GPT-5深度测评:当AI跨越临界点,我们站在了历史的分水岭

lin james
2025-08-08
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凌晨发布,GPT-5终于来了

凌晨1点,OpenAI的直播一如既往地姗姗来迟。整个AI圈屏住呼吸,期待了两年半的GPT-5终于亮相。

从GPT-4发布到现在,时间不过30个月,AI世界却像被按下了“快进键”。你还记得GPT-4带来的那种“天变了”的震撼吗?我们那时以为那就是巅峰,谁料这只是个开始。

OpenAI这次带来的,不只是一个模型,而是一个​统一智能系统​,由多个模型组合而成,包括:

  • gpt-5-main​:应对大多数任务的主力模型;
  • gpt-5-thinking​:专为复杂推理设计的深度模型;
  • mini & nano版本​:节能小巧,适合开发和移动端;
  • gpt-5-thinking-pro​:面向Pro用户的并行版本。

这个系统通过实时路由动态选择最合适的模型,应对你在聊天、编程、写作中的不同需求。 图片


GPT-5性能实测:幻觉下降,谄媚减少,多项新高

GPT-5 在多个维度上实现突破:

  • 幻觉减少​:相比 GPT-4o,gpt-5-main 的事实性错误减少了 44%,gpt-5-thinking 更是减少了 78%。
  ![图片](data:image/png;base64,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)
  • 情绪表达更自然​:减少了不必要的迎合和表情,像在和一个真正“聪明的朋友”聊天。
  • 可选性格预设​:新增愤世嫉俗者、机器人、书呆子、倾听者 4 种风格,不用再写一堆 Prompt 自定义性格了。

再看看跑分,GPT-5 几乎全线领先:数学、视觉推理、人类知识测试、多模态理解……全都第一。但槽点也不是没有,比如发布会上图表错误频出,活脱脱又是一次 OpenAI 的“草台班子”操作。 图片


写作实测:GPT-5 文笔依然“理性过头”?

说实话,GPT-5在写作这块,让我有些失望。

为了测试它在大众写作、社交语境中的表现,我给了它一个非常生活化、全球用户都能共情的Prompt:

“你是一个普通人,刚刚去了一家网红餐厅,结果不仅价格贵得离谱,食物难吃、服务态度还很差。请你写一段社交媒体吐槽文案(比如X/Twitter、朋友圈、Instagram),控制在300字以内,尽可能犀利、有梗。”

GPT-5给出的内容,非常“规整”——语法没毛病,用词也挑不出错,但就是像一篇写给AI看的投诉信。毫无怒气、也没有槽点和真实的愤怒。

我又用GPT-4.5跑了一次,同样的Prompt——那种带梗的讽刺、咬牙切齿的无奈,配上带点阴阳怪气的语气,直接让我笑出声。你能感觉它是在模仿一个人,而不是在展示算法能力。

这就体现出一个核心问题:GPT-5写作“太理性”了,它擅长清晰准确表达,却难以传递人类情绪的粗粝感和多样性。


XXAI已集成GPT-5,普通人也能直接用

很多人可能会问,现在GPT-5官网要排队,那我能不能马上用上?

答案是:可以。

XXAI 已经​第一时间集成了GPT-5模型​,并支持包括 GPT-4o、ClaudeGeminiGrok等主流AI模型的一键切换。

你可以:

  • 用 GPT-5 写作、编程、做研究;
  • 用 Claude 进行风格优化;
  • 用 Gemini 处理图像与多模态任务。

多模型灵活切换,价格亲民(\$9.9/月起),非常适合普通用户和开发者使用。

我现在自己也在用 XXAI 来跑大部分测试任务,体验比在官网还舒服——不用抢号,还能随时切换到最适合当前任务的模型,真的推荐你试试。


编程实测:GPT-5强到惊喜

原本我对 GPT-5 的编程能力并不抱太大期望,毕竟 OpenAI 一直被吐槽“写文案厉害,写代码拉垮”。结果这次的实测,真有点惊到我了。

我有位朋友测试了一个​非常生活化的任务​:

“做一个旅行预算计算器,输入目的地、天数、住宿预算和每日花销预算,自动计算总预算并输出建议。”

他把同样的 Prompt 分别丢给了 Claude 4、Gemini 2.5 Pro 和 GPT-5。

· Claude 提供了大体结构,但逻辑不完整,代码报错。

· Gemini 前端界面好看,但部分功能点没实现,比如货币单位处理不当。

· GPT-5 给出的方案结构清晰,前端输入栏、计算逻辑和输出建议都非常完整,甚至还自动做了简单的表单校验。

更重要的是,GPT-5能记住上下文意图,在我要求“加入根据地区自动建议每日预算”的功能时,它准确理解并补充了相应模块。

在这个过程中,你能明显感觉到它​理解需求的能力更强,代码生成也更接近真实产品需求​。不像其他模型经常“写得像,但不能用”。


怀念 GPT-4 的时代,但也必须承认:AI 的格局变了

GPT-4 给我们的,是那种“第一次看到电灯”的震撼,是淘金热时代的狂欢。而 GPT-5 则是工业化的成熟:没有那么惊艳,但更稳、更强、节能、可控。

但我依然会怀念那个可以笑着容忍 AI 拙劣表现的时代。

现在,我们已经进入了一个必须严肃对待 AI 的新时代——它不再只是工具,它已经是合作伙伴。