JSON to Code Model Generator
Generate Pydantic (Python), TypeScript interfaces, Java records, Go structs, and PHP classes from any JSON object instantly.
What is JSON to Code Model Generation?
JSON to code model generation takes a JSON object and automatically produces a typed data model or class in your target language. Instead of manually writing TypeScript interfaces, Pydantic models, Java records, Go structs, or PHP constructor classes by hand, you paste example JSON—such as an API response or config payload—and get a fully typed model in seconds. This eliminates transcription errors and ensures field names and types match exactly what the API returns.
How type inference works
The generator inspects each value in the JSON object. Booleans map to bool / boolean. Integers and floats get language-appropriate numeric types (int vs float64 in Go, int vs double in Java). Nested objects produce additional named types recursively, and arrays wrap the element type in the language's list syntax. Null values map to nullable or mixed types—review these manually as the intent depends on your API contract.
Frequently Asked Questions
What is code model generation and when should I use it?
Code model generation converts a data structure (like a JSON API response) into a language-specific typed model—an interface, class, or struct. Use it when integrating a new API endpoint, working with configuration schemas, or scaffolding data transfer objects. It saves you from writing boilerplate and reduces the chance of mismatched field names or wrong types.
What is the difference between Pydantic models and Python dataclasses?
Both provide typed Python data containers, but Pydantic adds runtime validation and coercion. A BaseModel will raise a ValidationError if you pass a string where an int is expected (unless it can coerce it). Python dataclasses are purely structural—no validation at all. Use Pydantic when working with untrusted data like API inputs; use dataclasses for internal data containers where you control all inputs.
Should I use a TypeScript interface or a type alias for a JSON model?
For plain data shapes from an API, either works. interface supports declaration merging (useful when extending third-party types) and is slightly faster for the TypeScript compiler on large projects. type is more flexible—it can express unions, intersections, and mapped types. The general community convention is to use interface for object shapes and type for everything else.
Why use a Java record instead of a regular class for a data model?
Records (introduced in Java 14, stable in Java 16) are immutable data carriers. The compiler auto-generates a constructor, equals(), hashCode(), and toString() from the declared components. For JSON deserialization, Jackson supports records natively since 2.12. They eliminate boilerplate compared to a traditional POJO and make the data-holding intent explicit.
What do the json struct tags in Go do?
Go struct field names are exported (capitalized) by convention, but JSON APIs often use lowercase or snake_case keys. The `json:"fieldName"` tag tells encoding/json which JSON key to map to that field during marshal and unmarshal. Without tags, Go uses the exact field name, causing mismatches with most REST APIs. The generator adds json tags automatically, preserving the original key from your JSON input.