JSON to Pydantic Model
Generate Python Pydantic models from sample JSON instantly.
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How to Generate Pydantic Models from JSON
Paste a sample JSON payload and the tool generates Pydantic BaseModel classes with type hints, snake_case fields, aliases for camelCase keys, and nested models - ready for Model.model_validate_json() in your FastAPI or data pipeline code.
Why Pydantic?
Pydantic validates data at runtime and powers FastAPI request/response models. Generating models from a real API sample saves the tedium of transcribing field names and types by hand.
Common Use Cases
Python developers generate Pydantic models when building FastAPI endpoints, validating third-party API responses, parsing configuration, and adding type safety to ETL pipelines.
Frequently Asked Questions
Which Pydantic version does the output target?
The generated code uses BaseModel with modern built-in generics (list[int]) and Field aliases, compatible with Pydantic v2 (and v1 for most models).
How are camelCase keys handled?
Field names are converted to snake_case per Python convention, with Field(alias="originalKey") preserving the JSON key for parsing.
How are nested objects handled?
Each nested object becomes its own BaseModel class, declared before the parent so the generated module imports cleanly.
What about null values?
Fields that are null in the sample are typed as Any since one sample can't reveal the real type - tighten them to Optional[str] or similar once you know the shape.