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Using concrete types instead of abstract container classes in data models improves validation performance. More specific types require fewer checks and coercion attempts, resulting in faster validation. Additionally, ensure proper model parametrization to prevent unnecessary revalidation, which can trigger validators repeatedly.

For example, prefer:

from pydantic import BaseModel

class Model(BaseModel):
    items: list[str]  # Concrete type

Instead of:

from collections.abc import Sequence
from pydantic import BaseModel

class Model(BaseModel):
    items: Sequence[str]  # Abstract type - incurs more validation overhead

This optimization is particularly important in performance-sensitive code paths where validation occurs frequently.