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Explain the similarities and differences between Google AI Studio Build and Cursor

Written by
Audrey Miles
Updated on:June-13th-2025
Recommendation

In-depth analysis of Google AI Studio and Cursor, exploring the similarities and differences of AI programming tools and their applicable scenarios.

Core content:
1. Tool positioning: comparison between full-stack lab and editor assistant
2. Underlying model: application differences between Google's self-developed model and hybrid model
3. User experience: experience differences from high-speed rail IDE to regional bus IDE

Yang Fangxian
Founder of 53A/Most Valuable Expert of Tencent Cloud (TVP)
Among AI programming tools, I have frequently used Google AI Studio and Cursor. They can both achieve "smart code generation just by describing requirements". But in actual use, there are many differences between the two, from project framework to code generation.
This article will analyze the similarities and differences between the two from multiple dimensions, including tool positioning, scaffolding, models, and interactive experience.

Tool positioning: Full-stack Lab VS Editor Assistant


Google AI Studio  Build :

  • Core positioning : End-to-end cloud AI platform. You can build an APP with just one sentence, directly generate code, perform online testing, prompt debugging, demo, and deploy on the cloud.

  • Output structure : complete project scaffolding - front-end, back-end, environment configuration file, deployment script, sample data, README.

Cursor :

  • Core positioning : Locally deployed intelligent IDE. Focus on code snippet completion, refactoring, error detection, and code generation after submitting requirements.

  • Output structure : local files and functions - generated or rewritten "in place" only in the current workspace, seamlessly connected to the existing project structure.


Underlying model: self-developed Gemini  VS hybrid model


Google AI Studio  Build :
  • Native access to Google's self-developed large models (Gemini 2.5 Pro/Flash, multi-modal, etc.)

Cursor :

  • Mix multiple cutting-edge models, focusing on optimizing low latency, localized reasoning, etc.



Scaffolding full bucket  VS half bucket


Take creating a puzzle Web APP as an example.


Google AI Studio  Build :
  • There are many built-in APP templates, such as Firebase+React+Tailwind+Typescript, etc. Generate a complete directory structure with one click

Cursor :

  • It adopts the strategy of "generating on-site" according to demand. It will create core files directly in the current directory according to user needs, but the coupling between files is relatively tight.

The different processing concepts of the two can be felt through the directory structure of this project. Google AI Studio creates different files according to functions, and the degree of decoupling is relatively high. In this way, when communicating with AI, you can avoid the situation where more bugs are caused by modifying a bug.


User experience: High-speed rail IDE  VS regional bus IDE


Google AI Studio  Build :
The whole process is very pleasant and smooth. As long as the requirements are described clearly, there is a 90% probability that the function can be accurately implemented in one go. The built-in real-time demo interface allows for demonstration, rapid optimization, and deployment while generating, which is very easy.
Cursor :

It is not as good as Google AI Studio because the generated files are often more coupled, which often leads to more bugs when fixing a bug, and then you need to repeatedly adjust the prompt to make the Cursor understand the real intention.


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