Paste or import JSON
Use a single object, nested object, or collection of records. The parser validates size, nesting depth, and structure. JSON Lines mode merges up to 1,000 sample records.
DEVELOPER TOOLS / DATA MODELS
Paste JSON. Get clean, typed models for your favorite language—complete with nested structures, optional fields, and source-ready exports.
Loading model preview…Fields are inferred from the supplied JSON. For arrays of objects, missing keys become optional.
No server or API is contacted. Browser storage is optional; exports are saved to your device.
Java targets Jackson annotations, Python targets Pydantic v2. TypeScript, Dart, C#, and Go target their standard language toolchains.
HOW IT WORKS
Use a single object, nested object, or collection of records. The parser validates size, nesting depth, and structure. JSON Lines mode merges up to 1,000 sample records.
Switch between six generators, or merge JSON Lines samples to infer optional properties from multiple responses.
Download the active file, original JSON, or a ZIP containing every language model, JSON Schema and generation notes.
Choose JSON Lines mode and paste one JSON object per line, up to 1,000 records. Missing properties become optional in generated models. Arrays in normal JSON mode are also inferred across representative elements.
Download a Draft 2020-12 schema representing the inferred types, nullable options, and required properties. The schema is an approximation from samples, not a replacement for a formally maintained API contract.
No. Parsing and generation happen inside your browser. Turn off “Remember in this browser” to avoid local autosave.
No. Types are inferred from your sample. Arrays with varying object keys produce optional fields. For complete contracts, review against your API schema and test with real data.
The Python output uses Pydantic v2 and Java models use Jackson annotations. The others target standard language features. Generated code is a starting point and should be reviewed before production use.
Input JSON is capped at 2 MB, nesting at 24 levels, and object fields at 160 per object. Long arrays are sampled across their length for inference, and this is disclosed in the generation notes.
Infer typed models from representative JSON or JSON Lines, review generated types and sample-derived schema, then export source files or a ZIP.
With two JSONL records where one has an optional nickname, inspect the generated field and schema, then export TypeScript and validate it in a separate project.
Inference reflects only samples, not an API contract; unseen variants may be missed. Input caps at 2 MB/1000 JSONL records and large arrays sample at most 2000 items. Java targets Jackson and Python targets Pydantic v2. The tool does not compile output. Review generated files before use.
No. Validate against the full contract and representative payloads.
No. Compile the export in the intended project with its dependencies.