diff --git a/bayesflow/approximators/continuous_approximator.py b/bayesflow/approximators/continuous_approximator.py
index 5c389b329..5ad856ddf 100644
--- a/bayesflow/approximators/continuous_approximator.py
+++ b/bayesflow/approximators/continuous_approximator.py
@@ -140,6 +140,8 @@ def sample(
         **kwargs,
     ) -> dict[str, np.ndarray]:
         conditions = self.adapter(conditions, strict=False, stage="inference", **kwargs)
+        # at inference time, inference_variables are estimated by the networks and thus ignored in conditions
+        conditions.pop("inference_variables", None)
         conditions = keras.tree.map_structure(keras.ops.convert_to_tensor, conditions)
         conditions = {"inference_variables": self._sample(num_samples=num_samples, **conditions, **kwargs)}
         conditions = keras.tree.map_structure(keras.ops.convert_to_numpy, conditions)
diff --git a/bayesflow/approximators/model_comparison_approximator.py b/bayesflow/approximators/model_comparison_approximator.py
index 6e37a843e..8c42e3cc6 100644
--- a/bayesflow/approximators/model_comparison_approximator.py
+++ b/bayesflow/approximators/model_comparison_approximator.py
@@ -208,6 +208,8 @@ def predict(
         **kwargs,
     ) -> np.ndarray:
         conditions = self.adapter(conditions, strict=False, stage="inference", **kwargs)
+        # at inference time, model_indices are predicted by the networks and thus ignored in conditions
+        conditions.pop("model_indices", None)
         conditions = keras.tree.map_structure(keras.ops.convert_to_tensor, conditions)
 
         output = self._predict(**conditions, **kwargs)