Pydantic Slow setting an value of a basic Model (Fix)

The Fix

pip install pydantic==2.10.0

Based on closed pydantic/pydantic issue #10853 · PR/commit linked

Production note: Most teams hit this during upgrades or environment changes. Roll out with a canary and smoke critical endpoints (health, OpenAPI/docs) before 100%.

Jump to Verify Open PR/Commit

@@ -152,6 +152,8 @@ def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None:
             )
 
+            cls.__pydantic_setattr_handlers__ = {}
+
             cls.__pydantic_decorators__ = DecoratorInfos.build(cls)

repro.py

import timeit
from pydantic import BaseModel


class MyObj(BaseModel):
    name: str
    value: int | None


if __name__ == "__main__":
    my_obj = MyObj(name="a", value=1)

    def set_obj(obj):
        obj.value = 10
        return obj

    def instantiation():
        return MyObj(name="a", value=1)

    print(timeit.timeit("set_obj(my_obj)", globals=globals(), number=1000000))
    print(timeit.timeit("instantiation()", globals=globals(), number=1000000))

verify

Re-run the minimal reproduction on your broken version, then apply the fix and re-run.

fix.md

Option A — Upgrade to fixed release\npip install pydantic==2.10.0\nWhen NOT to use: This fix is not suitable if extensive validation checks are required for every attribute assignment.\n\n

Why This Fix Works in Production

Trigger: Attribute assignment in models is significantly slower than instantiation, impacting performance.
Mechanism: The __setattr__ method performs extensive checks, causing slow attribute assignment in Pydantic models
Why the fix works: Improves the performance of `__setattr__` in Pydantic models by caching setter functions, making attribute assignment significantly faster. (first fixed release: 2.10.0).

Production impact:

If left unfixed, the same config can fail only in production (env differences), causing startup failures or partial feature outages.

Why This Breaks in Prod

Shows up under Python 3.12.7 in real deployments (not just unit tests).
The __setattr__ method performs extensive checks, causing slow attribute assignment in Pydantic models
Production symptom (often without a traceback): Attribute assignment in models is significantly slower than instantiation, impacting performance.

Proof / Evidence

GitHub issue: #10853
Fix PR: https://github.com/pydantic/pydantic/pull/10868
First fixed release: 2.10.0
Reproduced locally: No (not executed)
Last verified: 2026-02-09
Confidence: 0.85
Did this fix it?: Yes (upstream fix exists)
Own content ratio: 0.63

Discussion

High-signal excerpts from the issue thread (symptoms, repros, edge-cases).

Jump to Sources Open on GitHub

“Fixing this would be relatively straight forward https://github.com/pydantic/pydantic/pull/10868”

@MarkusSintonen · 2024-11-18 · source

“Yes, I have also seen this”

@MarkusSintonen · 2024-11-17 · source

Failure Signature (Search String)

Attribute assignment in models is significantly slower than instantiation, impacting performance.

Copy-friendly signature

signature.txt

Failure Signature
-----------------
Attribute assignment in models is significantly slower than instantiation, impacting performance.

Error Message

Signature-only (no traceback captured)

error.txt

Error Message
-------------
Attribute assignment in models is significantly slower than instantiation, impacting performance.

Minimal Reproduction

repro.py

import timeit
from pydantic import BaseModel


class MyObj(BaseModel):
    name: str
    value: int | None


if __name__ == "__main__":
    my_obj = MyObj(name="a", value=1)

    def set_obj(obj):
        obj.value = 10
        return obj

    def instantiation():
        return MyObj(name="a", value=1)

    print(timeit.timeit("set_obj(my_obj)", globals=globals(), number=1000000))
    print(timeit.timeit("instantiation()", globals=globals(), number=1000000))

Environment

Python: 3.12.7
Pydantic: 2

What Broke

Attribute assignment in models is significantly slower than instantiation, impacting performance.

Why It Broke

The __setattr__ method performs extensive checks, causing slow attribute assignment in Pydantic models

Fix Options (Details)

Option A — Upgrade to fixed release Safe default (recommended)

pip install pydantic==2.10.0

When NOT to use: This fix is not suitable if extensive validation checks are required for every attribute assignment.

Use when you can deploy the upstream fix. It is usually lower-risk than long-lived workarounds.

Fix reference: https://github.com/pydantic/pydantic/pull/10868

First fixed release: 2.10.0

Last verified: 2026-02-09. Validate in your environment.

When NOT to Use This Fix

This fix is not suitable if extensive validation checks are required for every attribute assignment.

Verify Fix

verify

Re-run the minimal reproduction on your broken version, then apply the fix and re-run.

Did This Fix Work in Your Case?

Quick signal helps us prioritize which fixes to verify and improve.

Prevention

Add a CI check that diffs key outputs after upgrades (OpenAPI schema snapshots, JSON payload shapes, CLI output).
Upgrade behind a canary and run integration tests against the canary before 100% rollout.

Version Compatibility Table

Version	Status
2.10.0	Fixed

Related Issues

No related fixes found.

Cluster: pydantic:configuration-error Pydantic hub Pydantic best practices All hubs All clusters

Related clusters: Data consistency OpenAPI schema Serialization

Sources

We don’t republish the full GitHub discussion text. Use the links above for context.