What's the key difference between jsons and pydantic?

[Raven888888]

For serialization, one can export an instance of pydantic model to json

from datetime import datetime
from pydantic import BaseModel

class BarModel(BaseModel):
    whatever: int

class FooBarModel(BaseModel):
    foo: datetime
    bar: BarModel


m = FooBarModel(foo=datetime(2032, 6, 1, 12, 13, 14), bar={'whatever': 123})
print(m.json())
#> {"foo": "2032-06-01T12:13:14", "bar": {"whatever": 123}}

For deserialization, one can directly parse string into an instance of pydantic model

from datetime import datetime
from pydantic import BaseModel

class User(BaseModel):
    id: int
    name = 'John Doe'
    signup_ts: datetime = None

m = User.parse_raw('{"id": 123, "name": "James"}')
print(m)
#> id=123 signup_ts=None name='James'

So, why jsons over pydantic?

I can see that jsons being more lightweight for obvious reasons, but I wish to hear from the authors as well!

[JobaDiniz]

Does pydantic support nested classes?
Does it support Generics?