Pydantic False positive validation error on nested generic

The Fix

pip install pydantic==1.10.19

Based on closed pydantic/pydantic issue #10359 · PR/commit linked

Jump to Verify Open PR/Commit

@@ -772,12 +772,6 @@ Order(id=1, product=ResponseModel[Product](content=Product(name='Apple', price=0
 ```
 
-!!! tip
-    When using a parametrized generic model as a type in another model (like `product: ResponseModel[Product]`),
-    make sure to parametrize said generic model when you initialize the model instance

repro.py

from typing import Generic, TypeVar
from pydantic import BaseModel

# Library side:

M = TypeVar("M", bound=BaseModel)

class InnerModel(BaseModel, Generic[M]):
    model: M

I = TypeVar("I", bound=InnerModel)

class OuterModel(BaseModel, Generic[I]):
    inner: I

# User side:

class MyModel(BaseModel):
    foo: int

# Construct two instances, with and without generic annotation in the constructor:
inner1 = InnerModel[MyModel](model=MyModel(foo=42))
inner2 = InnerModel(model=MyModel(foo=42))

# Everything suggests the two are exactly the same, even the `__eq__` assertion passes:
print(inner1)  # -> model=MyModel(foo=42)
print(inner2)  # -> model=MyModel(foo=42)
assert inner1 == inner2

# However, one of them fails to validate within the outer model!
outer1 = OuterModel[InnerModel[MyModel]](inner=inner1)  # works
outer2 = OuterModel[InnerModel[MyModel]](inner=inner2)  # fails!

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==1.10.19\nWhen NOT to use: Avoid using unparametrized generics in model initialization to prevent validation issues.\n\n

Why This Fix Works in Production

Trigger: In [1]: from typing import Any, TypeVar, Generic
Mechanism: False positive validation errors occur due to unparametrized generic models in nested structures
Why the fix works: Revalidates parametrized generics if an instance's origin is a subclass of the original class, addressing the issue of false positive validation errors in nested generic BaseModels. (first fixed release: 1.10.19).

Production impact:

If left unfixed, this can cause silent data inconsistencies that propagate (bad cache entries, incorrect downstream decisions).

Why This Breaks in Prod

Shows up under Python 3.10 in real deployments (not just unit tests).
False positive validation errors occur due to unparametrized generic models in nested structures
Surfaces as: In [1]: from typing import Any, TypeVar, Generic

Proof / Evidence

GitHub issue: #10359
Fix PR: https://github.com/pydantic/pydantic/pull/10666
First fixed release: 1.10.19
Reproduced locally: No (not executed)
Last verified: 2026-02-09
Confidence: 0.85
Did this fix it?: Yes (upstream fix exists)
Own content ratio: 0.36

Discussion

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

Jump to Sources Open on GitHub

“> We might consider changing the current behavior to match static type checkers semantics, @sydney-runkle wdyt? Yes, I think this is reasonable”

@sydney-runkle · 2024-09-10 · source

“I believe this is currently expected”

@Viicos · 2024-09-09 · source

“> Pydantic will default to Any, meaning Model(a=1) is equivalent to ModelAny Just to clarify: In the original example InnerModel(...) is actually not the same…”

@bluenote10 · 2024-09-09 · source

Failure Signature (Search String)

In [1]: from typing import Any, TypeVar, Generic

Error Message

Stack trace

error.txt

Error Message
-------------
In [1]: from typing import Any, TypeVar, Generic

In [2]: from pydantic import BaseModel

In [3]: T = TypeVar("T", bound=int)

In [4]: class Model(BaseModel, Generic[T]):
   ...:     x: T
   ...: 

In [5]: Model[Any](x="not_an_int")
Out[5]: Model[Any](x='not_an_int')

In [6]: Model(x="not_an_int")
---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
Cell In[6], line 1
----> 1 Model(x="not_an_int")

File ~/git/immoscrape/venv/lib/python3.10/site-packages/pydantic/main.py:193, in BaseModel.__init__(self, **data)
    191 # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    192 __tracebackhide__ = True
--> 193 self.__pydantic_validator__.validate_python(data, self_instance=self)

ValidationError: 1 validation error for Model
x
  Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='not_an_int', input_type=str]
    For further information visit https://errors.pydantic.dev/2.8/v/int_parsing

Minimal Reproduction

repro.py

from typing import Generic, TypeVar
from pydantic import BaseModel

# Library side:

M = TypeVar("M", bound=BaseModel)

class InnerModel(BaseModel, Generic[M]):
    model: M

I = TypeVar("I", bound=InnerModel)

class OuterModel(BaseModel, Generic[I]):
    inner: I

# User side:

class MyModel(BaseModel):
    foo: int

# Construct two instances, with and without generic annotation in the constructor:
inner1 = InnerModel[MyModel](model=MyModel(foo=42))
inner2 = InnerModel(model=MyModel(foo=42))

# Everything suggests the two are exactly the same, even the `__eq__` assertion passes:
print(inner1)  # -> model=MyModel(foo=42)
print(inner2)  # -> model=MyModel(foo=42)
assert inner1 == inner2

# However, one of them fails to validate within the outer model!
outer1 = OuterModel[InnerModel[MyModel]](inner=inner1)  # works
outer2 = OuterModel[InnerModel[MyModel]](inner=inner2)  # fails!

Environment

Python: 3.10
Pydantic: 2

What Broke

Users experience runtime crashes due to unexpected ValidationErrors in nested generic BaseModels.

Why It Broke

False positive validation errors occur due to unparametrized generic models in nested structures

Fix Options (Details)

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

pip install pydantic==1.10.19

When NOT to use: Avoid using unparametrized generics in model initialization to prevent validation issues.

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/10666

First fixed release: 1.10.19

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

When NOT to Use This Fix

Avoid using unparametrized generics in model initialization to prevent validation issues.

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
1.10.19	Fixed

Related Issues

No related fixes found.

Cluster: pydantic:data-consistency Pydantic hub Pydantic best practices All hubs All clusters

Related clusters: Configuration error OpenAPI schema Serialization

Sources

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

Pydantic False positive validation error on nested generic `BaseModel` (Fix)