---
title: 'Announcement: Pydantic v2.14 Release'
description: >-
  Pydantic v2.14 adds Python 3.15 support, with lazy imports and TypeForm,
  stabilizes the MISSING sentinel, and speeds up schema generation.
date: '2026-10-08'
authors:
  - Victorien Plot
categories:
  - Pydantic Validation
  - Release
  - New Features
  - Announcements
canonical: 'https://pydantic.dev/articles/pydantic-v2-14-release'
---
> ## Documentation Index
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>
> By [Victorien Plot](https://pydantic.dev/authors/victorien-plot.md) · 2026-10-08 · Pydantic Validation, Release, New Features, Announcements
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>
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---

# Announcement: Pydantic v2.14 Release

[Pydantic v2.14](https://github.com/pydantic/pydantic/releases/tag/v2.14.0) was released on October 8th.
You can install it now from [PyPI](https://pypi.org/project/pydantic/):

```bash
pip install --upgrade pydantic
```

This release features the work of 13 external contributors and provides various new features, performance improvements, and bug fixes.
Several minor changes (considered non-breaking changes according to our [versioning policy](https://pydantic.dev/docs/validation/latest/get-started/version-policy/#pydantic-v2))
are also included in this release. Make sure to look into them before upgrading.

This release drops support for Python 3.9 and adds support for Python 3.15.

Highlights include:
* [Stabilized `MISSING` sentinel](https://pydantic.dev/articles/pydantic-v2-14-release#stabilized-missing-sentinel)
* [Support for lazy imports](https://pydantic.dev/articles/pydantic-v2-14-release#support-for-lazy-imports)
* [Support for `TypeForm`](https://pydantic.dev/articles/pydantic-v2-14-release#support-for-typeform)

You can see the full changelog on [GitHub](https://github.com/pydantic/pydantic/releases/tag/v2.14.0).

## Quick Reference

* [New Features](https://pydantic.dev/articles/pydantic-v2-14-release#new-features)
* [Changes](https://pydantic.dev/articles/pydantic-v2-14-release#changes)
* [Performance](https://pydantic.dev/articles/pydantic-v2-14-release#performance)

## New Features

Pydantic 2.14 officially supports Python 3.15, including most of the new features it introduces. These are covered first.

### Stabilized `MISSING` sentinel

The [`MISSING`](https://pydantic.dev/docs/validation/dev/concepts/types/#missing-sentinel) sentinel, added as an experimental feature in 2.12,
is now stabilized and part of the main Pydantic API.

`MISSING` is a singleton indicating that a field value was not provided during validation. During serialization, any field with `MISSING` as a value is excluded from the output.

```python
from pydantic import MISSING, BaseModel


class Configuration(BaseModel):
    timeout: int | None | MISSING = MISSING


# configuration defaults, stored somewhere else:
defaults = {'timeout': 200}

conf = Configuration()

# `timeout` is excluded from the serialization output:
conf.model_dump()
#> {}

# The `MISSING` value doesn't appear in the JSON Schema:
Configuration.model_json_schema()['properties']['timeout']
#> {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'title': 'Timeout'}

# `is` can be used to discriminate between the sentinel and other values:
timeout = conf.timeout if conf.timeout is not MISSING else defaults['timeout']
```

> **Note**
>
> Static type checkers support `MISSING` starting from [pyright 1.1.414](https://github.com/microsoft/pyright/releases/tag/1.1.414)
> and [Mypy 2.4](https://mypy-lang.blogspot.com/2026/10/mypy-24-released.html).

PR reference: [#13782](https://github.com/pydantic/pydantic/pull/13782).

### Support for lazy imports

[Lazy imports](https://docs.python.org/3.15/reference/simple_stmts.html#lazy) were introduced by [PEP 810](https://peps.python.org/pep-0810/) in Python 3.15.

Lazy imports let models in separate modules reference each other without running into cyclic imports:

`a.py`:

```python
lazy from .b import B

from pydantic import BaseModel


class A(BaseModel):
    b: B | None = None
```

`b.py`:

```python
lazy from .a import A

from pydantic import BaseModel


class B(BaseModel):
    a: A | None = None
```

See [the documentation](https://pydantic.dev/docs/validation/dev/concepts/forward_annotations/#cyclic-imports) for more details.

PR reference: [#13776](https://github.com/pydantic/pydantic/pull/13776).

### Support for `TypeForm`

[`typing.TypeForm`](https://docs.python.org/3.15/library/typing.html#typing.TypeForm) was introduced by [PEP 747](https://peps.python.org/pep-0747/) in Python 3.15, and is supported by
major static type checkers.

Pydantic now uses `TypeForm` in a number of places, in particular with [`TypeAdapter`](https://pydantic.dev/docs/validation/latest/concepts/type_adapter/):

```python
from typing import reveal_type

from pydantic import TypeAdapter

ta = TypeAdapter(int | str)

validated = ta.validate_python(1)

reveal_type(validated)
# In <= 2.13: Unknown
# In >= 2.14: int | str
```

Type checkers now infer the correct type for any type form passed to Pydantic (`x | y` unions, `Annotated[...]` forms, etc.), not just classes.

> **Note**
>
> Static type checkers support `TypeForm` starting from [pyright 1.1.412](https://github.com/microsoft/pyright/releases/tag/1.1.412)
> and [Mypy 2.2](https://mypy-lang.blogspot.com/2026/07/mypy-22-released.html).

PR reference: [#13459](https://github.com/pydantic/pydantic/pull/13459).

### Support for `frozendict`

The [`frozendict`](https://docs.python.org/3.15/builtins/stdtypes.html#frozendict) type was introduced by [PEP 814](https://peps.python.org/pep-0814/) in Python 3.15. Pydantic supports it,
and it validates and serializes like a regular `dict`. See [the documentation](https://pydantic.dev/docs/validation/dev/api/pydantic/standard_library_types/#frozen-dictionaries)
for more details.

PR reference: [#13634](https://github.com/pydantic/pydantic/pull/13634).

### `__namespace__` argument for `create_model()`

[`create_model()`](https://pydantic.dev/docs/validation/dev/concepts/models/#dynamic-model-creation) now accepts a `__namespace__` argument,
to add any attribute to the class namespace of the created model, such as validators, methods or computed fields:

```python
from pydantic import computed_field, create_model


def full_name(self) -> str:
    return f'{self.first_name} {self.last_name}'


UserModel = create_model(
    'UserModel',
    first_name=str,
    last_name=str,
    __namespace__={'full_name': computed_field(property(full_name))},
)

UserModel(first_name='John', last_name='Doe').model_dump()
#> {'first_name': 'John', 'last_name': 'Doe', 'full_name': 'John Doe'}
```

The existing `__validators__` argument is limited to validators. It is now recommended to use `__namespace__` instead, as `__validators__` will be deprecated in v3.

PR reference: [#13895](https://github.com/pydantic/pydantic/pull/13895).

### New core schema types

Several supported types now have their own [core schema](https://pydantic.dev/docs/validation/latest/internals/architecture/#communicating-between-pydantic-and-pydantic-core-the-core-schema),
meaning they are natively validated/serialized by the `pydantic-core` Rust component. Previously, Pydantic defined custom
Python validators for them, which were slower and didn't support constraints.

The following types were migrated:

- [`fractions.Fraction`](https://docs.python.org/3/library/fractions.html#fractions.Fraction) in [#13339](https://github.com/pydantic/pydantic/pull/13339). [`ZeroDivisionError`](https://docs.python.org/3/builtins/exceptions.html#ZeroDivisionError)s during validation are now properly handled.
- [Named tuples](https://docs.python.org/3/library/collections.html#namedtuple-factory-function-for-tuples-with-named-fields) in [#13505](https://github.com/pydantic/pydantic/pull/13505). This fixes a number of issues related to validation.
- [`collections.deque`](https://docs.python.org/3/library/collections.html#collections.deque) in [#13757](https://github.com/pydantic/pydantic/pull/13757).
- [`collections.OrderedDict`](https://docs.python.org/3/library/collections.html#collections.OrderedDict) in [#13796](https://github.com/pydantic/pydantic/pull/13796).
- [`collections.Counter`](https://docs.python.org/3/library/collections.html#collections.Counter) in [#13824](https://github.com/pydantic/pydantic/pull/13824).

## Changes

This release contains some minor changes that may affect existing code. Make sure to go through them before upgrading.

### JSON Schema changes

This release introduces a number of JSON Schema changes that may affect your generated model schemas.

#### `Decimal` pattern

In 2.12, [#11987](https://github.com/pydantic/pydantic/pull/11987) added regex patterns in the JSON Schema for [`Decimal`](https://docs.python.org/3/library/decimal.html#decimal.Decimal) types. This caused a number of issues, as some users define validation contracts based on the generated schema.

For this reason, the pattern is no longer included by default. A
[custom `GenerateJsonSchema` subclass](https://pydantic.dev/docs/validation/dev/concepts/json_schema/#customizing-the-json-schema-generation-process)
can be defined to enable the pattern again (see [the documentation](https://pydantic.dev/docs/validation/dev/api/pydantic/standard_library_types/#decimals) for more details).

PR reference: [#13672](https://github.com/pydantic/pydantic/pull/13672).

#### `TypeAdapter` config

The configuration of the `TypeAdapter` is now used when generating the JSON Schema:

```python
from pydantic import ConfigDict, TypeAdapter

ta = TypeAdapter(list[bytes], config=ConfigDict(ser_json_bytes='base64'))
ta.json_schema()
#> {'type': 'array', 'items': {'type': 'string', 'format': 'base64url'}}
```

PR reference: [#13676](https://github.com/pydantic/pydantic/pull/13676).

#### Other JSON Schema changes

These changes are mostly bug fixes that make the JSON Schema more consistent with the validation/serialization behavior:

- Don't apply serialization temporal formats to validation JSON Schemas in [#13711](https://github.com/pydantic/pydantic/pull/13711).
- Reflect `str_min_length` and `str_max_length` config in the JSON Schema in [#13714](https://github.com/pydantic/pydantic/pull/13714).
- Use the field name in validation JSON Schemas when `validate_by_alias` is `False` in [#13717](https://github.com/pydantic/pydantic/pull/13717).
- Fix config propagation of stdlib dataclasses and `TypedDict`s in JSON Schema in [#13891](https://github.com/pydantic/pydantic/pull/13891).
- Don't apply `ser_json_timedelta` to all datetime types in serialization inference in [#13892](https://github.com/pydantic/pydantic/pull/13892).
- Encode JSON Schema defaults with a consistent configuration in [#13893](https://github.com/pydantic/pydantic/pull/13893).

### `model_config` is no longer mutated

Prior to 2.14, the [`model_config`](https://pydantic.dev/docs/validation/dev/api/pydantic/base_model/#pydantic.BaseModel.model_config) attribute could
be mutated by Pydantic, e.g. to populate values from deprecated settings. This is no longer the case: `model_config` will always reflect what was
set by the user (merged with the configuration of parent classes):

```python
from pydantic import BaseModel


class Base(BaseModel):
    model_config = {'title': 'MyBase'}


class Model(Base):
    model_config = {'populate_by_name': True}


Model.model_config
#> In <= 2.13: {'title': 'MyBase', 'populate_by_name': True, 'validate_by_alias': True, 'validate_by_name': True}
#> In >= 2.14: {'title': 'MyBase', 'populate_by_name': True}
```

PR reference: [#13825](https://github.com/pydantic/pydantic/pull/13825).

### Model signature with `validate_by_alias=False`

When [`validate_by_alias`](https://pydantic.dev/docs/validation/latest/api/pydantic/config/#pydantic.config.ConfigDict.validate_by_alias) is set to `False`,
the generated `__signature__` of models and Pydantic dataclasses now uses the field name instead of the alias, matching what validation actually accepts:

```python
import inspect

from pydantic import BaseModel, ConfigDict, Field


class Model(BaseModel):
    model_config = ConfigDict(validate_by_alias=False, validate_by_name=True)

    my_field: int = Field(alias='myAlias')


inspect.signature(Model)
#> In <= 2.13: (*, myAlias: int) -> None
#> In >= 2.14: (*, my_field: int) -> None
```

Contributed by [@jaideeppyne](https://github.com/jaideeppyne). PR reference: [#13730](https://github.com/pydantic/pydantic/pull/13730).

### `multiple_of` constraint

Using a non-positive value for `multiple_of` now raises an error when the model is defined. Previously, an unhandled runtime exception was raised during validation.

PR reference: [#13862](https://github.com/pydantic/pydantic/pull/13862).

### Numeric constraints in the pipeline API

In the experimental [pipeline API](https://pydantic.dev/docs/validation/latest/concepts/experimental/#pipeline-api), numeric constraints
(`gt()`, `ge()`, `lt()`, `le()` and `multiple_of()`) are now always applied natively by `pydantic-core` on numeric types. Previously, some of them were
applied as Python validators, e.g. when chaining several constraints or when the constraint value didn't match the validated type. As a result,
these constraints are now included in the JSON Schema, and validation errors are the same as for regular fields:

```python
from typing import Annotated

from pydantic import TypeAdapter
from pydantic.experimental.pipeline import validate_as

ta = TypeAdapter(Annotated[int, validate_as(int).ge(1).le(100)])

ta.json_schema()
#> In <= 2.13: {'minimum': 1, 'type': 'integer'}
#> In >= 2.14: {'maximum': 100, 'minimum': 1, 'type': 'integer'}

ta.validate_python(200)
"""
In <= 2.13:
  Value error, Expected <= 100 [type=value_error, input_value=200, input_type=int]
In >= 2.14:
  Input should be less than or equal to 100 [type=less_than_equal, input_value=200, input_type=int]
"""
```

PR reference: [#13516](https://github.com/pydantic/pydantic/pull/13516).

### Invalid `index_key` in wrap serializers

The handler of [wrap serializers](https://pydantic.dev/docs/validation/latest/concepts/serialization/#model-wrap-serializer) takes an optional
second `index_key` argument, which must be an integer or a string. A previous docstring example wrongly passed the `info` argument instead,
which went unnoticed unless `include` or `exclude` was used. Such invalid values now raise an explicit error:

```python
from pydantic import BaseModel, model_serializer


class Model(BaseModel):
    a: int

    @model_serializer(mode='wrap')
    def ser_model(self, handler, info):
        return handler(self, info)  # should be `handler(self)`


Model(a=1).model_dump()
"""
In <= 2.13:
  {'a': 1}
In >= 2.14:
  PydanticSerializationError: Error calling function `ser_model`: TypeError: 'index_key' is expected to be an integer or a string, got 'SerializationInfo(...)'
"""
```

If you copied this pattern, remove the second argument when calling the handler.

PR reference: [#13506](https://github.com/pydantic/pydantic/pull/13506).

## Performance

2.14 includes several optimizations to schema generation. Most of them individually reduce model build time by 5–20% on our benchmarks, which adds up at import time for applications that define many models:

- Optimize type lookup logic in core schema generation in [#13573](https://github.com/pydantic/pydantic/pull/13573).
- Refactor type references logic in [#13643](https://github.com/pydantic/pydantic/pull/13643).
- Move schema gathering logic to `pydantic-core` in [#13725](https://github.com/pydantic/pydantic/pull/13725).
- Improve performance of `FieldInfo` construction in [#13726](https://github.com/pydantic/pydantic/pull/13726).
- Improve performance of `GenerateSchema.generate_schema()` dispatching in [#13614](https://github.com/pydantic/pydantic/pull/13614).
- Avoid exponential core schema traversal in `gather_schemas_for_cleaning()` in [#13523](https://github.com/pydantic/pydantic/pull/13523).
- Introduce micro-optimizations for model class building in [#13540](https://github.com/pydantic/pydantic/pull/13540).

---

Ready to try the new features? Upgrade now with `pip install --upgrade pydantic` or `uv add --upgrade pydantic` and check out the [full release notes](https://github.com/pydantic/pydantic/releases/tag/v2.14.0). Have questions or feedback? Join the conversation on our community [Slack](https://pydantic.dev/docs/logfire/join-slack/).