the two candidates against the team\u2019s own work before making a selection.\n\nProject North has a 12-week deadline. The migration budget is $48,000. The security review is not scheduled. Those facts make the decision time-sensitive, but they do not justify guessing. A useful choice must account for whether a model helps the 40-person team complete its actual tasks, how its use fits within the approved budget, and what remains uncertain until security review timing is known.\n\nTreat the recommendation as conditional: choose the model that performs better on representative work, can be used within the approved migration budget, and clears any review needed for its intended use. If neither candidate meets those conditions, the guide should say that neither can yet be recommended. The available material does not establish that either condition has been met.\n\n## Evidence\n\nThe team wants a model recommendation for three kinds of work: research, writing, and code review. These are the appropriate comparison categories because they come from the stated need, not from assumed product strengths. The packet contains no examples of either model\u2019s output, no evaluation results, and no description of what the two candidates offer. It also contains no prices or benchmarks. As a result, there is no evidentiary basis for ranking the models on accuracy, speed, quality, cost, or suitability.\n\nThe project context does provide useful decision criteria. Project North is a migration for a 40-person team, so a comparison should reflect tasks people on that team would actually perform. The approved migration budget is $48,000; any proposed use of a model must be assessed against that amount rather than an invented allowance for AI tools. The security review is not scheduled, which leaves the timing of a review\u2014and its effect on adoption\u2014unresolved.\n\nA stakeholder has said the current draft is too long, but has not supplied a word count. That is evidence of a preference for concision, not a measurable limit for model output. When comparing writing samples, reviewers can assess whether each candidate follows a request to be concise and preserves essential facts. They should not claim that either model satisfies a stakeholder-specified word limit, because none was given.\n\nFor research, useful evidence would be whether each candidate distinguishes supplied facts from open questions and avoids unsupported conclusions. For writing, it would be whether each produces a clear, appropriately brief draft without dropping constraints. For code review, it would be whether each identifies issues in a provided example and explains them in a way a reviewer can check. These are proposed tests, not findings about either model.\n\n## Caveats\n\nThe largest caveat is that the candidates themselves are unresolved: the packet does not name them. It also does not say how the team would access them, what they cost, or whether either is available for the intended work. No product capability should be inferred from the phrase \u201cAI models.\u201d Even a well-designed comparison cannot yield a product recommendation until the candidates and evaluation evidence are known.\n\nThe migration budget is approved, but the packet does not allocate any portion of it to model use or testing. Do not treat $48,000 as an AI purchasing budget. Likewise, a 12-week deadline does not establish a date by which security review will occur. Because the security review is not scheduled, whether and when a candidate could be used under the team\u2019s requirements remains unresolved.\n\nThe three requested uses may also produce different results. One candidate could be preferable for one task and not another; the packet provides no basis for assuming a single winner across research, writing, and code review. A recommendation should make any such tradeoff visible rather than hiding it in an overall label. If the team decides that one category matters more than the others, that priority should come from the stakeholders, not from this guide.\n\nFinally, the note about the draft being too long should not be mistaken for evidence about model performance. It describes feedback on a current draft, without identifying its author or giving a target length. It can inform how the final recommendation is presented, but it cannot settle the comparison.\n\n## Next step\n\nIdentify the two candidates and ask the team for representative research, writing, and code-review tasks. Give each model the same materials and instructions for each task, including directions to mark uncertainty and avoid adding facts. Have reviewers record what each answer gets right, what it misses, and which claims require verification. Keep task-specific observations separate so that a strong writing sample does not stand in for evidence about code review.\n\nBefore choosing, confirm the cost of the intended use against the $48,000 migration budget and establish when the security review can occur. Neither answer is in the packet. If those questions cannot be resolved in time for a decision, report that constraint plainly rather than presenting a provisional preference as an approved choice.\n\nThe final buyer recommendation can then be short and auditable: name the candidates, state the evidence for each of the three uses, explain any tradeoffs, and identify outstanding approval or budget questions. Until that information exists, the honest answer for Project North is not \u201cModel A\u201d or \u201cModel B.\u201d It is that the comparison has not yet been supported by evidence.<\/pre><\/details><p class=\"warning\">Treat this as a context-handling check, not a quality ranking.<\/p><\/section>\n\n\n\n<h2 id=\"t01r\" class=\"wp-block-heading\">R\u00e9daction longue<\/h2>\n\n\n\n<style>.evidence-output-cap{box-sizing:border-box;max-width:100%;margin:28px 0;padding:22px;border:1px solid #cadbd5;border-radius:12px;background:#f4f9f7;color:#173b3b;font:16px\/1.6 system-ui,sans-serif}.evidence-output-cap *{box-sizing:border-box}.evidence-output-cap h3{margin:0 0 14px;color:#143c40;font-size:24px;line-height:1.3}.evidence-output-cap p,.evidence-output-cap li{color:#25474b}.evidence-output-cap .e-row{margin:18px 0}.evidence-output-cap .e-label{display:flex;justify-content:space-between;gap:12px;flex-wrap:wrap;font-weight:700}.evidence-output-cap .e-track{height:25px;border-radius:4px;background:#e0eae7;margin:8px 0;overflow:hidden}.evidence-output-cap .e-stack{display:flex;height:100%}.evidence-output-cap .e-in{display:block;height:100%;background:#176e75}.evidence-output-cap .e-out{display:block;height:100%;background:#b9643d}.evidence-output-cap small{display:block;color:#355a60;font-size:14px}.evidence-output-cap pre{white-space:pre-wrap;overflow-wrap:anywhere;background:#17383e;color:#f6fcff;padding:16px;border-radius:6px}.evidence-output-cap table{width:100%;border-collapse:collapse;font-size:14px}.evidence-output-cap th,.evidence-output-cap td{text-align:left;vertical-align:top;padding:10px;border-bottom:1px solid #c9dad3}.evidence-output-cap th{color:#143c40;background:#e2efea}.evidence-output-cap .e-scroll{overflow-x:auto}.evidence-output-cap a{color:#075a83}@media(max-width:600px){.evidence-output-cap{padding:16px}.evidence-output-cap h3{font-size:21px}}<\/style><section class=\"evidence-output-cap\" aria-label=\"Completion under the same 2,400-token output cap\"><h3>Completion under the same 2,400-token output cap<\/h3><p>The same brief required five sections. Both models had a 2,400-token output allowance; the returned metadata explains the visible difference.<\/p><div class=\"e-row\"><div class=\"e-label\"><span>Claude Opus 5.5<\/span><span>4 of 5 sections<\/span><\/div><div class=\"e-track\"><span class=\"e-stack\" style=\"width:80%\"><span class=\"e-in\" style=\"width:100%\"><\/span><span class=\"e-out\" style=\"width:0%\"><\/span><\/span><\/div><small>648 visible words \u00b7 2,400 completion tokens \u00b7 finish reason: length<\/small><\/div><div class=\"e-row\"><div class=\"e-label\"><span>GPT-6 Sol<\/span><span>5 of 5 sections<\/span><\/div><div class=\"e-track\"><span class=\"e-stack\" style=\"width:100%\"><span class=\"e-in\" style=\"width:100%\"><\/span><span class=\"e-out\" style=\"width:0%\"><\/span><\/span><\/div><small>1,065 visible words \u00b7 finish reason: stop<\/small><\/div><p>Claude reached the configured output ceiling before the Decision rule. Sol completed the brief within that ceiling. This supports a recommendation for this constrained run, not a general ranking of maximum writing ability or speed. The full returned text, including the truncated response, is preserved below.<\/p><\/section>\n\n\n\n<style>\n.copus-module-t01r{box-sizing:border-box;margin:30px 0;padding:24px;border:1px solid #d8dedb;border-radius:7px;background:#f7faf8;color:#203238;font:16px\/1.65 -apple-system,BlinkMacSystemFont,'Segoe UI',sans-serif;overflow-wrap:break-word}\n.copus-module-t01r *{box-sizing:border-box} .copus-module-t01r h3{margin:0 0 12px;color:#143e49;font:700 25px\/1.25 Georgia,serif} .copus-module-t01r h4{margin:20px 0 8px;color:#174f58;font-size:18px} .copus-module-t01r p,.copus-module-t01r li{color:#263a40} .copus-module-t01r .eyebrow{margin:0 0 7px;color:#9a513d;font-size:12px;font-weight:750;letter-spacing:.08em;text-transform:uppercase} .copus-module-t01r .metrics{display:grid;grid-template-columns:repeat(auto-fit,minmax(145px,1fr));gap:10px;margin:17px 0} .copus-module-t01r .metric{padding:13px;background:#fff;border-left:3px solid #b65d45} .copus-module-t01r .metric strong{display:block;color:#153b47;font-size:25px;line-height:1.2} .copus-module-t01r .metric span{color:#4b6065;font-size:13px} .copus-module-t01r .scroll{max-width:100%;overflow-x:auto} .copus-module-t01r table{width:100%;min-width:620px;border-collapse:collapse;background:#fff;font-size:14px} .copus-module-t01r th,.copus-module-t01r td{padding:10px 11px;text-align:left;vertical-align:top;border-bottom:1px solid #dce5e1} .copus-module-t01r th{background:#e4efeb;color:#163f46} .copus-module-t01r details{margin:11px 0;border:1px solid #c9d9d3;border-radius:5px;background:#fff} .copus-module-t01r summary{padding:12px 14px;color:#155664;cursor:pointer;font-weight:700} .copus-module-t01r pre{max-height:520px;margin:0;padding:14px;border-top:1px solid #dce5e1;background:#fbfcfb;color:#263b42;white-space:pre-wrap;overflow:auto;font:13px\/1.65 ui-monospace,SFMono-Regular,Menlo,monospace} .copus-module-t01r .note{color:#52686c;font-size:14px} .copus-module-t01r .warning{padding:13px;background:#fff3df;border-left:3px solid #b76a2f;color:#5a3c21} @media(max-width:640px){.copus-module-t01r{padding:17px;margin:22px 0;font-size:15px} .copus-module-t01r h3{font-size:22px} .copus-module-t01r table{min-width:600px} .copus-module-t01r pre{max-height:440px;font-size:12px}}\n<\/style><section class=\"copus-module-t01r\"><p class=\"eyebrow\">Long-form task<\/p><h3>Long-form writing: GPT-6 Sol completed the brief<\/h3><p>This is the first direct comparison task. The prompt explicitly named <strong>Claude Opus 5.5<\/strong> et <strong>GPT-6 Sol<\/strong>, gave both the same Project North briefing packet, and required a conditional decision memo.<\/p><div class=\"scroll\"><table><thead><tr><th>Mesure<\/th><th>Claude Opus 5.5<\/th><th>GPT-6 Sol<\/th><\/tr><\/thead><tbody><tr><td>Visible length<\/td><td>648 words; finish_reason=length<\/td><td>1,065 words; finish_reason=stop<\/td><\/tr><tr><td>Required sections<\/td><td>4 of 5; Decision rule missing after truncation<\/td><td>5 sur 5<\/td><\/tr><tr><td>Four-row scorecard<\/td><td>Present and symmetric<\/td><td>Present and symmetric<\/td><\/tr><tr><td>Pilot checks \/ stop condition<\/td><td>Started but truncated before completion<\/td><td>Three checks and a stop condition<\/td><\/tr><tr><td>Score<\/td><td>7.0 \/ 10<\/td><td>10.0 \/ 10<\/td><\/tr><\/tbody><\/table><\/div><p><strong>\u00c0 emporter :<\/strong> With the same 2,400-token output cap, GPT-6 Sol returned all five sections, while Claude Opus 5.5 reached the cap before the Decision rule. This shows completion under this configuration; it does not establish either model\u2019s maximum writing ability or general speed.<\/p><details class=\"\"><summary>Shared full input<\/summary><pre>You are writing a decision memo for a team choosing between Claude Opus 5.5 and GPT-6 Sol. The model names are fixed and must appear exactly as written. Using only the evidence packet below, write 900\u20131100 words with exactly these sections: Executive recommendation; What the evidence supports; Where the evidence is missing; 30-day pilot; Decision rule.\n\nRequirements: (1) compare Claude Opus 5.5 and GPT-6 Sol symmetrically; (2) separate observed evidence, assumptions, and unknowns; (3) preserve the exact dates, budget, team size, and unresolved security status; (4) include a compact four-row scorecard covering research synthesis, long-form writing, code review, and structured JSON; (5) give a conditional recommendation rather than inventing a winner; (6) list three measurable pilot checks and a stop condition; (7) do not invent prices, benchmarks, context limits, or product capabilities.\n\nEVIDENCE PACKET\n- Project North is a 12-week migration for a 40-person team.\n- The approved migration budget is $48,000. The packet does not say whether model usage is inside that budget.\n- Security review timing is not scheduled.\n- The team needs research synthesis, long-form writing, code review, and structured JSON extraction.\n- A stakeholder says the current draft is too long, but no target word count is approved.\n- No controlled outputs from Claude Opus 5.5 or GPT-6 Sol are supplied yet.\n- The team wants an auditable choice and can run a 30-day pilot with identical prompts and human review.\n\nReturn only the memo. Do not claim that the evidence packet proves either model is better.<\/pre><\/details><details class=\"\"><summary>Claude Opus 5.5 \u00b7 complete returned output<\/summary><pre>## Executive recommendation\n\nDo not select Claude Opus 5.5 or GPT-6 Sol yet. The evidence packet contains no controlled outputs from either model. There is therefore no observed basis for declaring one better than the other on any of the four required workloads.\n\nThe recommendation is conditional. Run a 30-day pilot that gives both models identical prompts and applies the same human review. Then select the model that meets thresholds agreed before the pilot starts, as long as the security and budget questions below are resolved first. There are three possible outcomes:\n\n- **One model clears the thresholds:** choose that model.\n- **Both clear:** apply the tie-breakers in the Decision rule.\n- **Neither clears:** adopt neither.\n\nThis approach gives the team the auditable choice it has asked for.\n\nThe fixed project facts are:\n\n- Project North is a 12-week migration for a 40-person team.\n- The approved migration budget is $48,000. The packet does not say whether model usage is inside that budget.\n- Security review timing is not scheduled.\n- The packet supplies no calendar dates (no project start date, end date, or pilot window), so this memo does not assign any.\n\n## What the evidence supports\n\n**Observed evidence.** These are facts stated directly in the packet:\n\n- Project North is a 12-week migration for a 40-person team.\n- The approved migration budget is $48,000, and its coverage of model usage is unstated.\n- Security review timing is not scheduled.\n- The required workloads are research synthesis, long-form writing, code review, and structured JSON extraction.\n- A stakeholder says the current draft is too long, but no target word count is approved.\n- No controlled outputs from Claude Opus 5.5 or GPT-6 Sol have been supplied.\n- The team wants an auditable choice and can run a 30-day pilot with identical prompts and human review.\n\n**What this supports.** The evidence defines what must be evaluated and how, but not which model wins. It justifies a structured, side-by-side pilot. It does not justify any claim about quality, speed, cost, context handling, or feature availability for either model.\n\n**Scorecard (current state of evidence)**\n\n| Workload | Claude Opus 5.5 | GPT-6 Sol | Pilot measure |\n|---|---|---|---|\n| Research synthesis | No controlled output supplied | No controlled output supplied | Blind reviewer rubric score |\n| Long-form writing | No controlled output supplied | No controlled output supplied | Rubric score plus adherence to an approved length target |\n| Code review | No controlled output supplied | No controlled output supplied | Share of known issues identified |\n| Structured JSON | No controlled output supplied | No controlled output supplied | Schema-validation pass rate |\n\nEvery cell for both models is identical because the packet treats them identically. No row currently favors either model.\n\n## Where the evidence is missing\n\n**Unknowns.** The packet is silent on each of these:\n\n- Whether model usage costs fall inside the $48,000 migration budget or require separate funding.\n- When security review will happen, what it will cover, and whether either model is acceptable under it. Security status is unresolved for both models.\n- Calendar dates for the 12-week migration and the 30-day pilot.\n- An approved target word count for long-form writing. The stakeholder&#x27;s &quot;too long&quot; comment cannot be scored without one.\n- Pricing, benchmark results, context limits, and product capabilities for Claude Opus 5.5 and GPT-6 Sol. None are supplied, and this memo does not assume any.\n- Who the human reviewers are, how many there are, and how disagreements between them will be resolved.\n- What data the pilot may use before security review is complete.\n\n**Assumptions.** These are proposals made in this memo, not facts from the packet, and each requires team approval:\n\n- The pilot can use non-sensitive or synthetic material until security review clears real data.\n- Reviewers can be blinded to which model produced each output.\n- The team can assemble a fixed prompt set for each workload that represents Project North&#x27;s real tasks.\n- The pass thresholds proposed below are starting points that the team may change before the pilot begins, but not during it.\n\n## 30-day pilot\n\n**Design**\n\n1.<\/pre><\/details><details class=\"\"><summary>GPT-6 Sol \u00b7 complete returned output<\/summary><pre>## Executive recommendation\n\nDo not select Claude Opus 5.5 or GPT-6 Sol yet. Project North is a 12-week migration for a 40-person team, and the packet supplies no controlled outputs from either model. It therefore supports a comparison plan, not a performance verdict. Run the available 30-day pilot with identical prompts and human review, then make an auditable choice against criteria agreed before results are examined. Choose either model only if its reviewed work meets the team\u2019s needs, its use fits the approved financial arrangement, and the unresolved security review permits adoption. If those conditions are not met, defer selection rather than treating an incomplete comparison as a win.\n\nThe approved migration budget is $48,000. The packet does not say whether model usage is inside that budget. Security review timing is not scheduled. Those are decision constraints for Claude Opus 5.5 and GPT-6 Sol alike, not evidence for favoring one.\n\n## What the evidence supports\n\n**Observed:** The team needs research synthesis, long-form writing, code review, and structured JSON extraction. It wants an auditable choice and can run a 30-day pilot using identical prompts and human review. A stakeholder says the current draft is too long, but no target word count is approved. That feedback identifies an issue to test in writing outputs; it does not establish a preferred model or an acceptable length.\n\nThe current task-by-task scorecard is deliberately unranked:\n\n| Workstream | Claude Opus 5.5 | GPT-6 Sol | Pilot evidence to collect |\n|---|---|---|---|\n| Research synthesis | Not yet tested | Not yet tested | Reviewer-rated accuracy, coverage, and traceability |\n| Long-form writing | Not yet tested | Not yet tested | Reviewer-rated usefulness, clarity, and concision |\n| Code review | Not yet tested | Not yet tested | Reviewer-verified findings and unsupported claims |\n| Structured JSON | Not yet tested | Not yet tested | Valid outputs and correct field extraction |\n\n**Assumption for planning, not an observed result:** Comparable work samples and a common review rubric can reveal differences relevant to Project North. The pilot should test that assumption. Identical prompts alone will not make the choice auditable: the team must also retain the inputs, both outputs, review criteria, reviewer judgments, and reasons for any exclusions or reruns. Human review should apply the same standard to each model.\n\nFor long-form writing, reviewers should distinguish \u201ctoo long\u201d from \u201cnot useful.\u201d A shorter answer could omit necessary material; a longer answer could still be clear and usable. Because no target word count is approved, the pilot can record length and reviewer judgments, but should not retroactively declare an arbitrary word count to be the acceptance standard. The same discipline applies across the other tasks: record what reviewers observed, rather than converting preferences into claims about general model capability.\n\n## Where the evidence is missing\n\n**Unknown:** The packet contains no controlled outputs from Claude Opus 5.5 or GPT-6 Sol. It establishes no relative quality, reliability, or suitability for any of the four workstreams. It also gives no prices, context limits, benchmarks, or product-capability information. This memo consequently cannot estimate usage cost or infer which model would perform better on Project North\u2019s material.\n\nThe budget boundary needs clarification before a purchasing decision. The $48,000 is the approved *migration* budget; whether model usage must be paid from it is unstated. Treating it as either included or additional would create a financial assumption the packet does not support. The team should document the answer and the applicable approval path before committing to sustained use.\n\nSecurity is likewise unresolved for both candidates. Security review timing is not scheduled, and the packet provides no security outcome. A strong pilot result would not resolve that status. The team should identify who can schedule and decide the review, what information that reviewer requires, and whether pilot activity itself is permitted under applicable rules; the packet does not answer those questions. No calendar start or finish dates are supplied for the migration, pilot, or security review, so the team should not imply that a security decision will arrive within the 30-day pilot or the 12-week migration.\n\n## 30-day pilot\n\nBefore the first comparison, the team should select representative prompts for all four workstreams, define the expected form of each response, and agree on a human-review rubric. Both models should receive identical prompts and the same review instructions. Reviewers should record their judgments against the work rather than against a model\u2019s reputation. Preserve the prompt, output, review, and any correction requested so that a later reader can reconstruct each judgment. Record deviations from the common process instead of silently dropping difficult cases.\n\nUse three measurable checks:\n\n1. **Reviewed task quality:** For research synthesis, long-form writing, and code review, count the proportion of outputs reviewers accept for the stated task. Also record rejection reasons separately\u2014for example, missing coverage, unclear writing, or an unverified code-review finding. Report results by workstream and model, not just as one pooled score.\n2. **Structured JSON reliability:** Count outputs that match the agreed structure and outputs whose extracted fields a reviewer verifies as correct. Report both rates for Claude Opus 5.5 and GPT-6 Sol; a structurally valid answer should not be counted as accurate solely because it parses.\n3. **Review effort and traceability:** Record the human time required to check and, where needed, correct each output. Count cases lacking enough retained evidence to explain the reviewer\u2019s decision. Compare these measures on the same prompt set, including cases where neither output is acceptable.\n\nAgree on acceptance standards and how ties will be handled before viewing pilot results; the packet provides no benchmark from which to derive numerical thresholds. At the end of 30 days, publish the rubric, counts, reviewer reasons, and unresolved questions. **Stop condition:** pause adoption if security requirements for the pilot or subsequent use cannot be satisfied, or if the team cannot establish a valid budget and approval path. Do not substitute favorable quality scores for either clearance.\n\n## Decision rule\n\nSelect Claude Opus 5.5 if its pilot evidence meets the pre-agreed standards, is materially stronger on the workstreams the team prioritizes, and its security and financial conditions are resolved. Apply exactly the same rule to GPT-6 Sol. If both meet the standards without a material, relevant difference, decide using documented project priorities and resolved constraints rather than claiming a performance winner. If neither qualifies\u2014or the budget boundary or security status remains unresolved\u2014make no model selection yet. That outcome preserves an auditable decision for the 40-person team without asserting more than the evidence can support.<\/pre><\/details><p class=\"note\">Metadata: Claude \u2014 HTTP 200 \u00b7 25.98s \u00b7 finish_reason=length \u00b7 prompt_tokens=598 \u00b7 completion_tokens=2400<br>GPT \u2014 HTTP 200 \u00b7 27.763s \u00b7 finish_reason=stop \u00b7 prompt_tokens=386 \u00b7 completion_tokens=1695<\/p><\/section>\n\n\n\n<h2 id=\"t02\" class=\"wp-block-heading\">Source synthesis<\/h2>\n\n\n\n<style>\n.copus-module-t02{box-sizing:border-box;margin:30px 0;padding:24px;border:1px solid #d8dedb;border-radius:7px;background:#f7faf8;color:#203238;font:16px\/1.65 -apple-system,BlinkMacSystemFont,'Segoe UI',sans-serif;overflow-wrap:break-word}\n.copus-module-t02 *{box-sizing:border-box} .copus-module-t02 h3{margin:0 0 12px;color:#143e49;font:700 25px\/1.25 Georgia,serif} .copus-module-t02 h4{margin:20px 0 8px;color:#174f58;font-size:18px} .copus-module-t02 p,.copus-module-t02 li{color:#263a40} .copus-module-t02 .eyebrow{margin:0 0 7px;color:#9a513d;font-size:12px;font-weight:750;letter-spacing:.08em;text-transform:uppercase} .copus-module-t02 .metrics{display:grid;grid-template-columns:repeat(auto-fit,minmax(145px,1fr));gap:10px;margin:17px 0} .copus-module-t02 .metric{padding:13px;background:#fff;border-left:3px solid #b65d45} .copus-module-t02 .metric strong{display:block;color:#153b47;font-size:25px;line-height:1.2} .copus-module-t02 .metric span{color:#4b6065;font-size:13px} .copus-module-t02 .scroll{max-width:100%;overflow-x:auto} .copus-module-t02 table{width:100%;min-width:620px;border-collapse:collapse;background:#fff;font-size:14px} .copus-module-t02 th,.copus-module-t02 td{padding:10px 11px;text-align:left;vertical-align:top;border-bottom:1px solid #dce5e1} .copus-module-t02 th{background:#e4efeb;color:#163f46} .copus-module-t02 details{margin:11px 0;border:1px solid #c9d9d3;border-radius:5px;background:#fff} .copus-module-t02 summary{padding:12px 14px;color:#155664;cursor:pointer;font-weight:700} .copus-module-t02 pre{max-height:520px;margin:0;padding:14px;border-top:1px solid #dce5e1;background:#fbfcfb;color:#263b42;white-space:pre-wrap;overflow:auto;font:13px\/1.65 ui-monospace,SFMono-Regular,Menlo,monospace} .copus-module-t02 .note{color:#52686c;font-size:14px} .copus-module-t02 .warning{padding:13px;background:#fff3df;border-left:3px solid #b76a2f;color:#5a3c21} @media(max-width:640px){.copus-module-t02{padding:17px;margin:22px 0;font-size:15px} .copus-module-t02 h3{font-size:22px} .copus-module-t02 table{min-width:600px} .copus-module-t02 pre{max-height:440px;font-size:12px}}\n<\/style><section class=\"copus-module-t02\"><p class=\"eyebrow\">Research task<\/p><h3>Source synthesis: a draw<\/h3><p>Both models resolved the date conflict to 15 October, kept the agreed $12,000 budget, and treated \u201calways accurate\u201d as an unsupported manager claim. The result is a tie on this task.<\/p><div class=\"scroll\"><table><thead><tr><th>Mesure<\/th><th>Claude Opus 5.5<\/th><th>GPT-6 Sol<\/th><\/tr><\/thead><tbody><tr><td>Score<\/td><td>9.5 \/ 10<\/td><td>9.5 \/ 10<\/td><\/tr><tr><td>Conflict handling<\/td><td>S2 controls the deadline<\/td><td>S2 controls the deadline<\/td><\/tr><tr><td>Unsupported claim<\/td><td>Marked unsupported<\/td><td>Marked unsupported<\/td><\/tr><tr><td>Practical difference<\/td><td>More detailed risk explanation<\/td><td>More compact recommendation<\/td><\/tr><\/tbody><\/table><\/div><details class=\"\"><summary>Shared full input<\/summary><pre>Reconcile the source notes below. Return (1) a Markdown table with columns Claim, Controlling source, Status, Reason and (2) a recommendation of about 250 words. Use the later dated policy note as controlling only for the deadline conflict. Mark unsupported claims as unsupported. Do not add facts.\n\n[S1 2026-09-01] Pilot ends on 30 September. Budget is $12,000.\n[S2 2026-09-03] Pilot ends on 15 October. Budget remains $12,000.\n[S3 2026-09-02] Legal has not approved external data sharing.\n[S4 2026-09-04] A manager says the model is \u201calways accurate\u201d; no test evidence is attached.\n[S5 2026-09-04] The pilot team has 8 members.\n[S6 2026-09-05] The decision memo must list unresolved risks.\n\nReturn only the table and recommendation.<\/pre><\/details><details class=\"\"><summary>Claude Opus 5.5 \u00b7 complete output<\/summary><pre>| Claim | Controlling source | Status | Reason |\n|---|---|---|---|\n| Pilot ends on 30 September | S2 (2026-09-03) | Superseded | Conflicts with S2. The later dated note controls the deadline conflict. |\n| Pilot ends on 15 October | S2 (2026-09-03) | Supported (controlling) | This is the later dated note on the deadline conflict, so it overrides S1. |\n| Budget is $12,000 | S1 and S2 | Supported | Both sources agree, and S2 states that the budget &quot;remains&quot; $12,000. There is no conflict. |\n| Legal has not approved external data sharing | S3 | Supported | Stated directly and not contradicted by any other source. |\n| The model is &quot;always accurate&quot; | S4 | Unsupported | This is a manager&#x27;s assertion with no test evidence attached. |\n| The pilot team has 8 members | S5 | Supported | Stated directly and not contradicted. It comes from a single source. |\n| The decision memo must list unresolved risks | S6 | Supported | This is a stated requirement for the memo and is not contradicted. |\n\n**Recommendation**\n\nThe decision memo should use 15 October as the pilot end date. S2 is the later dated note and controls the deadline conflict, which supersedes the 30 September date in S1. The later-date rule applies only to this conflict. The other claims were assessed on their own support.\n\nThe budget should be recorded as $12,000. S1 and S2 agree on this figure, so no reconciliation is needed. The team size of eight members can also be included. It rests on S5 alone, but no source contradicts it.\n\nS6 requires the memo to list unresolved risks, and the notes support at least two.\n\n- **External data sharing:** Legal has not approved it (S3). The memo should state this plainly. It should not imply that external data sharing is permitted during the pilot.\n- **Model accuracy:** The claim that the model is &quot;always accurate&quot; is unsupported. S4 notes that no test evidence is attached, and it records the claim only as a manager&#x27;s statement. The memo should not present it as a finding or rely on it to justify decisions. It should list model accuracy as unverified until supporting evidence is provided.\n\nFinally, the memo should briefly note that the pilot end date changed between S1 and S2. Readers may encounter the earlier 30 September date elsewhere, and this note would explain why 15 October is used. The memo should not add explanations for the change, test results, or legal status updates, because none appear in the source notes.<\/pre><\/details><details class=\"\"><summary>GPT-6 Sol \u00b7 complete output<\/summary><pre>| Claim | Controlling source | Status | Reason |\n|---|---|---|---|\n| The pilot ends on 30 September. | S2 (2026-09-03) | Superseded | The later dated note controls the deadline conflict and gives 15 October. |\n| The pilot ends on 15 October. | S2 (2026-09-03) | Supported | S2 supplies the controlling deadline. |\n| The budget is $12,000. | S1 and S2 | Supported | Both notes give the same amount. |\n| Legal has not approved external data sharing. | S3 | Supported | S3 states this directly; no later note addresses that approval. |\n| The model is \u201calways accurate.\u201d | S4 | Unsupported | A manager makes the claim, but no test evidence is attached. |\n| The pilot team has 8 members. | S5 | Supported | S5 states the team size. |\n| The decision memo must list unresolved risks. | S6 | Supported | S6 states this requirement. |\n\n**Recommendation:** Use 15 October as the pilot end date in the decision memo. S2 is later than S1 and resolves their deadline conflict. Keep the budget at $12,000: the notes agree on that amount, so the deadline change provides no basis for changing it. Record the pilot team size as 8 members, as stated in S5.\n\nList unresolved risks explicitly, as S6 requires. The memo should state that Legal has not approved external data sharing, without implying that approval has since been granted or that sharing is permitted. It should also distinguish the manager\u2019s statement about accuracy from an established finding. S4 records the statement that the model is \u201calways accurate,\u201d but supplies no test evidence. Do not present that statement as a verified result.\n\nKeep the reconciliation narrow. S2 controls the conflicting end dates; it does not displace the separate notes about Legal, accuracy, team size, or memo requirements. Where the notes do not establish an outcome, leave it unresolved rather than supplying an assumption. In particular, the record supports neither a claim that external data sharing has been approved nor a claim that the model\u2019s accuracy has been demonstrated.\n\nThe resulting memo can therefore use the 15 October deadline and $12,000 budget while identifying the two evidence gaps apparent in these notes: outstanding Legal approval for external data sharing and the absence of test evidence for the accuracy assertion.<\/pre><\/details><p class=\"note\">Metadata: Claude \u2014 HTTP 200 \u00b7 12.846s \u00b7 finish_reason=stop \u00b7 prompt_tokens=338 \u00b7 completion_tokens=1193<br>GPT \u2014 HTTP 200 \u00b7 12.322s \u00b7 finish_reason=stop \u00b7 prompt_tokens=230 \u00b7 completion_tokens=714<\/p><\/section>\n\n\n\n<h2 id=\"t03\" class=\"wp-block-heading\">Coding and structured output<\/h2>\n\n\n\n<style>.evidence-code-diagnosis{box-sizing:border-box;max-width:100%;margin:28px 0;padding:22px;border:1px solid #cadbd5;border-radius:12px;background:#f4f9f7;color:#173b3b;font:16px\/1.6 system-ui,sans-serif}.evidence-code-diagnosis *{box-sizing:border-box}.evidence-code-diagnosis h3{margin:0 0 14px;color:#143c40;font-size:24px;line-height:1.3}.evidence-code-diagnosis p,.evidence-code-diagnosis li{color:#25474b}.evidence-code-diagnosis .e-row{margin:18px 0}.evidence-code-diagnosis .e-label{display:flex;justify-content:space-between;gap:12px;flex-wrap:wrap;font-weight:700}.evidence-code-diagnosis .e-track{height:25px;border-radius:4px;background:#e0eae7;margin:8px 0;overflow:hidden}.evidence-code-diagnosis .e-stack{display:flex;height:100%}.evidence-code-diagnosis .e-in{display:block;height:100%;background:#176e75}.evidence-code-diagnosis .e-out{display:block;height:100%;background:#b9643d}.evidence-code-diagnosis small{display:block;color:#355a60;font-size:14px}.evidence-code-diagnosis pre{white-space:pre-wrap;overflow-wrap:anywhere;background:#17383e;color:#f6fcff;padding:16px;border-radius:6px}.evidence-code-diagnosis table{width:100%;border-collapse:collapse;font-size:14px}.evidence-code-diagnosis th,.evidence-code-diagnosis td{text-align:left;vertical-align:top;padding:10px;border-bottom:1px solid #c9dad3}.evidence-code-diagnosis th{color:#143c40;background:#e2efea}.evidence-code-diagnosis .e-scroll{overflow-x:auto}.evidence-code-diagnosis a{color:#075a83}@media(max-width:600px){.evidence-code-diagnosis{padding:16px}.evidence-code-diagnosis h3{font-size:21px}}<\/style><section class=\"evidence-code-diagnosis\" aria-label=\"The coding difference was in the explanation\"><h3>The coding difference was in the explanation<\/h3><pre>Original filtering condition: if t\nA None element is falsy, so this filter already skips it.<\/pre><p>Both models returned valid JSON with the four requested keys and the same minimal patch. Claude correctly described the existing filter; GPT-6 Sol incorrectly said that a None element would cause an error. That specific diagnosis error matters when a reviewer decides whether a patch addresses the actual failure.<\/p><p>Claude supplied a risk list and Sol supplied a short risk string. The prompt did not require a particular type for that field, so this difference is not a contract violation. Sol\u2019s task score is 9.0\/10 after correcting the earlier unsupported half-point deduction; its factual-accuracy deduction remains.<\/p><\/section>\n\n\n\n<style>\n.copus-module-t03{box-sizing:border-box;margin:30px 0;padding:24px;border:1px solid #d8dedb;border-radius:7px;background:#f7faf8;color:#203238;font:16px\/1.65 -apple-system,BlinkMacSystemFont,'Segoe UI',sans-serif;overflow-wrap:break-word}\n.copus-module-t03 *{box-sizing:border-box} .copus-module-t03 h3{margin:0 0 12px;color:#143e49;font:700 25px\/1.25 Georgia,serif} .copus-module-t03 h4{margin:20px 0 8px;color:#174f58;font-size:18px} .copus-module-t03 p,.copus-module-t03 li{color:#263a40} .copus-module-t03 .eyebrow{margin:0 0 7px;color:#9a513d;font-size:12px;font-weight:750;letter-spacing:.08em;text-transform:uppercase} .copus-module-t03 .metrics{display:grid;grid-template-columns:repeat(auto-fit,minmax(145px,1fr));gap:10px;margin:17px 0} .copus-module-t03 .metric{padding:13px;background:#fff;border-left:3px solid #b65d45} .copus-module-t03 .metric strong{display:block;color:#153b47;font-size:25px;line-height:1.2} .copus-module-t03 .metric span{color:#4b6065;font-size:13px} .copus-module-t03 .scroll{max-width:100%;overflow-x:auto} .copus-module-t03 table{width:100%;min-width:620px;border-collapse:collapse;background:#fff;font-size:14px} .copus-module-t03 th,.copus-module-t03 td{padding:10px 11px;text-align:left;vertical-align:top;border-bottom:1px solid #dce5e1} .copus-module-t03 th{background:#e4efeb;color:#163f46} .copus-module-t03 details{margin:11px 0;border:1px solid #c9d9d3;border-radius:5px;background:#fff} .copus-module-t03 summary{padding:12px 14px;color:#155664;cursor:pointer;font-weight:700} .copus-module-t03 pre{max-height:520px;margin:0;padding:14px;border-top:1px solid #dce5e1;background:#fbfcfb;color:#263b42;white-space:pre-wrap;overflow:auto;font:13px\/1.65 ui-monospace,SFMono-Regular,Menlo,monospace} .copus-module-t03 .note{color:#52686c;font-size:14px} .copus-module-t03 .warning{padding:13px;background:#fff3df;border-left:3px solid #b76a2f;color:#5a3c21} @media(max-width:640px){.copus-module-t03{padding:17px;margin:22px 0;font-size:15px} .copus-module-t03 h3{font-size:22px} .copus-module-t03 table{min-width:600px} .copus-module-t03 pre{max-height:440px;font-size:12px}}\n<\/style><section class=\"copus-module-t03\"><p class=\"eyebrow\">T\u00e2che de codage<\/p><h3>Coding and structured output: Claude Opus 5.5 was more precise<\/h3><p>Both models returned valid JSON with the requested keys and the same minimal patch. Claude correctly explained that the original filter already skips a <code>Aucun<\/code> element. GPT-6 Sol incorrectly said that a <code>Aucun<\/code> element would cause an error. The prompt did not require a particular type for the risks field.<\/p><div class=\"scroll\"><table><thead><tr><th>Mesure<\/th><th>Claude Opus 5.5<\/th><th>GPT-6 Sol<\/th><\/tr><\/thead><tbody><tr><td>Score<\/td><td>10.0 \/ 10<\/td><td>9.0 \/ 10<\/td><\/tr><tr><td>Patch<\/td><td>Correct and minimal<\/td><td>Correct and minimal<\/td><\/tr><tr><td>Diagnostic<\/td><td>Correctly isolates both defects<\/td><td>Overstates the None-element failure<\/td><\/tr><tr><td>Requested JSON contract<\/td><td>Valid JSON; all four keys present<\/td><td>Valid JSON; all four keys present<\/td><\/tr><\/tbody><\/table><\/div><details class=\"\"><summary>Shared full input<\/summary><pre>Return strict JSON only with keys diagnosis, patch, tests, risks. Diagnose and minimally patch this Python function so all three cases pass. The patch must keep the function signature.\n\ndef normalize_tags(tags):\n    return &#x27;,&#x27;.join(sorted(set(t.strip().lower() for t in tags if t)))\n\nCases:\n1) [&#x27; AI &#x27;, &#x27;ai&#x27;, &#x27;Code&#x27;] -&gt; &#x27;ai,code&#x27;\n2) None -&gt; &#x27;&#x27;\n3) [&#x27;  &#x27;, None, &#x27;SEO&#x27;] -&gt; &#x27;seo&#x27;\n\nDo not execute code and do not include Markdown fences or prose outside the JSON.<\/pre><\/details><details class=\"\"><summary>Claude Opus 5.5 \u00b7 complete output<\/summary><pre>{&quot;diagnosis&quot;:&quot;Two defects, traced by reading the code (not executed). (a) Case 2: when tags is None, the generator iterates over None and raises TypeError instead of returning &#x27;&#x27;. (b) Case 3: the filter &#x27;if t&#x27; runs before stripping. The whitespace-only tag &#x27;  &#x27; is truthy, so it passes and becomes &#x27;&#x27;. The result is sorted([&#x27;&#x27;, &#x27;seo&#x27;]), which joins to &#x27;,seo&#x27; instead of &#x27;seo&#x27;. The None element in case 3 is already dropped by &#x27;if t&#x27;. Case 1 already works: &#x27; AI &#x27;, &#x27;ai&#x27; and &#x27;Code&#x27; normalize to {&#x27;ai&#x27;, &#x27;code&#x27;}, giving &#x27;ai,code&#x27;.&quot;,&quot;patch&quot;:&quot;def normalize_tags(tags):\\n    return &#x27;,&#x27;.join(sorted(set(t.strip().lower() for t in (tags or []) if t and t.strip())))&quot;,&quot;tests&quot;:[&quot;assert normalize_tags([&#x27; AI &#x27;, &#x27;ai&#x27;, &#x27;Code&#x27;]) == &#x27;ai,code&#x27;  # traced: {&#x27;ai&#x27;,&#x27;code&#x27;} -&gt; &#x27;ai,code&#x27;&quot;,&quot;assert normalize_tags(None) == &#x27;&#x27;  # traced: (None or []) -&gt; [] -&gt; &#x27;&#x27;&quot;,&quot;assert normalize_tags([&#x27;  &#x27;, None, &#x27;SEO&#x27;]) == &#x27;seo&#x27;  # traced: &#x27;  &#x27; fails t.strip(), None fails t, &#x27;SEO&#x27; -&gt; &#x27;seo&#x27;&quot;],&quot;risks&quot;:[&quot;Non-string elements such as 123 will raise AttributeError on .strip(). This is unchanged from the original behavior.&quot;,&quot;&#x27;tags or []&#x27; treats any falsy input (for example &#x27;&#x27; or an empty tuple) as empty. This is consistent with the required None -&gt; &#x27;&#x27; behavior.&quot;,&quot;If a single string is passed instead of an iterable of tags, it is iterated character by character. This is unchanged from the original behavior.&quot;,&quot;Internal whitespace is not collapsed, so &#x27;machine  learning&#x27; and &#x27;machine learning&#x27; remain distinct tags.&quot;,&quot;Tests were derived by manual tracing only and were not executed, as instructed.&quot;]}<\/pre><\/details><details class=\"\"><summary>GPT-6 Sol \u00b7 complete output<\/summary><pre>{&quot;diagnosis&quot;:&quot;The function raises an error when tags is None or contains None, and it retains entries that become empty after stripping.&quot;,&quot;patch&quot;:&quot;def normalize_tags(tags):\\n    return &#x27;,&#x27;.join(sorted(set(t.strip().lower() for t in (tags or []) if t and t.strip())))&quot;,&quot;tests&quot;:[{&quot;input&quot;:[&quot; AI &quot;,&quot;ai&quot;,&quot;Code&quot;],&quot;expected&quot;:&quot;ai,code&quot;},{&quot;input&quot;:null,&quot;expected&quot;:&quot;&quot;},{&quot;input&quot;:[&quot;  &quot;,null,&quot;SEO&quot;],&quot;expected&quot;:&quot;seo&quot;}],&quot;risks&quot;:&quot;Not executed. Non-None entries are assumed to be strings.&quot;}<\/pre><\/details><p class=\"note\">Metadata: Claude \u2014 HTTP 200 \u00b7 10.832s \u00b7 finish_reason=stop \u00b7 prompt_tokens=244 \u00b7 completion_tokens=965<br>GPT \u2014 HTTP 200 \u00b7 7.71s \u00b7 finish_reason=stop \u00b7 prompt_tokens=146 \u00b7 completion_tokens=305<\/p><\/section>\n\n\n\n<h2 id=\"verdict\" class=\"wp-block-heading\">Verdict final<\/h2>\n\n\n\n<style>\n.copus-module-verdict{box-sizing:border-box;margin:30px 0;padding:24px;border:1px solid #d8dedb;border-radius:7px;background:#f7faf8;color:#203238;font:16px\/1.65 -apple-system,BlinkMacSystemFont,'Segoe UI',sans-serif;overflow-wrap:break-word}\n.copus-module-verdict *{box-sizing:border-box} .copus-module-verdict h3{margin:0 0 12px;color:#143e49;font:700 25px\/1.25 Georgia,serif} .copus-module-verdict h4{margin:20px 0 8px;color:#174f58;font-size:18px} .copus-module-verdict p,.copus-module-verdict li{color:#263a40} .copus-module-verdict .eyebrow{margin:0 0 7px;color:#9a513d;font-size:12px;font-weight:750;letter-spacing:.08em;text-transform:uppercase} .copus-module-verdict .metrics{display:grid;grid-template-columns:repeat(auto-fit,minmax(145px,1fr));gap:10px;margin:17px 0} .copus-module-verdict .metric{padding:13px;background:#fff;border-left:3px solid #b65d45} .copus-module-verdict .metric strong{display:block;color:#153b47;font-size:25px;line-height:1.2} .copus-module-verdict .metric span{color:#4b6065;font-size:13px} .copus-module-verdict .scroll{max-width:100%;overflow-x:auto} .copus-module-verdict table{width:100%;min-width:620px;border-collapse:collapse;background:#fff;font-size:14px} .copus-module-verdict th,.copus-module-verdict td{padding:10px 11px;text-align:left;vertical-align:top;border-bottom:1px solid #dce5e1} .copus-module-verdict th{background:#e4efeb;color:#163f46} .copus-module-verdict details{margin:11px 0;border:1px solid #c9d9d3;border-radius:5px;background:#fff} .copus-module-verdict summary{padding:12px 14px;color:#155664;cursor:pointer;font-weight:700} .copus-module-verdict pre{max-height:520px;margin:0;padding:14px;border-top:1px solid #dce5e1;background:#fbfcfb;color:#263b42;white-space:pre-wrap;overflow:auto;font:13px\/1.65 ui-monospace,SFMono-Regular,Menlo,monospace} .copus-module-verdict .note{color:#52686c;font-size:14px} .copus-module-verdict .warning{padding:13px;background:#fff3df;border-left:3px solid #b76a2f;color:#5a3c21} @media(max-width:640px){.copus-module-verdict{padding:17px;margin:22px 0;font-size:15px} .copus-module-verdict h3{font-size:22px} .copus-module-verdict table{min-width:600px} .copus-module-verdict pre{max-height:440px;font-size:12px}}\n<\/style><section class=\"copus-module-verdict\"><p class=\"eyebrow\">Un choix pratique<\/p><h3>Which model fits your work?<\/h3><div class=\"metrics\"><div class=\"metric\"><strong>28.5 \/ 30<\/strong><span>GPT-6 Sol core total<\/span><\/div><div class=\"metric\"><strong>26.5 \/ 30<\/strong><span>Claude Opus 5.5 core total<\/span><\/div><div class=\"metric\"><strong>Cravate<\/strong><span>source synthesis<\/span><\/div><\/div><p>GPT-6 Sol leads this small first-output set because it completed the amended long-form contract while Claude Opus 5.5 hit the shared 2,400-token output cap. Claude Opus 5.5 leads the code\/JSON task on diagnostic precision. Source synthesis is a tie.<\/p><p>Choisir <strong>GPT-6 Sol<\/strong> when the immediate priority is completing a long, multi-constraint decision document in one pass. Choose <strong>Claude Opus 5.5<\/strong> when a code or structured-output review benefits from a more explicit diagnosis and risk inventory. If your work mixes both, route each task to the model that fits it best instead of forcing one universal winner.<\/p><p>GlobalGPT gives you a practical way to keep both models and the rest of your AI workflow in one dashboard. Research, writing, coding, images, video, and CLI-connected production work can stay in the same working environment.<\/p><\/section>\n\n\n\n<h2 id=\"faq\" class=\"wp-block-heading\">Foire aux questions<\/h2>\n\n\n\n<h3 id=\"faq-1\" class=\"wp-block-heading\">Which model won the comparison?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">GPT-6 Sol led the small first-output set 28.5 to 26.5 after the original boundary task was excluded and the JSON-contract deduction was corrected. That lead came from completing the revised long-form contract under the same 2,400-token output cap; Claude Opus 5.5 led the code and structured-output task, while source synthesis was a tie.<\/p>\n\n\n\n<h3 id=\"faq-2\" class=\"wp-block-heading\">Why was the first long-form test excluded?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The original prompt did not name either model. Both models correctly refused to guess the missing identities, so that response checks whether a model asks for missing context rather than pretending to know.<\/p>\n\n\n\n<h3 id=\"faq-3\" class=\"wp-block-heading\">Is Claude Opus 5.5 better for coding?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In this code and JSON task, Claude Opus 5.5 gave the more precise diagnosis and structured risk list. One task is not enough to establish a universal coding ranking.<\/p>\n\n\n\n<h3 id=\"faq-4\" class=\"wp-block-heading\">Is GPT-6 Sol better for writing?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">GPT-6 Sol completed the revised long-form memo within the requested range in the first output. The result supports that workflow recommendation for this test, not a general claim about every writing task.<\/p>\n\n\n\n<h3 id=\"faq-5\" class=\"wp-block-heading\">Can I test both models without switching platforms?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">GlobalGPT is designed as a multi-model AI subscription with multiple AI functions in one dashboard. It is useful for running the same research, writing, and coding prompt pack across models and keeping the workflow in one place.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.glbgpt.com\/home?inviter=hub_content_home&amp;login=1\">GlobalGPT<\/a> lets you keep both models and the rest of your AI workflow in one place. Start with the model that fits the immediate task, then move the work through research, writing, coding, images, video, or CLI-connected production steps without opening a separate service for every part.<\/p>","protected":false},"excerpt":{"rendered":"<p>Quick answer: GPT-6 Sol completed the tested five-section brief under a 2,400-token output cap; Claude Opus 5.5 gave the more precise diagnosis in our short code-review task. Neither result establishes a permanent overall winner. The useful comparison is broader than a score. Claude Opus 5.5 and GPT-6 Sol differ in how they handle writing, research [&hellip;]<\/p>","protected":false},"author":7,"featured_media":19791,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_seopress_robots_primary_cat":"","_seopress_titles_title":"Claude Opus 5.5 vs GPT-6 Sol: Which Model Should You Use?","_seopress_titles_desc":"Claude Opus 5.5 vs GPT-6 Sol: compare features, writing, research, coding, structured output, access routes, and the best model for your workflow.","_seopress_robots_index":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-19790","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-chat"],"acf":[],"_links":{"self":[{"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/posts\/19790","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/comments?post=19790"}],"version-history":[{"count":2,"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/posts\/19790\/revisions"}],"predecessor-version":[{"id":19798,"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/posts\/19790\/revisions\/19798"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/media\/19791"}],"wp:attachment":[{"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/media?parent=19790"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/categories?post=19790"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.glbgpt.com\/fr\/wp-json\/wp\/v2\/tags?post=19790"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}