[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-gpt-5-model-comparison-5-0-to-5-5-en":3,"article-related-gpt-5-model-comparison-5-0-to-5-5-en":30,"series-model-release-3dea00fc-0edf-4a95-8809-803f0bea1b35":83},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":22,"views":26,"created_at":27,"published_at":28,"topic_cluster_id":29},"3dea00fc-0edf-4a95-8809-803f0bea1b35","gpt-5-model-comparison-5-0-to-5-5-en","GPT-5.0 to 5.5: Which ChatGPT Model Wins?","\u003Cp data-speakable=\"summary\">OpenAI’s GPT-5 family grew from a 400K-token baseline to 1M-token agentic models, with GPT-5.5 now leading benchmarks.\u003C\u002Fp>\u003Cp>OpenAI has shipped six GPT-5 variants in less than nine months, and the differences are large enough to matter in real projects. The latest, \u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fgpt-5-5\u002F\" target=\"_blank\" rel=\"noopener\">GPT-5.5\u003C\u002Fa>, arrived on April 23, 2026 and posts 93.6% on GPQA Diamond, 82.7% on Terminal-Bench 2.0, and 78.7% on OSWorld-Verified.\u003C\u002Fp>\u003Cp>If you only remember one thing, remember this: GPT-5.0 was the baseline, GPT-5.1 made the system faster, GPT-5.2 pushed reasoning harder, GPT-5.3 cut cost, GPT-5.4 added computer use, and GPT-5.5 is the current top model. That is a lot of movement in a short window, and it changes how developers should pick models for chat, coding, research, and agent workflows.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Model\u003C\u002Fth>\u003Cth>Release\u003C\u002Fth>\u003Cth>Context\u003C\u002Fth>\u003Cth>API price per 1M tokens\u003C\u002Fth>\u003Cth>Key result\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>GPT-5.0\u003C\u002Ftd>\u003Ctd>Aug 7, 2025\u003C\u002Ftd>\u003Ctd>400K in \u002F 128K out\u003C\u002Ftd>\u003Ctd>$1.25 \u002F $10\u003C\u002Ftd>\u003Ctd>94.6% on AIME 2025\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>GPT-5.1\u003C\u002Ftd>\u003Ctd>Nov 13, 2025\u003C\u002Ftd>\u003Ctd>400K, 272K in\u003C\u002Ftd>\u003Ctd>$1.25 \u002F $10\u003C\u002Ftd>\u003Ctd>2 to 3x faster on simple tasks\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>GPT-5.2\u003C\u002Ftd>\u003Ctd>Dec 11, 2025\u003C\u002Ftd>\u003Ctd>400K, 272K in\u003C\u002Ftd>\u003Ctd>$1.75 \u002F $14\u003C\u002Ftd>\u003Ctd>100% on AIME 2025\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>GPT-5.3 Instant\u003C\u002Ftd>\u003Ctd>Mar 3, 2026\u003C\u002Ftd>\u003Ctd>400K\u003C\u002Ftd>\u003Ctd>~$0.30 \u002F ~$1.20\u003C\u002Ftd>\u003Ctd>26.8% fewer hallucinations than 5.2\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>GPT-5.4\u003C\u002Ftd>\u003Ctd>Mar 5, 2026\u003C\u002Ftd>\u003Ctd>1M API only\u003C\u002Ftd>\u003Ctd>$2.50 \u002F $15\u003C\u002Ftd>\u003Ctd>75.0% on OSWorld-Verified\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>GPT-5.5\u003C\u002Ftd>\u003Ctd>Apr 23, 2026\u003C\u002Ftd>\u003Ctd>1M API only\u003C\u002Ftd>\u003Ctd>$5 \u002F $30\u003C\u002Ftd>\u003Ctd>93.6% on GPQA Diamond\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>How the GPT-5 family changed so quickly\u003C\u002Fh2>\u003Cp>The speed of this rollout is the real story. OpenAI did not ship one model and let it age quietly; it kept splitting the family into specialized versions for speed, reasoning, cost, and agentic work. That means “best \u003Ca href=\"\u002Ftag\u002Fchatgpt\">ChatGPT\u003C\u002Fa> model” is no longer a single answer. It depends on whether you care about price, latency, \u003Ca href=\"\u002Ftag\u002Flong-context\">long context\u003C\u002Fa>, tool use, or \u003Ca href=\"\u002Ftag\u002Fbenchmark\">benchmark\u003C\u002Fa> strength.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780007585568-qbtw.png\" alt=\"GPT-5.0 to 5.5: Which ChatGPT Model Wins?\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fgpt-5\u002F\" target=\"_blank\" rel=\"noopener\">GPT-5.0\u003C\u002Fa> launched on August 7, 2025 as a unified system with a fast base model and a deeper reasoning layer called GPT-5 Thinking. A router chose the mode automatically, which removed the old ritual of manually switching between chat and reasoning models. For most users, that was the first big quality-of-life improvement in the family.\u003C\u002Fp>\u003Cul>\u003Cli>GPT-5.0: unified routing, 400K input context, 128K output context\u003C\u002Fli>\u003Cli>GPT-5.1: adaptive reasoning, same price as GPT-5.0, faster on easy prompts\u003C\u002Fli>\u003Cli>GPT-5.2: first to cross 90% on ARC-AGI-1\u003C\u002Fli>\u003Cli>GPT-5.4 and GPT-5.5: 1M-token API context and computer-use workflows\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Where each model actually differs\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fgpt-5-1\u002F\" target=\"_blank\" rel=\"noopener\">GPT-5.1\u003C\u002Fa> did not aim to be smarter in a dramatic way. It aimed to be more efficient. OpenAI’s adaptive reasoning system lets the model spend less compute on easy prompts and more on hard ones, which is why simple tasks can run 2 to 3 times faster than the standard mode. That matters more than it sounds, because speed changes how often people use a model during a workday.\u003C\u002Fp>\u003Cp>\u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fgpt-5-2\u002F\" target=\"_blank\" rel=\"noopener\">GPT-5.2\u003C\u002Fa> is the one that pushed reasoning into a new bracket. It hit 100% on AIME 2025 math, more than 90% on ARC-AGI-1 in Pro mode, and 80.0% on \u003Ca href=\"\u002Ftag\u002Fswe-bench-verified\">SWE-Bench Verified\u003C\u002Fa>. Those numbers tell a simple story: this was the model for hard problems, especially when accuracy mattered more than cost.\u003C\u002Fp>\u003Cblockquote>“The model is a significant leap in intelligence, and our most capable model yet,” OpenAI said in its GPT-5 launch announcement.\u003C\u002Fblockquote>\u003Cp>That quote matters because it shows how OpenAI framed the family from the start: not as one monolithic model, but as a stack of trade-offs. Once that framing is in place, the rest of the releases make sense. GPT-5.3 cut hallucinations and price, GPT-5.4 added desktop control, and GPT-5.5 raised the ceiling again.\u003C\u002Fp>\u003Ch2>Why GPT-5.4 changed agent workflows\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fgpt-5-4\u002F\" target=\"_blank\" rel=\"noopener\">GPT-5.4\u003C\u002Fa> is where the family became useful in a more operational sense. Its native computer use lets it click through interfaces, run commands, verify output, and loop through a build-run-verify-fix cycle. On \u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fevaluating-computer-use\u002F\" target=\"_blank\" rel=\"noopener\">OSWorld-Verified\u003C\u002Fa>, it scored 75.0%, above the measured human baseline of 72.4%.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780007586417-qmf6.png\" alt=\"GPT-5.0 to 5.5: Which ChatGPT Model Wins?\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>That is a meaningful line to cross. It means the model is not just answering questions or writing code snippets; it can interact with software like an operator. For teams building agents, that opens up workflows that used to require a human sitting in the loop for every step.\u003C\u002Fp>\u003Cul>\u003Cli>OSWorld-Verified: 75.0% for GPT-5.4 vs 72.4% human baseline\u003C\u002Fli>\u003Cli>SWE-Bench Pro: 57.7% for GPT-5.4, up from 55.6% for GPT-5.2\u003C\u002Fli>\u003Cli>FrontierMath: 47.6% for GPT-5.4, up from 40.3% for GPT-5.2\u003C\u002Fli>\u003Cli>Tool search cut token usage by 47% in tool-heavy workflows\u003C\u002Fli>\u003C\u002Ful>\u003Cp>There is also a practical pricing wrinkle. GPT-5.4’s 1M-token context window is API-only, and OpenAI charges extra once a request crosses 272K input tokens. So while the number sounds generous, the economics still push teams to think carefully about when they use the full window.\u003C\u002Fp>\u003Ch2>Why GPT-5.5 is the model most teams will notice\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fopenai.com\u002Findex\u002Fgpt-5-5\u002F\" target=\"_blank\" rel=\"noopener\">GPT-5.5\u003C\u002Fa> is the model that makes the family feel complete. It beats GPT-5.4 on the benchmarks that matter for knowledge work and coding, including 93.6% on GPQA Diamond, 82.7% on Terminal-Bench 2.0, and 78.7% on OSWorld-Verified. Its Pro version goes further, with 90.1% on BrowseComp and 39.6% on FrontierMath Tier 4.\u003C\u002Fp>\u003Cp>That said, the jump comes with a price jump too. GPT-5.5 API pricing is $5 per 1M input tokens and $30 per 1M output tokens, while GPT-5.4 sits at $2.50 and $15. If you are building a product with heavy \u003Ca href=\"\u002Ftag\u002Finference\">inference\u003C\u002Fa> volume, GPT-5.5 is the model you reserve for hard queries, not the default for everything.\u003C\u002Fp>\u003Cp>For comparison, GPT-5.3 Instant is still the bargain model in the family at roughly $0.30 per 1M input tokens and $1.20 per 1M output tokens. It is a much better fit for everyday writing, support, and search-backed answers than for deep reasoning. That price gap is large enough that model routing matters as much as model quality.\u003C\u002Fp>\u003Ch2>What developers should pick right now\u003C\u002Fh2>\u003Cp>If you are building with the GPT-5 family, the choice is mostly about workload shape. GPT-5.3 is the sensible default for cost-sensitive, high-volume tasks. GPT-5.2 still makes sense when reasoning quality matters more than latency. GPT-5.4 is the one to use for agentic software work, especially if your app needs to operate across desktop tools or long sessions.\u003C\u002Fp>\u003Cp>GPT-5.5 is the model to test when you want the best overall performance and can tolerate the price. That is especially true for research assistants, coding copilots, and enterprise workflows where a wrong answer costs more than a few cents in tokens. If you want a compact comparison with more context on OpenAI releases, our \u003Ca href=\"\u002Fnews\u002Fchatgpt-pricing-guide\" target=\"_blank\" rel=\"noopener\">ChatGPT pricing guide\u003C\u002Fa> is a useful companion read.\u003C\u002Fp>\u003Cp>One more detail matters: OpenAI has already said GPT-5.2 Thinking is being retired on June 3, 2026, which is a reminder that model families are now moving targets. If you are shipping product features on top of these APIs, you need fallback logic and a plan for version changes, because the model you test today may not be the one you get next quarter.\u003C\u002Fp>\u003Ch2>What comes after GPT-5.5\u003C\u002Fh2>\u003Cp>\u003Ca href=\"https:\u002F\u002Fopenai.com\" target=\"_blank\" rel=\"noopener\">OpenAI\u003C\u002Fa> has not shipped GPT-6 yet, but the direction is already visible. The next step is likely to focus on persistent memory, longer-running autonomous agents, and better control over multi-step work. If that happens, the big question will not be whether the model can answer more questions. It will be whether it can keep state, remember goals, and complete work without losing the thread halfway through.\u003C\u002Fp>\u003Cp>For now, the practical takeaway is simple: do not choose a GPT-5 model by headline benchmark alone. Pick the cheapest model that can handle your failure mode, then move up only when the task actually needs more reasoning, more context, or computer control. That is the difference between using AI as a demo and using it as infrastructure.\u003C\u002Fp>","OpenAI’s GPT-5 family grew from a 400K-token baseline to 1M-token agentic models, with GPT-5.5 now leading benchmarks.","felloai.com","https:\u002F\u002Ffelloai.com\u002Fthe-ultimate-chatgpt-model-comparison\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780007585568-qbtw.png","model-release","en","4a50f829-80a7-4303-9e77-6baf4df97903",[17,18,19,20,21],"GPT-5","ChatGPT models","OpenAI","benchmark comparison","agentic coding",[23,24,25],"GPT-5.5 is OpenAI’s strongest GPT-5 model so far, with top scores on GPQA Diamond, Terminal-Bench 2.0, and OSWorld-Verified.","GPT-5.4 matters because it adds native computer use and a 1M-token API context window for agent workflows.","GPT-5.3 is the best low-cost option, while GPT-5.2 still leads on hard reasoning tasks like ARC-AGI-1 and AIME 2025.",10,"2026-05-28T22:32:39.599972+00:00","2026-05-28T22:32:39.586+00:00","1bae1133-d241-4581-9332-fbf39690c319",{"tags":31,"relatedLang":42,"relatedPosts":46},[32,34,36,38,40],{"name":19,"slug":33},"openai",{"name":18,"slug":35},"chatgpt-models",{"name":17,"slug":37},"gpt-5",{"name":21,"slug":39},"agentic-coding",{"name":20,"slug":41},"benchmark-comparison",{"id":15,"slug":43,"title":44,"language":45},"gpt-5-model-comparison-5-0-to-5-5-zh","GPT-5.0 到 5.5 怎麼選","zh",[47,53,59,65,71,77],{"id":48,"slug":49,"title":50,"cover_image":51,"image_url":51,"created_at":52,"category":13},"58aa41ca-2c5f-44c6-ab07-2002473e95b1","gemini-1-5-pro-002-flash-002-2-0-flash-update-en","Gemini 1.5 Pro-002, Flash-002 and 2.0 Flash update Google AI","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780999383257-jccn.png","2026-06-09T10:02:28.362637+00:00",{"id":54,"slug":55,"title":56,"cover_image":57,"image_url":57,"created_at":58,"category":13},"435fc551-a461-444a-bf95-dbf5685cfac0","minimax-m3-open-weight-coding-win-en","MiniMax M3 Proves Open-Weight Can Still Win on Coding","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780968781159-odhi.png","2026-06-09T01:32:31.256895+00:00",{"id":60,"slug":61,"title":62,"cover_image":63,"image_url":63,"created_at":64,"category":13},"12af5a0d-1bbf-4a50-a391-b53f8003f234","gemini-35-flash-pricing-benchmarks-en","Gemini 3.5 Flash Pricing, Context, Benchmarks","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780840981235-e7hm.png","2026-06-07T14:02:30.280485+00:00",{"id":66,"slug":67,"title":68,"cover_image":69,"image_url":69,"created_at":70,"category":13},"0e767e9d-5d17-4cd0-b6ee-0328f89eb49b","gemma-4-12b-specs-benchmarks-run-locally-en","Gemma 4 12B: Specs, Benchmarks & How to Run It Locally","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780777984661-5ymr.png","2026-06-06T20:32:25.294996+00:00",{"id":72,"slug":73,"title":74,"cover_image":75,"image_url":75,"created_at":76,"category":13},"9d15f962-739d-44f8-a7f9-11bca64d38e0","best-kimi-models-2026-k2-5-vs-k2-thinking-en","Best Kimi Models in 2026: K2.5 vs K2 Thinking","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780770786284-shy0.png","2026-06-06T18:32:39.779504+00:00",{"id":78,"slug":79,"title":80,"cover_image":81,"image_url":81,"created_at":82,"category":13},"34547376-5d6b-4453-8d80-8072d8ac36ed","kimi-k2-6-open-source-coding-agent-swarm-en","Kimi K2.6 adds open-source coding and agent swarm","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780761781526-wop4.png","2026-06-06T16:02:22.26883+00:00",[84,89,94,99,104,109,114,119,124,129],{"id":85,"slug":86,"title":87,"created_at":88},"d4cffde7-9b50-4cc7-bb68-8bc9e3b15477","nvidia-rubin-ai-supercomputer-en","NVIDIA Unveils Rubin: A Leap in AI Supercomputing","2026-03-25T16:24:35.155565+00:00",{"id":90,"slug":91,"title":92,"created_at":93},"eab919b9-fbac-4048-89fc-afad6749ccef","google-gemini-ai-innovations-2026-en","Google's AI Leap with Gemini Innovations in 2026","2026-03-25T16:27:18.841838+00:00",{"id":95,"slug":96,"title":97,"created_at":98},"5f5cfc67-3384-4816-a8f6-19e44d90113d","gap-google-gemini-ai-checkout-en","Gap Teams Up with Google Gemini for AI-Driven Checkout","2026-03-25T16:27:46.483272+00:00",{"id":100,"slug":101,"title":102,"created_at":103},"f6d04567-47f6-49ec-804c-52e61ab91225","ai-model-release-wave-march-2026-en","Navigating the AI Model Release Wave of March 2026","2026-03-25T16:28:45.409716+00:00",{"id":105,"slug":106,"title":107,"created_at":108},"895c150c-569e-4fdf-939d-dade785c990e","small-language-models-transform-ai-en","Small Language Models: Llama 3.2 and Phi-3 Transform AI","2026-03-25T16:30:26.688313+00:00",{"id":110,"slug":111,"title":112,"created_at":113},"38eb1d26-d961-4fd3-ae12-9c4089680f5f","midjourney-v8-alpha-features-pricing-en","Midjourney V8 Alpha: A Deep Dive into Its Features and Pricing","2026-03-26T01:25:36.387587+00:00",{"id":115,"slug":116,"title":117,"created_at":118},"bf36bb9e-3444-4fb8-ab19-0df6bc9d8271","rag-2026-indispensable-ai-bridge-en","RAG in 2026: The Indispensable AI Bridge","2026-03-26T01:28:34.472046+00:00",{"id":120,"slug":121,"title":122,"created_at":123},"60881d6d-2310-44ef-b1fb-7f98e9dd2f0e","xiaomi-mimo-trio-agents-robots-voice-en","Xiaomi’s MiMo trio targets agents, robots, and voice","2026-03-28T03:05:08.899895+00:00",{"id":125,"slug":126,"title":127,"created_at":128},"f063d8d1-41d1-4de4-8ebc-6c40511b9369","xiaomi-mimo-v2-pro-1t-moe-agents-en","Xiaomi MiMo-V2-Pro: 1T MoE Model for Agents","2026-03-28T03:06:19.238032+00:00",{"id":130,"slug":131,"title":132,"created_at":133},"a1379e9a-6785-4ff5-9b0a-8cff55f8264f","cursor-composer-2-started-from-kimi-en","Cursor’s Composer 2 started from Kimi","2026-03-28T03:11:59.132398+00:00"]