dataclass vs pydantic: python version performance

Overview

python 3.13 introduced significant performance improvements for dataclasses. this benchmark tracks performance evolution from python 3.10 through 3.14.

Methodology

three operations tested with 10,000 iterations each:

• creation: object instantiation
• modification: attribute updates
• serialization: conversion to dictionary

Test Implementation

# Example test structure
@dataclass
class PersonDataclass:
    name: str
    age: int
    email: str
    active: bool = True

class PersonPydantic(BaseModel):
    name: str
    age: int
    email: str
    active: bool = True

timing via time.perf_counter(). complete benchmark: benchmark.py

Results

Summary

• python 3.13 improved dataclass creation by 3x and serialization by 4x
• pydantic performance remained consistent across versions
• pydantic’s serialization advantage decreased from 6x to 1.3x

Performance Data

lower times indicate better performance. relative = pydantic / dataclass

Scripts

• benchmark.py - benchmark runner
• analyze.py - data analysis
• visualize_versions.py - visualization
• results_python_*.json - raw results per version

Recommendations

• on python 3.13+, performance differences are minimal - choose based on features
• for serialization-heavy workloads, pydantic maintains an advantage
• for simple data containers, dataclasses offer excellent performance with no dependencies

Notes

• python 3.14 requires pydantic 2.12.0a1 or later (pre-release)
• all benchmarks run on identical hardware
• measurements include all overhead (validation, type checking)

See Also

• full benchmark comparison
• choosing a data model
• dataclasses documentation
• pydantic documentation
• python setup 2025