JSON to Pydantic Converter

Generate a Python Pydantic BaseModel class from a JSON sample — infers types, nested models, lists, and Optional fields.

Root model nameUse X | None instead of Optional[X]

JSON input

Drop a JSON file or click to browseLoads it into the input below

Pydantic model

from typing import Optional
from pydantic import BaseModel, Field


class Address(BaseModel):
    street: str
    city: str
    zip_code: str


class Order(BaseModel):
    order_id: int
    total: float
    shipped: bool


class RootModel(BaseModel):
    id_: int = Field(alias="id")
    name: str
    email: str
    is_active: bool
    balance: float
    signup_ip: None = None
    tags: list[str]
    address: Address
    orders: list[Order]

How JSON to Pydantic conversion works

Pydantic (v2) uses type-annotated classes that subclass BaseModel to validate and parse data — the same shape FastAPI request/response bodies use. This tool walks your JSON sample and infers a matching model: strings become str, whole numbers become int, decimals become float, booleans become bool, arrays become list[...], and nested objects become their own named BaseModel classes (capitalized from the parent key, singularized for list items — e.g. an "orders" array of objects produces an Order model).

Fields that are null in the sample, or that are missing from some array items, are wrapped in Optional[...] (or X | None if you enable the modern union syntax) with a default of None. Keys that aren't valid Python identifiers — reserved words, names starting with a digit, or non-alphanumeric characters — are renamed and mapped back to the original JSON key with Field(alias="..."), so Model.model_validate(json_data) still round-trips correctly. Everything runs locally in your browser; nothing is uploaded.