JSON Model Generator
Generate deterministic TypeScript, Dart, C#, Java, and Python models from JSON.
Invalid identifier characters are normalized.
2 MiB · 30 nesting levels0 characters
Array samples are merged. Missing fields become optional, explicit nulls remain nullable, and mixed types become an unknown type with a warning.
Generated code
Waiting for a JSON sample
// Generated models appear here.Working notes
Use JSON Model Generator with the boundary visible.
JSON Model Generator infers a deterministic object shape and emits starting-point types for TypeScript, Dart, C#, Java, or Python from representative JSON.
What is JSON Model Generator?
When integrating with a REST API or processing JSON data, the first step is defining types that match the response structure. Writing these types by hand from API documentation is tedious and error-prone, especially for deeply nested objects with dozens of fields. A JSON-to-model generator automates this: paste a sample JSON response, select your target language, and get a type-safe model definition. The generator examines every value to infer its type (string, number, boolean, null, nested object, or array) and produces idiomatic code: TypeScript interfaces, Python dataclasses with type hints, C# records, Java POJOs, or Dart classes. For nested objects, it creates separate named types and references them. For arrays, it inspects the elements to determine the element type. This is a starting point — a single sample cannot capture every possible shape (missing optional fields, union types, or polymorphic responses), so the generated code should always be reviewed against the actual API contract.
When to use it
- API integration — paste a sample API response to generate TypeScript interfaces or Python dataclasses, then refine optional fields and union types.
- Prototyping — quickly generate type definitions from JSON fixtures to get type safety during early development before the API contract is finalized.
- Code migration — convert a JSON schema to types in a new language when porting a service from one stack to another.
- Documentation — generate type definitions to include in technical documentation or README files alongside API examples.
- Testing — create type-safe test fixtures from JSON samples to catch deserialization issues in unit tests.
How to use it
- 01Paste a representative JSON object or array into the input editor.
- 02Choose the target language: TypeScript, Dart, C#, Java, or Python.
- 03Enter the root type name (e.g., UserResponse, OrderItem).
- 04Generate the model. Nested objects produce separate named types automatically.
- 05Review inferred optionality and types against your API documentation, then copy the code.
Common mistakes
- Trusting a single sample — one JSON response may omit optional fields, show null where a string is expected, or represent a union type as only one variant. Always cross-check with the API schema.
- Ignoring null values — a field with a null value could be nullable or simply absent in this sample. The generator may infer any? or object? where you need string? or number?.
- Wrong root type name — the generator uses your chosen name for the top-level type and derives nested type names from field names. A generic name like "Data" produces less readable code.
- Array element inference — if an array contains objects with different shapes (a polymorphic list), the generator merges them into a single type with optional fields. Use discriminated unions instead.
- Copying without review — generated types are a starting point. Add validation, default values, and documentation before using them in production code.
Synthetic example
Infer a service record
Input
{"name":"ledger","retries":3,"enabled":true}Result
interface Service {
name: string
retries: number
enabled: boolean
}Related standards
Limits and data boundary
- One sample cannot prove every production variant or nullable field.
- Generated code is a starting point and should be reviewed against the real API contract.
Frequently asked questions
- Which languages are supported?
- TypeScript interfaces, Dart classes, C# records, Java POJOs, and Python dataclasses. Each output follows the language's idiomatic naming conventions.
- Does it handle nested objects and arrays?
- Yes. Nested objects produce separate named types, and arrays are typed by the element shape inferred from the sample.
- Is my JSON sent to a server?
- No. Type inference and code generation run entirely in your browser. Your data stays local.
- How does it handle null values?
- A null value is inferred as a nullable type (e.g., string | null in TypeScript, Optional in Python). Review these carefully — the field may have a more specific type in other samples.
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