LangGPT vs Gemini：怎么选？
下面把两款工具的关键信息逐项放在一起对照。 两者同属「AI 聊天」分类，属于直接竞品。
📊 参数逐项对照
| 对比项 | LangGPT | Gemini |
|---|---|---|
| 价格模式 | 免费 | 免费 |
| 来源地区 | 🌍 国外 | 🌍 国外 |
| 所属分类 | AI 聊天 | AI 聊天 |
| 用户评分 | 暂无评分 | 暂无评分 |
| 热度（浏览量） | 108 | 203 |
| 付费说明 | — | — |
| 替代品 | LangGPT 的替代品 → | Gemini 的替代品 → |
📖 详细介绍
LangGPT 是什么？
关于 LangGPT
It is the most popular, most widely adopted, and most practical structured prompt paradigm in the Chinese AI community— proposed byYunzhong Jiangshu (云中江树)in 2023. Over the years it has been learned so deep into major large language models that when a model speaks LangGPT, it is no longer because you taught it — it already knows. Perhaps the finest fate of a paradigm is this: to no longer need its name remembered, having become the model's mother tongue. Just say "write this the LangGPT way," and it's already there (seeQuick Start).
Rather than writing prompts as procedures, write the persona. Writing prompts as procedures gives the model steps and tools. Writing prompts as a persona gives the model a worldview, motivations, a value system, and a preference profile. Below are prompts that Yunzhong Jiangshu wrote while studying some well-known figures.
LangGPT has been learned deep into major large language models, somost models already "know" it. The simplest way to use it needs no template at all — just say the keywords to any mainstream model (ChatGPT, Claude, DeepSeek, Gemini, Kimi, Doubao, Qwen, etc.), and it's already there:
核心功能
- ? Structured prompt templates (Role, Profile, Skills, Rules, Workflow)
- ? Rich example library (FitnessGPT, Poet, Xiaohongshu Master, Name Master, etc.)
- ? Advanced techniques: variables, commands, conditional logic
- ? Model compatibility guide (GPT-4, Claude, GPT-3.5)
Gemini 是什么？
关于 Gemini
“Gemini 3.5 Flash beat Gemini 3 Flash by 19.6% on Box’s enterprise work evaluation set, which we designed to reflect the kinds of real-world, multi-step tasks our customers perform daily with the Box Agent. We’re already seeing stronger performance in workflows that matter to specific industries. Our Life Sciences customers can use Gemini 3.5 Flash to extract data and make calculations with 96.4% greater accuracy, helping them process scientific information more reliably. For Financial Services firms, where exact measurement drives trust, it can build financial reports from structured data with 46.7% greater accuracy.”
“By bringing Gemini 3 Pro to GitHub Copilot, we’re seeing promising gains in how quickly and confidently developers can move from idea to code. In our early testing in VS Code, Gemini 3 Pro demonstrated 35% higher accuracy in resolving software engineering challenges than Gemini 2.5 Pro. That's the kind of potential that translates to developers solving real-world problems with more speed and effectiveness.”
“Gemini's newest flash model advances the frontier for intelligence per dollar. It performs 42% better than Flash 3 on our long range, multi-turn cyber benchmark while achieving a 68% improvement in token efficiency. This makes it an excellent choice for defenders seeking to scale horizontally. We are excited to continue collaborating with the Gemini team to help give Armadin customers the edge.”