You can also use its __init__ method:

your_model = YourModel(**your_dict)

Updated @Alon's answer to handle nested modals:

from typing import Any, Type, Optional
from enum import Enum

from pydantic import BaseModel, Field, create_model


def json_schema_to_base_model(schema: dict[str, Any]) -> Type[BaseModel]:
    type_mapping: dict[str, type] = {
        "string": str,
        "integer": int,
        "number": float,
        "boolean": bool,
        "array": list,
        "object": dict,
    }

    properties = schema.get("properties", {})
    required_fields = schema.get("required", [])
    model_fields = {}

    def process_field(field_name: str, field_props: dict[str, Any]) -> tuple:
        """Recursively processes a field and returns its type and Field instance."""
        json_type = field_props.get("type", "string")
        enum_values = field_props.get("enum")

        # Handle Enums
        if enum_values:
            enum_name: str = f"{field_name.capitalize()}Enum"
            field_type = Enum(enum_name, {v: v for v in enum_values})
        # Handle Nested Objects
        elif json_type == "object" and "properties" in field_props:
            field_type = json_schema_to_base_model(
                field_props
            )  # Recursively create submodel
        # Handle Arrays with Nested Objects
        elif json_type == "array" and "items" in field_props:
            item_props = field_props["items"]
            if item_props.get("type") == "object":
                item_type: type[BaseModel] = json_schema_to_base_model(item_props)
            else:
                item_type: type = type_mapping.get(item_props.get("type"), Any)
            field_type = list[item_type]
        else:
            field_type = type_mapping.get(json_type, Any)

        # Handle default values and optionality
        default_value = field_props.get("default", ...)
        nullable = field_props.get("nullable", False)
        description = field_props.get("title", "")

        if nullable:
            field_type = Optional[field_type]

        if field_name not in required_fields:
            default_value = field_props.get("default", None)

        return field_type, Field(default_value, description=description)

    # Process each field
    for field_name, field_props in properties.items():
        model_fields[field_name] = process_field(field_name, field_props)

    return create_model(schema.get("title", "DynamicModel"), **model_fields)

Example Schema

schema = {
    "title": "User",
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"},
        "is_active": {"type": "boolean"},
        "address": {
            "type": "object",
            "properties": {
                "street": {"type": "string"},
                "city": {"type": "string"},
                "zipcode": {"type": "integer"},
            },
        },
        "roles": {
            "type": "array",
            "items": {
                "type": "string",
                "enum": ["admin", "user", "guest"]
            }
        }
    },
    "required": ["name", "age"]
}

Generate the Pydantic model

DynamicModel = json_schema_to_base_model(schema)

Example usage

print(DynamicModel.schema_json(indent=2))