JSON to Pydantic Model

Generate Pydantic v2 BaseModel classes from a JSON sample. Get type-safe Python models for API responses, configs, and LLM structured output.

Input JSON

Pydantic v2 Model

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Requires pydantic — install with pip install pydantic

Error

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How it works

Paste a JSON sample representing your expected data shape.
Click Generate Pydantic Model — nested objects become their own BaseModel classes, arrays become List[...], and nullable values become Optional[...].
Copy the generated classes into your Python project and use them for validation, serialization, and type hints.

Usage example

from models import UserModel
import requests

resp = requests.get("https://api.example.com/user/1")
user = UserModel.model_validate(resp.json())
print(user.name, user.address.city)

About JSON to Pydantic Model

Pydantic is the standard data-validation library for Python, using type hints to validate and serialize data at runtime. Generating BaseModel classes from a JSON sample saves you from hand-writing the boilerplate for API responses, configuration objects, and LLM structured output, while keeping your code type-checkable.

Use it when you receive untyped JSON — from a REST API, a database row, or an LLM call — and want runtime guarantees in Python. As with any inferred model, the output is only as accurate as the sample: add optional fields, unions, and constraints by hand after generation, and remember that an empty array becomes List[Any] because the sample carries no element type.