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JSON to Pydantic

Turn JSON into Pydantic v2 models with automatic validation. Nested models, lists, and optional fields are inferred from your sample payload.

JSON input

Paste JSON, optionally name the root type, then generate.

Your Pydantic model code will appear here.
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Validation built into the model

Unlike plain dataclasses, Pydantic models coerce and validate on construction: a numeric string becomes an int, a bad date raises a clear error. Generating the model from a real payload gives you that safety net with zero hand written boilerplate.

Perfect for FastAPI

FastAPI uses Pydantic models for request bodies, responses, and auto generated docs. Paste a sample response from your API, generate the model, and your endpoints get validation plus OpenAPI documentation for free.

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JSON to Pydantic FAQs

What is Pydantic used for?
Pydantic validates and parses data in Python, and it powers FastAPI request handling. A model class both documents the shape and rejects bad input at runtime.
Is this Pydantic v1 or v2 syntax?
The output uses Pydantic v2 style BaseModel classes, which work with the current Pydantic releases and FastAPI.
How are optional fields handled?
Fields that are null or missing in some records become Optional[X] with a default of None, so partial payloads validate instead of raising errors.
Can nested objects be validated too?
Yes. Each nested object becomes its own BaseModel subclass, and validation recurses automatically, so a bad value three levels deep is caught with a clear error path.
What about extra fields in the input?
By default Pydantic v2 ignores extra fields. Add model_config = ConfigDict(extra='forbid') if you want strict rejection of unexpected keys.
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