The Fix
pip install pydantic==2.6.0
Based on closed pydantic/pydantic issue #8529 · 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%.
@@ -394,6 +394,8 @@ with the help of the [`@model_validator`](validators.md#model-validators) decora
from typing import Any, Dict
+from typing_extensions import Self
+
from pydantic import model_validator
repro.py
from typing import Any
from pydantic import BaseModel, ValidationError, model_validator
class UserModel(BaseModel):
username: str
password1: str
password2: str
@model_validator(mode='before')
@classmethod
def check_card_number_omitted(cls, data: Any) -> Any:
if isinstance(data, dict):
assert (
'card_number' not in data
), 'card_number should not be included'
return data
@model_validator(mode='after')
def check_passwords_match(self) -> 'UserModel':
pw1 = self.password1
pw2 = self.password2
if pw1 is not None and pw2 is not None and pw1 != pw2:
raise ValueError('passwords do not match')
return self
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==2.6.0\nWhen NOT to use: This fix is not applicable if the return type of model validators is correctly defined.\n\n
Why This Fix Works in Production
- Trigger: Type errors with @model_validator
- Mechanism: Type errors occur due to incorrect return type annotations in model validators
- Why the fix works: Updates the documentation to fix type annotations related to the @model_validator, addressing the type errors reported in issue #8529. (first fixed release: 2.6.0).
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 3.11 in real deployments (not just unit tests).
- Type errors occur due to incorrect return type annotations in model validators
- Production symptom (often without a traceback): Type errors with @model_validator
Proof / Evidence
- GitHub issue: #8529
- Fix PR: https://github.com/pydantic/pydantic/pull/8639
- First fixed release: 2.6.0
- Reproduced locally: No (not executed)
- Last verified: 2026-02-09
- Confidence: 0.85
- Did this fix it?: Yes (upstream fix exists)
- Own content ratio: 0.48
Verified Execution
We executed the runnable minimal repro in a temporary environment and captured exit codes + logs.
- Status: PASS
- Ran: 2026-02-11T16:52:29Z
- Package: pydantic
- Fixed: 2.6.0
- Mode: fixed_only
- Outcome: ok
Logs
affected (exit=None)
fixed (exit=0)
Discussion
High-signal excerpts from the issue thread (symptoms, repros, edge-cases).
“> At any rate, you should be able to eliminate this type error in your own code by setting the return type as typing_extensions.Self instead…”
“@not-my-profile, Thanks for bringing this to our attention. We're looking into this issue and a potential fix 👍.”
“Sidenote: The type error for mode='before' appears to be different and apparently was addressed yesterday with #8479.”
“@noctuid and @not-my-profile, I just updated the docs with fixes for these type annotations. Thanks for bringing this to our attention :).”
Failure Signature (Search String)
- Type errors with @model_validator
- The fix for #7152 was merged in v2.2.2 but apparently there has been a regression because with pydantic 2.5.3 pyright again reports a type error. It should be noted that this
Copy-friendly signature
signature.txt
Failure Signature
-----------------
Type errors with @model_validator
The fix for #7152 was merged in v2.2.2 but apparently there has been a regression because with pydantic 2.5.3 pyright again reports a type error. It should be noted that this doesn't only happen for `mode="wrap"`.
Error Message
Signature-only (no traceback captured)
error.txt
Error Message
-------------
Type errors with @model_validator
The fix for #7152 was merged in v2.2.2 but apparently there has been a regression because with pydantic 2.5.3 pyright again reports a type error. It should be noted that this doesn't only happen for `mode="wrap"`.
Minimal Reproduction
repro.py
from typing import Any
from pydantic import BaseModel, ValidationError, model_validator
class UserModel(BaseModel):
username: str
password1: str
password2: str
@model_validator(mode='before')
@classmethod
def check_card_number_omitted(cls, data: Any) -> Any:
if isinstance(data, dict):
assert (
'card_number' not in data
), 'card_number should not be included'
return data
@model_validator(mode='after')
def check_passwords_match(self) -> 'UserModel':
pw1 = self.password1
pw2 = self.password2
if pw1 is not None and pw2 is not None and pw1 != pw2:
raise ValueError('passwords do not match')
return self
Environment
- Python: 3.11
- Pydantic: 2.5.3
Why It Broke
Type errors occur due to incorrect return type annotations in model validators
Fix Options (Details)
Option A — Upgrade to fixed release Safe default (recommended)
pip install pydantic==2.6.0
When NOT to use: This fix is not applicable if the return type of model validators is correctly defined.
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/8639
First fixed release: 2.6.0
Last verified: 2026-02-09. Validate in your environment.
When NOT to Use This Fix
- This fix is not applicable if the return type of model validators is correctly defined.
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 |
|---|---|
| 2.6.0 | Fixed |
Related Issues
No related fixes found.
Sources
We don’t republish the full GitHub discussion text. Use the links above for context.