How do I turn a JSON object into a Pydantic model?

Paste the JSON into the input box and the tool generates a BaseModel on the spot, no build step and no install. Each top-level object becomes one class. For example, {"id": 1, "name": "Ada"} produces a class Root(BaseModel) with id: int and name: str. Set the root model name to something meaningful like GithubUser, pick Pydantic v2, and copy the result into your schemas.py. Then GithubUser.model_validate( resp.json()) gives you a typed object with editor autocomplete instead of a raw dict you index by string and hope for the best.

How are nested objects turned into sub-models?

Every nested object lifts out into its own child BaseModel named after the key in PascalCase, and the child is written before the parent so there are no forward references. Given {"id": 1, "address": {"city": "SF", "zip": "94105"}} you get a class Address(BaseModel) with city and zip, then class Root(BaseModel) whose address field is typed address: Address. Nesting goes as deep as your JSON does, so a tls block inside a service block inside the root each become their own class.

When does a field become Optional?

Two signals make a field Optional. First, the value appears as null in your JSON, so {"company": null} yields company: Optional[Any] = None. Second, the key is present in some elements of an object array but missing in others. If you paste [{"id": "a"}, {"id": "b", "vip": true}] the folded model marks vip as Optional[bool] = None because the first element lacked it. A single object has no missing-key signal, so every field is required. Wrap one sample as [ ... ] to explore optionality.

What is the difference between Pydantic v1 and v2 here?

The field annotations are almost identical between the two versions. The difference shows up only when a key needs an alias. In v2 the tool emits model_config = ConfigDict(populate_by_name=True) so you can construct the model by either the field name or the wire key. In v1 it emits the older nested class Config with allow_population_by_field_name = True instead. Pick the version that matches the pydantic in your requirements file and the import line and config block adjust to match.

Should I use Optional[X] or X | None?

Both mean the same thing to Pydantic, so it comes down to your Python version and house style. Optional[X] works everywhere and reads clearly, which is why it is the default and why the typing import is added for you. X | None is the modern PEP 604 syntax that runs on Python 3.10 and up and needs no import. Flip the Optional style dropdown and every nullable field rewrites, with the typing import dropping out when nothing else needs it.

Is my JSON uploaded anywhere?

No. Parsing, type inference and rendering all happen inside this browser tab using the built-in JSON.parse. Open DevTools, watch the Network panel, and you will see zero requests fire as you paste. The Share link encodes your JSON into the URL so a result is reproducible, which means you control what gets shared. If the payload is sensitive, copy the generated Python instead of sharing the link. Only your option choices, the version and the Optional style, live in localStorage.

Type a third-party API response without hand-writing models

You are integrating a REST API and the user object has 30 plus fields. Curl the endpoint once, paste the JSON, set the root name to GithubUser, pick Pydantic v2, and copy the output into app/clients/github.py. The nested plan object lifts out as Plan automatically, html_url keeps its alias, and now GithubUser.model_validate(resp.json()) gives you a typed object with autocomplete instead of a raw dict you index by string and pray over.

Generate request and response models for a FastAPI endpoint

A teammate sends a sample JSON body for the /orders POST they are building. Paste it, name the root CreateOrderRequest, choose Pydantic v2 so FastAPI validates the body automatically. Paste the response sample too as CreateOrderResponse. Drop both into schemas.py, wire them into the route signature, and the OpenAPI docs plus 422 validation come for free before either of you writes the handler.

Decide Optional vs required for a nullable webhook payload

A vendor webhook sometimes includes cancelled_at and sometimes omits it. Log the first few payloads, paste them as a JSON array, and the Optional detection flags cancelled_at as Optional[str] = None. In your handler, checking cancelled_at is not None cleanly distinguishes a cancelled event from an active one, and Pydantic raises if a genuinely required field is missing instead of letting a KeyError surface three calls deep.

Replace a dict[str, Any] blob with a real model

Old code does data = resp.json() and reaches in with data["user"]["profile"]["id"], exploding at runtime on a renamed key. Capture one representative response, paste it, generate the model, replace the dict. The string indexing that bit you turns into attribute access like data.user.profile.id, with mypy or Pyright catching typos at edit time instead of in production.

JSON to Pydantic BaseModel Generator, Nested Models, Optional, v1 / v2

Paste JSON, get Pydantic BaseModel classes with type hints, nested sub-models, List[T], Optional inference, v1 / v2 and aliases.

Runs locally
Category Format Converter
Best for Turning pasted content or local files into a handoff-friendly format.

Root model namePydantic versionOptional style

Examples:

JSON input

Pydantic output

How types, Optional & nested models are inferred

Each JSON object becomes a Pydantic BaseModel. Integer numbers infer int; fractional numbers infer float; a number that is sometimes whole and sometimes fractional widens to float. Nested objects become their own child BaseModel named after the key in PascalCase, emitted before the parent so the file has no forward references. Arrays of objects fold into ONE model — keys missing from some elements (or seen as null) become Optional. A value that is only ever null types as Optional[Any]. Keys that are not valid Python identifiers are converted to snake_case and the original key is preserved with Field(alias=...), with populate_by_name turned on so the model still accepts the field name. Switch between Optional[X] and X | None, and between v1 and v2 config blocks.

What this tool does

Paste any JSON payload, a REST API response, a webhook body, a config.json or a test fixture, and get back a set of fully type-annotated Pydantic models you can drop straight into a .py file. Types are inferred, not flattened: an integer sample becomes int, a fractional one becomes float, and a field that is whole in one sample and fractional in another widens to float so your model never rejects a valid row. true and false become bool, strings become str, and a value that only ever appears as null becomes Optional[Any]. Nested objects lift out as their own child BaseModel named after the key in PascalCase and are emitted before the parent, so the file reads top to bottom with no forward references. Arrays of objects fold into one shared model, so a list whose second element carries an extra key gives you one model with that key flagged Optional, not two near-duplicate classes you reconcile by hand. Keys that are not valid Python identifiers are cleaned to snake_case and the original wire key is kept with Field(alias=...). Switch between Optional[X] and X | None, between Pydantic v1 and v2, rename the root model, copy or share. Everything runs in your browser and the JSON never touches a server.

Tool details

Input: Text + Numbers + Structured content; The page exposes text boxes, numeric controls, file pickers, or structured inputs depending on the tool.
Output: Live result + Copy + Download; The result area focuses on usable output, with copy, download, or preview actions when supported.
Privacy: Browser-side processing; The main tool logic does not call an external API, so inputs normally stay in the current tab.
Save / share: Shareable URL state; Key settings are encoded in the URL so another person can reopen the same setup.
Performance budget: Initial JS <= 9 KB; No WASM budget is declared, keeping the tool quick to open on mobile.
Best fit: Format Converter · Developer; Category and role tags drive related tools, internal links, and quick fit checks.

How to use

1. Input

Paste or drop your content into the tool panel.
2. Process

Click the button. All processing is local in your browser.
3. Copy / Download

Copy the result or download to disk in one click.

How JSON to Pydantic fits into your work

Use it when the main problem is getting content from one practical format into another.

Conversion jobs

Turning pasted content or local files into a handoff-friendly format.
Previewing a conversion before you use it in a larger workflow.
Cleaning small format mismatches without opening a full editor.

Conversion checks

Try a small sample first when the source format is messy.
Check character encoding, separators, and line endings after conversion.
Keep the source until the converted output has been reviewed.

Good next steps

These links move the current task into a more complete workflow.

Real-world use cases

Type a third-party API response without hand-writing models
You are integrating a REST API and the user object has 30 plus fields. Curl the endpoint once, paste the JSON, set the root name to GithubUser, pick Pydantic v2, and copy the output into app/clients/github.py. The nested plan object lifts out as Plan automatically, html_url keeps its alias, and now GithubUser.model_validate(resp.json()) gives you a typed object with autocomplete instead of a raw dict you index by string and pray over.
Generate request and response models for a FastAPI endpoint
A teammate sends a sample JSON body for the /orders POST they are building. Paste it, name the root CreateOrderRequest, choose Pydantic v2 so FastAPI validates the body automatically. Paste the response sample too as CreateOrderResponse. Drop both into schemas.py, wire them into the route signature, and the OpenAPI docs plus 422 validation come for free before either of you writes the handler.
Decide Optional vs required for a nullable webhook payload
A vendor webhook sometimes includes cancelled_at and sometimes omits it. Log the first few payloads, paste them as a JSON array, and the Optional detection flags cancelled_at as Optional[str] = None. In your handler, checking cancelled_at is not None cleanly distinguishes a cancelled event from an active one, and Pydantic raises if a genuinely required field is missing instead of letting a KeyError surface three calls deep.
Replace a dict[str, Any] blob with a real model
Old code does data = resp.json() and reaches in with data["user"]["profile"]["id"], exploding at runtime on a renamed key. Capture one representative response, paste it, generate the model, replace the dict. The string indexing that bit you turns into attribute access like data.user.profile.id, with mypy or Pyright catching typos at edit time instead of in production.

Common pitfalls

Pasting a single object and expecting some fields to be Optional. A single object has no missing-key signal, so every field is required with no = None. Optional inference only kicks in across an array of objects where some elements carry a key and others do not, or when a value is literally null. Wrap one sample as [ ... ] to explore optionality.
Choosing the wrong Pydantic version for your alias config. In v2 the tool writes model_config = ConfigDict(populate_by_name=True); in v1 it writes the nested class Config with allow_population_by_field_name = True. If you copy v2 output into a project pinned to pydantic 1.x, the ConfigDict import fails. Match the version dropdown to the pydantic in your requirements.
Manually narrowing a float field back to int when the JSON sometimes has decimals. If one sample is 1 and another is 1.5, the field correctly widens to float. Narrowing it to int makes Pydantic reject the 1.5 row. Trust the widening, since JSON 1 is a valid float in Python anyway.
Pasting JSON5, JSON with comments, or trailing commas. The tool uses strict JSON.parse, so comments, unquoted keys, and trailing commas all error out with a line and column. Strip them first, or run the payload through a JSON formatter as a pre-step.

Privacy

Your JSON never leaves this browser tab. Parsing and type inference use the browser built-in JSON.parse; there is no network call and no analytics on the textarea content. The Share link encodes your input and the root model name into the URL so a result is reproducible, which means you decide when to share. If the payload is sensitive, internal IDs, customer data or production responses, copy the generated Python instead of sharing the link. Only your option choices, the Pydantic version and the Optional style, are stored in localStorage so your preferred style persists across visits.

FAQ

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JSON to Pydantic BaseModel Generator, Nested Models, Optional, v1 / v2

What this tool does

Tool details

How to use

1. Input

2. Process

3. Copy / Download

How JSON to Pydantic fits into your work

Conversion jobs

Conversion checks

Good next steps

Real-world use cases

Type a third-party API response without hand-writing models

Generate request and response models for a FastAPI endpoint

Decide Optional vs required for a nullable webhook payload

Replace a dict[str, Any] blob with a real model

Common pitfalls

Privacy

FAQ

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