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.

Names, aliases and annotation mapping

Field names are sanitised for Python: invalid characters become underscores, a leading digit gets an underscore prefix, and a name colliding with one of thirty-seven Python keywords gains a trailing underscore, so `class` becomes `class_` and `match` becomes `match_`. Whenever the sanitised name differs from the JSON key, the field is annotated `Field(alias="original")` so parsing still matches the wire format exactly.

Types map as expected: integers to `int`, fractional numbers to `float`, strings to `str`, booleans to `bool`. A `null` value produces `Optional[...]` with a `= None` default, which makes the field genuinely optional rather than required-but-nullable — a distinction that matters if a missing key should be an error rather than a silent `None`.

How arrays and nested models are emitted

Nested objects of any depth each become their own `BaseModel` class, declared above the model that references them, and the root becomes `RootModel`. Arrays are annotated `List[...]`, with heterogeneous element types collapsed into a `Union`, and an array of objects emits its own element class — `items: List[ItemsModel]` — rather than referencing a name the file never defines. Class names come from the key, so two keys that differ only by capitalisation get a numeric suffix instead of colliding. An empty array becomes `List[Any]`, since an empty sample carries no element type at all — fill it in yourself rather than leaving it unvalidated.

Aliases and optionality interact in one way worth knowing: a field whose JSON key needed sanitising is emitted as `Field(alias="original")`, and a `null` value is emitted as `Optional[Any] = None`. When both apply the default is folded into the `Field` call as `Field(None, alias="...")`, so the field stays optional instead of becoming required. A key starting with a digit is prefixed with `field_`, because Pydantic rejects field names with a leading underscore.

Before you rely on it

No constraints are inferred. There are no `Field` bounds, no `Literal` enums, no `default_factory` for list fields, and no discriminated unions — a field that is a string in one record and an object in another is typed from whichever the sample happened to show, with no signal that the other case exists.

The generated header imports `BaseModel`, `Field` and the typing names, so the file is self-contained and works unchanged under Pydantic v2 with `model_validate`. FastAPI accepts these models directly as response models. Add your own validators for anything a single sample cannot express, and prefer an explicit JSON Schema when the contract needs to be enforceable rather than merely descriptive.

Frequently Asked Questions

How do I convert JSON to a Pydantic model?

Paste a JSON sample into the input and click Generate Model. The tool analyses the data types and produces Python Pydantic v2 BaseModel classes with proper type annotations. Optional fields are inferred from null values.

What Pydantic version does it generate?

It generates Pydantic v2 compatible code using the BaseModel class, model_validator, and Field syntax. It also produces FastAPI-compatible response models with from_dict and to_dict helper methods.

Is my data sent to a server?

No. Every tool on jsonversal runs entirely in your browser using the Web Crypto API, File API, and Clipboard API. Nothing is ever uploaded — not even the JSON you paste in.

Do I need an account or API key?

No signup, no email, no API key. All tools are free to use with no signup required.

Can I use these tools offline?

Yes. Once the page loads, all tools work offline. There are no external API calls or CDN dependencies at runtime.

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Your data never leaves your device

All processing happens in your browser. No uploads, no tracking, no servers. Your API keys, configs, and private payloads stay private — 100% client-side.