diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0ecf93714..48f62f687 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -35,7 +35,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - 
 - 
 ### Fixed
-- 
+- Type hints for `Pipeline.model` match `models.nn`([#768](https://github.com/tinkoff-ai/etna/pull/840))
 - 
 - 
 - Fix behavior of SARIMAXModel if simple_differencing=True is set ([#837](https://github.com/tinkoff-ai/etna/pull/837))
diff --git a/etna/models/base.py b/etna/models/base.py
index 6a87ca624..33e631181 100644
--- a/etna/models/base.py
+++ b/etna/models/base.py
@@ -820,4 +820,31 @@ def get_model(self) -> "DeepBaseNet":
         return self.net
 
 
-BaseModel = Union[PerSegmentModel, PerSegmentPredictionIntervalModel, MultiSegmentModel, DeepBaseModel]
+class MultiSegmentPredictionIntervalModel(FitAbstractModel, PredictIntervalAbstractModel, BaseMixin):
+    """Class for holding specific models for multi-segment prediction which are able to build prediction intervals."""
+
+    def __init__(self):
+        """Init MultiSegmentPredictionIntervalModel."""
+        self.model = None
+
+    def get_model(self) -> Any:
+        """Get internal model that is used inside etna class.
+
+        Internal model is a model that is used inside etna to forecast segments,
+        e.g. :py:class:`catboost.CatBoostRegressor` or :py:class:`sklearn.linear_model.Ridge`.
+
+        Returns
+        -------
+        :
+           Internal model
+        """
+        return self.model
+
+
+BaseModel = Union[
+    PerSegmentModel,
+    PerSegmentPredictionIntervalModel,
+    MultiSegmentModel,
+    DeepBaseModel,
+    MultiSegmentPredictionIntervalModel,
+]
diff --git a/etna/models/nn/deepar.py b/etna/models/nn/deepar.py
index 0f04380d7..74cb6698e 100644
--- a/etna/models/nn/deepar.py
+++ b/etna/models/nn/deepar.py
@@ -10,8 +10,7 @@
 from etna import SETTINGS
 from etna.datasets.tsdataset import TSDataset
 from etna.loggers import tslogger
-from etna.models.base import Model
-from etna.models.base import PredictIntervalAbstractModel
+from etna.models.base import MultiSegmentPredictionIntervalModel
 from etna.models.base import log_decorator
 from etna.models.nn.utils import _DeepCopyMixin
 from etna.transforms import PytorchForecastingTransform
@@ -25,7 +24,7 @@
     from pytorch_lightning import LightningModule
 
 
-class DeepARModel(Model, PredictIntervalAbstractModel, _DeepCopyMixin):
+class DeepARModel(MultiSegmentPredictionIntervalModel, _DeepCopyMixin):
     """Wrapper for :py:class:`pytorch_forecasting.models.deepar.DeepAR`.
 
     Notes
@@ -84,6 +83,7 @@ def __init__(
         quantiles_kwargs:
             Additional arguments for computing quantiles, look at ``to_quantiles()`` method for your loss.
         """
+        super().__init__()
         if loss is None:
             loss = NormalDistributionLoss()
         self.max_epochs = max_epochs
diff --git a/etna/models/nn/tft.py b/etna/models/nn/tft.py
index b3ff52b09..427ad85f3 100644
--- a/etna/models/nn/tft.py
+++ b/etna/models/nn/tft.py
@@ -11,8 +11,7 @@
 from etna import SETTINGS
 from etna.datasets.tsdataset import TSDataset
 from etna.loggers import tslogger
-from etna.models.base import Model
-from etna.models.base import PredictIntervalAbstractModel
+from etna.models.base import MultiSegmentPredictionIntervalModel
 from etna.models.base import log_decorator
 from etna.models.nn.utils import _DeepCopyMixin
 from etna.transforms import PytorchForecastingTransform
@@ -26,7 +25,7 @@
     from pytorch_lightning import LightningModule
 
 
-class TFTModel(Model, PredictIntervalAbstractModel, _DeepCopyMixin):
+class TFTModel(MultiSegmentPredictionIntervalModel, _DeepCopyMixin):
     """Wrapper for :py:class:`pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer`.
 
     Notes
@@ -89,6 +88,7 @@ def __init__(
         quantiles_kwargs:
             Additional arguments for computing quantiles, look at ``to_quantiles()`` method for your loss.
         """
+        super().__init__()
         if loss is None:
             loss = QuantileLoss()
         self.max_epochs = max_epochs