[{"data":1,"prerenderedAt":1146},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"model-gpt-5":9},{"tools":4,"reviews":5},65,7,{"tools":4,"reviews":5,"playbooks":7,"news":8},10,8,{"id":10,"title":11,"apiCompatible":12,"benchmarks":14,"body":30,"category":1111,"contextWindow":1112,"description":1113,"extension":1114,"maxOutput":1115,"meta":1116,"navigation":123,"path":1117,"pricing":1118,"published":1119,"relatedTools":1120,"releaseDate":1124,"seo":1125,"slug":1126,"stem":1127,"strengths":1128,"updated":1119,"useCases":1134,"vendor":1139,"vendorEn":1139,"weaknesses":1140,"__hash__":1145},"models\u002Fmodels\u002Fgpt-5.md","GPT-5",[13],"openai",[15,18,21,24,27],{"name":16,"score":17},"SWE-bench Verified","68.0%",{"name":19,"score":20},"HumanEval","94.6%",{"name":22,"score":23},"MMLU","91.2%",{"name":25,"score":26},"GPQA Diamond","62.5%",{"name":28,"score":29},"MATH-500","98.4%",{"type":31,"value":32,"toc":1089},"minimark",[33,37,41,44,48,67,70,77,208,214,217,220,236,239,242,246,249,402,406,414,569,575,579,582,602,609,612,715,718,764,767,770,792,796,799,887,890,894,1000,1006,1009,1054,1057,1085],[34,35,36],"h2",{"id":36},"概述",[38,39,40],"p",{},"GPT-5 是 OpenAI 于 2025 年 8 月发布的旗舰模型。400K token 上下文窗口和 128K 输出窗口使其成为处理超长内容的最佳选择之一。在推理、数学、多模态方面均为顶级水平。",[34,42,43],{"id":43},"核心能力",[45,46,47],"h3",{"id":47},"超长上下文",[38,49,50,51,56,57,61,62,66],{},"400K token 的上下文窗口是 ",[52,53,55],"a",{"href":54},"\u002Fmodels\u002Fclaude-sonnet-4.html","Claude Sonnet 4","（200K）的两倍。对于需要分析整个代码仓库、超长文档或多文件对比的场景，GPT-5 是更好的选择。但",[58,59,60],"strong",{},"实测上下文超过 200K 后质量下降明显","，参考 ",[52,63,65],{"href":64},"\u002Fwiki\u002Fcontext-engineering.html","Context Engineering"," 里的「中间遗忘」现象。",[45,68,69],{"id":69},"推理能力",[38,71,72,73,76],{},"在 MATH-500 上拿到 98.4%，GPQA Diamond 62.5%，科学推理能力领先。GPT-5 是 OpenAI 把 GPT 主线和 o-series 推理线",[58,74,75],{},"合并","后的产物——内置 reasoning effort 控制：",[78,79,84],"pre",{"className":80,"code":81,"language":82,"meta":83,"style":83},"language-python shiki shiki-themes github-light github-dark","from openai import OpenAI\nclient = OpenAI()\n\nresp = client.chat.completions.create(\n    model=\"gpt-5\",\n    reasoning_effort=\"high\",   # low \u002F medium \u002F high\n    messages=[{\"role\": \"user\", \"content\": \"Prove that...\"}],\n)\n","python","",[85,86,87,106,118,125,136,152,170,203],"code",{"__ignoreMap":83},[88,89,92,96,100,103],"span",{"class":90,"line":91},"line",1,[88,93,95],{"class":94},"szBVR","from",[88,97,99],{"class":98},"sVt8B"," openai ",[88,101,102],{"class":94},"import",[88,104,105],{"class":98}," OpenAI\n",[88,107,109,112,115],{"class":90,"line":108},2,[88,110,111],{"class":98},"client ",[88,113,114],{"class":94},"=",[88,116,117],{"class":98}," OpenAI()\n",[88,119,121],{"class":90,"line":120},3,[88,122,124],{"emptyLinePlaceholder":123},true,"\n",[88,126,128,131,133],{"class":90,"line":127},4,[88,129,130],{"class":98},"resp ",[88,132,114],{"class":94},[88,134,135],{"class":98}," client.chat.completions.create(\n",[88,137,139,143,145,149],{"class":90,"line":138},5,[88,140,142],{"class":141},"s4XuR","    model",[88,144,114],{"class":94},[88,146,148],{"class":147},"sZZnC","\"gpt-5\"",[88,150,151],{"class":98},",\n",[88,153,155,158,160,163,166],{"class":90,"line":154},6,[88,156,157],{"class":141},"    reasoning_effort",[88,159,114],{"class":94},[88,161,162],{"class":147},"\"high\"",[88,164,165],{"class":98},",   ",[88,167,169],{"class":168},"sJ8bj","# low \u002F medium \u002F high\n",[88,171,172,175,177,180,183,186,189,192,195,197,200],{"class":90,"line":5},[88,173,174],{"class":141},"    messages",[88,176,114],{"class":94},[88,178,179],{"class":98},"[{",[88,181,182],{"class":147},"\"role\"",[88,184,185],{"class":98},": ",[88,187,188],{"class":147},"\"user\"",[88,190,191],{"class":98},", ",[88,193,194],{"class":147},"\"content\"",[88,196,185],{"class":98},[88,198,199],{"class":147},"\"Prove that...\"",[88,201,202],{"class":98},"}],\n",[88,204,205],{"class":90,"line":8},[88,206,207],{"class":98},")\n",[38,209,210,213],{},[85,211,212],{},"high"," 模式下模型会用大量 reasoning token 思考再回答，复杂数学\u002F算法成功率显著上升，但 output token 消费增加 3-5 倍。",[45,215,216],{"id":216},"多模态",[38,218,219],{},"原生支持图片、音频和视频输入。可以：",[221,222,223,227,230,233],"ul",{},[224,225,226],"li",{},"分析 UI 截图并生成前端代码",[224,228,229],{},"理解白板照片中的架构图",[224,231,232],{},"分析数据图表",[224,234,235],{},"转录 + 理解音频内容",[45,237,238],{"id":238},"编程",[38,240,241],{},"SWE-bench Verified 68.0%，略低于 Claude Sonnet 4 的 72.7%。实际使用中，Cursor 用户反馈 Claude Sonnet 4 在多文件改写和代码审查方面更稳定，但 GPT-5 在算法实现和数学密集型代码上更强。",[34,243,245],{"id":244},"api-调用示例","API 调用示例",[45,247,248],{"id":248},"基础调用",[78,250,252],{"className":80,"code":251,"language":82,"meta":83,"style":83},"from openai import OpenAI\nclient = OpenAI(api_key=\"sk-...\")\n\nresp = client.chat.completions.create(\n    model=\"gpt-5\",\n    messages=[\n        {\"role\": \"system\", \"content\": \"You are a senior engineer.\"},\n        {\"role\": \"user\", \"content\": \"Refactor this function.\"},\n    ],\n    temperature=1,           # 推理模型保持默认 1\n)\nprint(resp.choices[0].message.content)\n",[85,253,254,264,283,287,295,305,314,338,359,365,382,387],{"__ignoreMap":83},[88,255,256,258,260,262],{"class":90,"line":91},[88,257,95],{"class":94},[88,259,99],{"class":98},[88,261,102],{"class":94},[88,263,105],{"class":98},[88,265,266,268,270,273,276,278,281],{"class":90,"line":108},[88,267,111],{"class":98},[88,269,114],{"class":94},[88,271,272],{"class":98}," OpenAI(",[88,274,275],{"class":141},"api_key",[88,277,114],{"class":94},[88,279,280],{"class":147},"\"sk-...\"",[88,282,207],{"class":98},[88,284,285],{"class":90,"line":120},[88,286,124],{"emptyLinePlaceholder":123},[88,288,289,291,293],{"class":90,"line":127},[88,290,130],{"class":98},[88,292,114],{"class":94},[88,294,135],{"class":98},[88,296,297,299,301,303],{"class":90,"line":138},[88,298,142],{"class":141},[88,300,114],{"class":94},[88,302,148],{"class":147},[88,304,151],{"class":98},[88,306,307,309,311],{"class":90,"line":154},[88,308,174],{"class":141},[88,310,114],{"class":94},[88,312,313],{"class":98},"[\n",[88,315,316,319,321,323,326,328,330,332,335],{"class":90,"line":5},[88,317,318],{"class":98},"        {",[88,320,182],{"class":147},[88,322,185],{"class":98},[88,324,325],{"class":147},"\"system\"",[88,327,191],{"class":98},[88,329,194],{"class":147},[88,331,185],{"class":98},[88,333,334],{"class":147},"\"You are a senior engineer.\"",[88,336,337],{"class":98},"},\n",[88,339,340,342,344,346,348,350,352,354,357],{"class":90,"line":8},[88,341,318],{"class":98},[88,343,182],{"class":147},[88,345,185],{"class":98},[88,347,188],{"class":147},[88,349,191],{"class":98},[88,351,194],{"class":147},[88,353,185],{"class":98},[88,355,356],{"class":147},"\"Refactor this function.\"",[88,358,337],{"class":98},[88,360,362],{"class":90,"line":361},9,[88,363,364],{"class":98},"    ],\n",[88,366,367,370,372,376,379],{"class":90,"line":7},[88,368,369],{"class":141},"    temperature",[88,371,114],{"class":94},[88,373,375],{"class":374},"sj4cs","1",[88,377,378],{"class":98},",           ",[88,380,381],{"class":168},"# 推理模型保持默认 1\n",[88,383,385],{"class":90,"line":384},11,[88,386,207],{"class":98},[88,388,390,393,396,399],{"class":90,"line":389},12,[88,391,392],{"class":374},"print",[88,394,395],{"class":98},"(resp.choices[",[88,397,398],{"class":374},"0",[88,400,401],{"class":98},"].message.content)\n",[45,403,405],{"id":404},"structured-outputs强约束-json","Structured Outputs（强约束 JSON）",[38,407,408,409,413],{},"GPT-5 的 ",[52,410,412],{"href":411},"\u002Fwiki\u002Ffunction-calling.html","Structured Outputs"," 是工具调用最稳的：",[78,415,417],{"className":80,"code":416,"language":82,"meta":83,"style":83},"from pydantic import BaseModel\n\nclass CodeReview(BaseModel):\n    severity: str\n    issues: list[str]\n    suggestions: list[str]\n\nresp = client.chat.completions.parse(\n    model=\"gpt-5\",\n    response_format=CodeReview,\n    messages=[{\"role\": \"user\", \"content\": \"Review:\\n\" + code}],\n)\nreview: CodeReview = resp.choices[0].message.parsed\n",[85,418,419,431,435,453,461,472,481,485,494,504,514,549,553],{"__ignoreMap":83},[88,420,421,423,426,428],{"class":90,"line":91},[88,422,95],{"class":94},[88,424,425],{"class":98}," pydantic ",[88,427,102],{"class":94},[88,429,430],{"class":98}," BaseModel\n",[88,432,433],{"class":90,"line":108},[88,434,124],{"emptyLinePlaceholder":123},[88,436,437,440,444,447,450],{"class":90,"line":120},[88,438,439],{"class":94},"class",[88,441,443],{"class":442},"sScJk"," CodeReview",[88,445,446],{"class":98},"(",[88,448,449],{"class":442},"BaseModel",[88,451,452],{"class":98},"):\n",[88,454,455,458],{"class":90,"line":127},[88,456,457],{"class":98},"    severity: ",[88,459,460],{"class":374},"str\n",[88,462,463,466,469],{"class":90,"line":138},[88,464,465],{"class":98},"    issues: list[",[88,467,468],{"class":374},"str",[88,470,471],{"class":98},"]\n",[88,473,474,477,479],{"class":90,"line":154},[88,475,476],{"class":98},"    suggestions: list[",[88,478,468],{"class":374},[88,480,471],{"class":98},[88,482,483],{"class":90,"line":5},[88,484,124],{"emptyLinePlaceholder":123},[88,486,487,489,491],{"class":90,"line":8},[88,488,130],{"class":98},[88,490,114],{"class":94},[88,492,493],{"class":98}," client.chat.completions.parse(\n",[88,495,496,498,500,502],{"class":90,"line":361},[88,497,142],{"class":141},[88,499,114],{"class":94},[88,501,148],{"class":147},[88,503,151],{"class":98},[88,505,506,509,511],{"class":90,"line":7},[88,507,508],{"class":141},"    response_format",[88,510,114],{"class":94},[88,512,513],{"class":98},"CodeReview,\n",[88,515,516,518,520,522,524,526,528,530,532,534,537,540,543,546],{"class":90,"line":384},[88,517,174],{"class":141},[88,519,114],{"class":94},[88,521,179],{"class":98},[88,523,182],{"class":147},[88,525,185],{"class":98},[88,527,188],{"class":147},[88,529,191],{"class":98},[88,531,194],{"class":147},[88,533,185],{"class":98},[88,535,536],{"class":147},"\"Review:",[88,538,539],{"class":374},"\\n",[88,541,542],{"class":147},"\"",[88,544,545],{"class":94}," +",[88,547,548],{"class":98}," code}],\n",[88,550,551],{"class":90,"line":389},[88,552,207],{"class":98},[88,554,556,559,561,564,566],{"class":90,"line":555},13,[88,557,558],{"class":98},"review: CodeReview ",[88,560,114],{"class":94},[88,562,563],{"class":98}," resp.choices[",[88,565,398],{"class":374},[88,567,568],{"class":98},"].message.parsed\n",[38,570,571,574],{},[85,572,573],{},"strict: true"," 模式保证 100% 符合 schema，省去后处理校验。",[45,576,578],{"id":577},"prompt-cache自动开启","Prompt Cache（自动开启）",[38,580,581],{},"OpenAI 的 cache 是自动触发的——任何 ≥1024 token 的前缀重复出现就自动命中，Cache Read 价格 $0.125\u002FM（-90%）：",[78,583,585],{"className":80,"code":584,"language":82,"meta":83,"style":83},"# 第一次调用：input 完整计费\n# 第二次同样 system prompt 开头：input 前缀自动 cache 命中\n# 无需任何代码改动，OpenAI 后台自动判断\n",[85,586,587,592,597],{"__ignoreMap":83},[88,588,589],{"class":90,"line":91},[88,590,591],{"class":168},"# 第一次调用：input 完整计费\n",[88,593,594],{"class":90,"line":108},[88,595,596],{"class":168},"# 第二次同样 system prompt 开头：input 前缀自动 cache 命中\n",[88,598,599],{"class":90,"line":120},[88,600,601],{"class":168},"# 无需任何代码改动，OpenAI 后台自动判断\n",[38,603,604,605,608],{},"要利用好它，",[58,606,607],{},"动态内容必须放在 messages 末尾","，不要在 system 里插时间戳。",[34,610,611],{"id":611},"关键参数",[613,614,615,631],"table",{},[616,617,618],"thead",{},[619,620,621,625,628],"tr",{},[622,623,624],"th",{},"参数",[622,626,627],{},"推荐值",[622,629,630],{},"说明",[632,633,634,656,669,686,703],"tbody",{},[619,635,636,642,645],{},[637,638,639],"td",{},[85,640,641],{},"temperature",[637,643,644],{},"1（默认）",[637,646,647,648,651,652],{},"GPT-5 是推理模型，",[58,649,650],{},"不要改 temperature","，详见 ",[52,653,655],{"href":654},"\u002Fwiki\u002Ftemperature-top-p.html","Temperature 与 Top-P",[619,657,658,663,666],{},[637,659,660],{},[85,661,662],{},"reasoning_effort",[637,664,665],{},"low \u002F medium \u002F high",[637,667,668],{},"控制内部推理深度，复杂任务 high",[619,670,671,676,679],{},[637,672,673],{},[85,674,675],{},"max_completion_tokens",[637,677,678],{},"显式设",[637,680,681,682,685],{},"GPT-5 用这个而非 ",[85,683,684],{},"max_tokens","，老参数被废弃",[619,687,688,693,696],{},[637,689,690],{},[85,691,692],{},"seed",[637,694,695],{},"固定值",[637,697,698,699,702],{},"best-effort 复现，配合 ",[85,700,701],{},"system_fingerprint"," 验证",[619,704,705,710,712],{},[637,706,707],{},[85,708,709],{},"top_p",[637,711,644],{},[637,713,714],{},"不要同时调 temperature 和 top_p",[34,716,717],{"id":717},"定价",[613,719,720,730],{},[616,721,722],{},[619,723,724,727],{},[622,725,726],{},"项目",[622,728,729],{},"价格",[632,731,732,740,748,756],{},[619,733,734,737],{},[637,735,736],{},"Input",[637,738,739],{},"$1.25 \u002F 百万 token",[619,741,742,745],{},[637,743,744],{},"Output",[637,746,747],{},"$10 \u002F 百万 token",[619,749,750,753],{},[637,751,752],{},"Cached Input",[637,754,755],{},"$0.125 \u002F 百万 token",[619,757,758,761],{},[637,759,760],{},"Batch API（24h）",[637,762,763],{},"-50%",[38,765,766],{},"GPT-5 的 Input 价格仅为 Claude Sonnet 4 的 42%，Output 价格低 33%。对于高吞吐场景（批量处理、大量 API 调用），GPT-5 的成本优势明显。",[45,768,769],{"id":769},"实际账单注意",[221,771,772,786],{},[224,773,774,777,778,781,782,785],{},[58,775,776],{},"reasoning token"," 算 output：开 ",[85,779,780],{},"reasoning_effort=high"," 时单条对话 output 可能是普通模式的 3-5 倍。账单会单独显示 ",[85,783,784],{},"reasoning_tokens"," 字段。",[224,787,788,791],{},[58,789,790],{},"Batch API","：异步批量请求 24 小时内出结果，所有价格 -50%，非常适合离线数据处理。",[34,793,795],{"id":794},"限流tier-体系","限流（Tier 体系）",[38,797,798],{},"OpenAI 的 rate limit 按账户消费分 5 个 Tier：",[613,800,801,817],{},[616,802,803],{},[619,804,805,808,811,814],{},[622,806,807],{},"Tier",[622,809,810],{},"月消费门槛",[622,812,813],{},"RPM",[622,815,816],{},"TPM",[632,818,819,833,847,860,874],{},[619,820,821,824,827,830],{},[637,822,823],{},"Tier 1",[637,825,826],{},"$5",[637,828,829],{},"500",[637,831,832],{},"30K",[619,834,835,838,841,844],{},[637,836,837],{},"Tier 2",[637,839,840],{},"$50",[637,842,843],{},"5,000",[637,845,846],{},"450K",[619,848,849,852,855,857],{},[637,850,851],{},"Tier 3",[637,853,854],{},"$100",[637,856,843],{},[637,858,859],{},"800K",[619,861,862,865,868,871],{},[637,863,864],{},"Tier 4",[637,866,867],{},"$250",[637,869,870],{},"10,000",[637,872,873],{},"2M",[619,875,876,879,882,884],{},[637,877,878],{},"Tier 5",[637,880,881],{},"$1,000+",[637,883,870],{},[637,885,886],{},"30M",[38,888,889],{},"生产环境跑量前先把 Tier 提到 3+，否则会频繁 429。Token 限流（TPM）比请求限流（RPM）更容易先打到，长 prompt 场景尤其。",[34,891,893],{"id":892},"与-claude-sonnet-4-怎么选","与 Claude Sonnet 4 怎么选",[613,895,896,907],{},[616,897,898],{},[619,899,900,903,905],{},[622,901,902],{},"维度",[622,904,11],{},[622,906,55],{},[632,908,909,920,929,940,950,959,970,981,990],{},[619,910,911,914,917],{},[637,912,913],{},"编程（Cursor\u002FClaude Code）",[637,915,916],{},"★★★★☆",[637,918,919],{},"★★★★★",[619,921,922,925,927],{},[637,923,924],{},"推理\u002F数学",[637,926,919],{},[637,928,916],{},[619,930,931,934,937],{},[637,932,933],{},"上下文长度",[637,935,936],{},"400K",[637,938,939],{},"200K",[619,941,942,944,947],{},[637,943,216],{},[637,945,946],{},"原生（图+音+视频）",[637,948,949],{},"仅图片",[619,951,952,955,957],{},[637,953,954],{},"工具调用 \u002F Structured Outputs",[637,956,919],{},[637,958,919],{},[619,960,961,964,967],{},[637,962,963],{},"Input 价格",[637,965,966],{},"$1.25\u002FM",[637,968,969],{},"$3\u002FM",[619,971,972,975,978],{},[637,973,974],{},"Output 价格",[637,976,977],{},"$10\u002FM",[637,979,980],{},"$15\u002FM",[619,982,983,986,988],{},[637,984,985],{},"Agent 工具调用稳定性",[637,987,916],{},[637,989,919],{},[619,991,992,995,998],{},[637,993,994],{},"国内可用",[637,996,997],{},"❌",[637,999,997],{},[38,1001,1002,1005],{},[58,1003,1004],{},"建议","：主力编程用 Claude Sonnet 4，需要超长上下文 \u002F 多模态 \u002F 复杂推理时切 GPT-5。混搭最香。",[34,1007,1008],{"id":1008},"避坑清单",[221,1010,1011,1022,1031,1037,1048],{},[224,1012,1013,1018,1019,1021],{},[58,1014,1015,1016],{},"不要再用 ",[85,1017,684],{},"：GPT-5 上是 ",[85,1020,675],{},"。老代码迁过来会报错或行为异常。",[224,1023,1024,1027,1028,1030],{},[58,1025,1026],{},"不要把 temperature 设 0","：推理模型设 0 反而降质量，详见 ",[52,1029,655],{"href":654},"。",[224,1032,1033,1036],{},[58,1034,1035],{},"reasoning_effort 不要默认 high","：贵且慢。默认 medium，遇到难题再升 high。",[224,1038,1039,1042,1043,1047],{},[58,1040,1041],{},"400K 不要塞满","：上下文超过 200K 后召回精度下降明显，配合 ",[52,1044,1046],{"href":1045},"\u002Fwiki\u002Frag.html","RAG"," 或检索式压缩更靠谱。",[224,1049,1050,1053],{},[58,1051,1052],{},"多模态 input 也算 token","：一张 1024×1024 图片约消耗 ~1000 input token，批量处理要算账。",[34,1055,1056],{"id":1056},"延伸阅读",[221,1058,1059,1069,1075,1080],{},[224,1060,1061,1062,1064,1065],{},"对比同档：",[52,1063,55],{"href":54}," \u002F ",[52,1066,1068],{"href":1067},"\u002Fmodels\u002Fgemini-2.5-pro.html","Gemini 2.5 Pro",[224,1070,1071,1072],{},"调用模式：",[52,1073,1074],{"href":411},"Function Calling",[224,1076,1077,1078],{},"长上下文：",[52,1079,65],{"href":64},[224,1081,1082,1083],{},"推理参数：",[52,1084,655],{"href":654},[1086,1087,1088],"style",{},"html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .s4XuR, html code.shiki .s4XuR{--shiki-default:#E36209;--shiki-dark:#FFAB70}html pre.shiki code .sZZnC, html code.shiki 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$10\u002FM · Cached $0.125\u002FM","2026-06-21",[1121,1122,1123],"coding\u002Fide\u002Fcursor","coding\u002Fcopilot\u002Fgithub-copilot","coding\u002Fapi\u002Fopenrouter","2025-08-07",{"title":11,"description":1113},"gpt-5","models\u002Fgpt-5",[1129,1130,1131,1132,1133],"400K 超长上下文，全项目代码分析无压力","128K 输出窗口，长文件一次生成","多模态原生支持（图片、音频、视频）","推理能力顶级，数学\u002F科学\u002F代码全面","API 价格比 Sonnet 4 更低",[1135,1136,1137,1138],"通用推理与数学","多模态分析（图片\u002F截图理解）","长文档\u002F全项目代码分析","ChatGPT \u002F API 对话","OpenAI",[1141,1142,1143,1144],"国内无官方 API，需走中转","编程实操中不如 Claude Sonnet 4 稳定（Cursor 用户反馈）","延迟略高，流式首 token 慢于 Sonnet 4","知识截止较早","q83eVVwHgUNMitKxvtSqyVwIqO3IweXrUG_jjAJbzFg",1782316489331]