diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0b6d7f61bc..07b9dbbe56 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -451,6 +451,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed wrong typehint for `Trainer.lightning_optimizers` ([#11155](https://github.com/PyTorchLightning/pytorch-lightning/pull/11155))
 
 
+- Fixed bug where the path for "last" checkpoints was not getting saved correctly which caused newer runs to not remove the previous "last" checkpoint ([#11481](https://github.com/PyTorchLightning/pytorch-lightning/pull/11481))
+
+
+- Fixed bug where the path for best checkpoints was not getting saved correctly when no metric was monitored which caused newer runs to not use the best checkpoint ([#11481](https://github.com/PyTorchLightning/pytorch-lightning/pull/11481))
+
+
 - Fixed the format of the configuration saved automatically by the CLI's `SaveConfigCallback` ([#11532](https://github.com/PyTorchLightning/pytorch-lightning/pull/11532))
 
 
diff --git a/pytorch_lightning/callbacks/model_checkpoint.py b/pytorch_lightning/callbacks/model_checkpoint.py
index 442bd7a692..f857bbdeac 100644
--- a/pytorch_lightning/callbacks/model_checkpoint.py
+++ b/pytorch_lightning/callbacks/model_checkpoint.py
@@ -641,12 +641,11 @@ class ModelCheckpoint(Callback):
             return
 
         filepath = self.format_checkpoint_name(monitor_candidates, self.CHECKPOINT_NAME_LAST)
+        # set the last model path before saving because it will be part of the state.
+        previous, self.last_model_path = self.last_model_path, filepath
         trainer.save_checkpoint(filepath, self.save_weights_only)
-
-        if self.last_model_path and self.last_model_path != filepath:
-            trainer.strategy.remove_checkpoint(self.last_model_path)
-
-        self.last_model_path = filepath
+        if previous and previous != filepath:
+            trainer.strategy.remove_checkpoint(previous)
 
     def _save_top_k_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, _METRIC]) -> None:
         if self.monitor is None or self.save_top_k == 0:
@@ -666,12 +665,11 @@ class ModelCheckpoint(Callback):
             return
 
         filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer)
+        # set the best model path before saving because it will be part of the state.
+        previous, self.best_model_path = self.best_model_path, filepath
         trainer.save_checkpoint(filepath, self.save_weights_only)
-
-        if self.save_top_k == 1 and self.best_model_path and self.best_model_path != filepath:
-            trainer.strategy.remove_checkpoint(self.best_model_path)
-
-        self.best_model_path = filepath
+        if self.save_top_k == 1 and previous and previous != filepath:
+            trainer.strategy.remove_checkpoint(previous)
 
     def _is_valid_monitor_key(self, metrics: Dict[str, _METRIC]) -> bool:
         return self.monitor in metrics or len(metrics) == 0
diff --git a/tests/checkpointing/test_model_checkpoint.py b/tests/checkpointing/test_model_checkpoint.py
index 315c33bb6c..94431030b5 100644
--- a/tests/checkpointing/test_model_checkpoint.py
+++ b/tests/checkpointing/test_model_checkpoint.py
@@ -1234,3 +1234,30 @@ def test_model_checkpoint_saveload_ckpt(tmpdir):
             assert getattr(cb_restore, key) == val
         else:
             assert getattr(cb_restore, key) != val
+
+
+def test_save_last_saves_correct_last_model_path(tmpdir):
+    mc = ModelCheckpoint(dirpath=tmpdir, save_last=True)
+    mc.CHECKPOINT_NAME_LAST = "{foo}-last"
+    trainer = Trainer(callbacks=mc)
+    trainer.strategy.connect(BoringModel())
+
+    mc._save_last_checkpoint(trainer, {"foo": 1})
+    expected = "foo=1-last.ckpt"
+    assert os.listdir(tmpdir) == [expected]
+    full_path = str(tmpdir / expected)
+    ckpt = torch.load(full_path)
+    assert ckpt["callbacks"][mc.state_key]["last_model_path"] == full_path
+
+
+def test_none_monitor_saves_correct_best_model_path(tmpdir):
+    mc = ModelCheckpoint(dirpath=tmpdir, monitor=None)
+    trainer = Trainer(callbacks=mc)
+    trainer.strategy.connect(BoringModel())
+
+    mc._save_none_monitor_checkpoint(trainer, {})
+    expected = "epoch=0-step=0.ckpt"
+    assert os.listdir(tmpdir) == [expected]
+    full_path = str(tmpdir / expected)
+    ckpt = torch.load(full_path)
+    assert ckpt["callbacks"][mc.state_key]["best_model_path"] == full_path
diff --git a/tests/deprecated_api/test_remove_2-0.py b/tests/deprecated_api/test_remove_2-0.py
index ed0520b9b8..8b7dbe7488 100644
--- a/tests/deprecated_api/test_remove_2-0.py
+++ b/tests/deprecated_api/test_remove_2-0.py
@@ -40,6 +40,7 @@ def test_v2_0_resume_from_checkpoint_trainer_constructor(tmpdir):
     assert trainer.checkpoint_connector.resume_from_checkpoint_fit_path == ckpt_path
     with pytest.deprecated_call(match=r"trainer.resume_from_checkpoint` is deprecated in v1.5"):
         trainer.fit(model)
+    ckpt_path = trainer.checkpoint_callback.best_model_path  # last `fit` replaced the `best_model_path`
     assert callback.state == 111
     assert trainer.checkpoint_connector.resume_checkpoint_path is None
     assert trainer.checkpoint_connector.resume_from_checkpoint_fit_path is None