JSON to Pydantic Model Generator (v1 & v2)




JSON to Pydantic Model Generator is an online tool for Python developers that significantly speeds up the process of creating Pydantic models from JSON data. It is designed for those who value precise data structure, ease of working with validation, and minimizing routine coding. Unlike existing solutions, our service offers an extended set of features and flexible settings, making it useful for both rapid prototyping and preparing production-ready code. The JSON to Pydantic online solution was created with the real needs of programmers in mind and meets standards of convenience, speed, and quality.

How to Convert JSON to Pydantic Model?

Our service is ideal for those who need to quickly and accurately convert JSON to Pydantic model. It combines automation, precision, and flexible settings, eliminating the need to write repetitive code manually.

Advantages of our service:

  1. Convert JSON to Pydantic (v1 and v2). The service supports automatic generation of models for both Pydantic versions, which is especially important for developers working with new projects (v2) as well as existing codebases (v1). Conversion is accompanied by extended formatting options: you can choose code style, indentation, and field declaration order.
  2. Convenient JSON input field. The built-in JSON editor supports drag&drop - simply drop a file, and the data will instantly appear in the workspace. For testing or a quick start, there is an “Example” button that inserts ready-to-use valid JSON code. This is useful when you need to test the service or quickly check formatting on sample data.
  3. Auto-update of Python code. Every change in JSON is instantly reflected in the generated Python code window. This allows you to see in real time how the data structure is transformed into a model and make immediate adjustments. This approach eliminates the need for multiple “Convert” clicks and saves time - especially when working with large, frequently changing JSON structures.
  4. Auto-generation without the “Convert” button. The code is generated automatically - no extra actions are needed. Just make changes to JSON, and the model updates immediately. This is particularly convenient for rapid prototyping and for those who value instant feedback.
  5. Advanced settings
    • Pydantic version selection - switch between v1 and v2 depending on your project requirements.
    • Use Optional - automatic generation of fields that allow None.
    • Field(default=...) - the ability to explicitly set default values.
    • snake_case or camelCase - choose variable naming style based on your team’s code standard.
    • Comments with examples - automatically generate explanatory comments in the code for better structure understanding.
      These options give the developer full control over the final model’s appearance and allow integrating the code without extra manual edits.
  6. Flexible export. The generated code can be saved as a .py file for later use in the project or copied to the clipboard with one click to paste into your IDE or code editor. This streamlines integration into your workflow and speeds up the transition from model generation to usage.

How to Use the JSON to Pydantic Model Generator?

Using the generator is simple - the process to convert JSON to Pydantic schema takes no more than 10 seconds, even for complex JSON structures.

Step-by-step guide:

  1. Upload JSON - drag and drop the file into the window or paste the text manually.
  2. Use the example if needed - click “Example” to automatically insert test data.
  3. Select Pydantic version - v1 or v2, depending on your project.
  4. Adjust formatting - enable/disable Optional, set naming style (snake_case or camelCase), add comments.
  5. Get the code - the Python model will appear automatically without button clicks.
  6. Save the result - download the .py file or copy the code to the clipboard.

Frequently Asked Questions

1. What is a Pydantic model?

A Pydantic model is a Python class that describes a data structure and validates it based on type annotations. It is used for strict control of input and output data in APIs, microservices, and other applications. The advantage of Pydantic is automatic type checking and value conversion, which reduces runtime errors. Models can be easily serialized to JSON or other formats, and also used for auto-generating documentation (for example, in FastAPI). This is especially useful when working with data from external sources, where the format may differ from what’s expected. Pydantic supports nested models, allowing you to describe even complex structures.

2. What is Pydantic used for?

Pydantic is used for data validation and serialization in Python projects. It is particularly useful in web development, when working with APIs, and in projects requiring strict typing. With Pydantic, you can quickly describe a data structure and ensure incoming data matches expectations. This greatly simplifies working with requests and responses in FastAPI, Flask, or other frameworks. Additionally, Pydantic simplifies JSON integration, makes it easy to describe application configurations, and reduces the amount of manual data validation code.

3. How does v1 differ from v2?

Pydantic v1 and v2 differ in architecture and performance. The second version features a rewritten engine in Rust (pydantic-core), which increases validation speed several times. v2 also changes the syntax of some functions, adds new types, and improves support for complex data structures. However, v1 is still widely used in existing projects, so it’s important to be able to generate code for both versions. Our generator supports both formats, making it a universal tool for different tasks.

4. How to describe nested models?

Nested models allow you to structure complex JSON data as separate classes. For example, if a user object contains an address field, this address should be placed in a separate Address model and then used as a type in the main User class. This approach improves code reuse, readability, and testing. In our service, nested JSON structures are automatically converted into nested Pydantic models, preserving correct types and nesting.

5. What is Optional in Pydantic?

Optional is a way to indicate that a field can have a value of None. In Pydantic, this is written as Optional[type], for example Optional[str]. This is important when data is not always complete, and you want to allow the absence of a specific field. Our service allows you to enable or disable Optional generation for fields where it is appropriate.

6. How to generate models from JSON Schema?

JSON Schema describes the structure of JSON data in specification format. If you have a JSON Schema, you can first generate a JSON example (using specialized tools) and then upload it to our service. The generator will automatically convert JSON to Pydantic model, taking into account types and nesting, saving time on manual code markup.

If you encounter errors, inaccuracies, or incorrect code generation when using the generator, email us at This email address is being protected from spambots. You need JavaScript enabled to view it.. We will carefully review your request, run a check, and provide a solution as soon as possible. Feedback helps us improve the tool and make it even more convenient and reliable for users.

JSON input

or drop a JSON file here
Drop JSON file to load…
Examples: User Product list Order (nested)

Generated Python (Pydantic)

Tip: adjust options above (version, snake_case, Optional/defaults) and the code updates live.