diff --git a/src/transformers/integrations/heterogeneity/configuration_utils.py b/src/transformers/integrations/heterogeneity/configuration_utils.py
index 836060b9d343..6a8216387db9 100644
--- a/src/transformers/integrations/heterogeneity/configuration_utils.py
+++ b/src/transformers/integrations/heterogeneity/configuration_utils.py
@@ -227,13 +227,21 @@ def __getitem__(self, layer_idx: int | slice | str) -> PreTrainedConfig | list[P
                     f"Available layer types: {set(layer_types)}"
                 )
 
-            configs = [self[i] for i, layer_type in enumerate(layer_types) if layer_type == layer_idx]
-            if any(config != configs[0] for config in configs):
-                raise ValueError(
-                    f"Layer type '{layer_idx}' is not homogeneous across layers. "
-                    f"Use an integer index to access a specific layer's config."
-                )
-            return configs[0]
+            # Config is actually homogeneous so just return the global config
+            if not self._config.is_heterogeneous:
+                return self._config
+
+            # Ensure that all layers of the requested type have the same overrides
+            layer_overrides = self._config._heterogeneity_spec.per_layer_overrides
+            reference_overrides = layer_overrides.get(layer_types.index(layer_idx), {})
+            for idx, layer_type in enumerate(layer_types):
+                if layer_type == layer_idx and layer_overrides.get(idx, {}) != reference_overrides:
+                    raise ValueError(
+                        f"Layer type '{layer_idx}' is not homogeneous across layers (layer {idx} differs). "
+                        f"Use an integer index to access a specific layer's config."
+                    )
+
+            return _get_layer_config(self._config, reference_overrides)
 
         # Return a list of configs for a slice of layers
         if isinstance(layer_idx, slice):
diff --git a/tests/heterogeneity/test_configuration_utils.py b/tests/heterogeneity/test_configuration_utils.py
index 91624b67583b..356d5d553e7b 100644
--- a/tests/heterogeneity/test_configuration_utils.py
+++ b/tests/heterogeneity/test_configuration_utils.py
@@ -131,6 +131,50 @@ def test_uniform_per_layer_values_do_not_overwrite_global(self):
         for layer_idx in range(4):
             self.assertEqual(config.per_layer_config[layer_idx].num_key_value_heads, 2)
 
+    def test_indexing_by_layer_type(self):
+        config = _tiny_llama_config(
+            per_layer_config={1: {"num_key_value_heads": 2}, 3: {"num_key_value_heads": 2}},
+            layer_types=["full_attention", "sliding_attention"] * 2,
+        )
+
+        self.assertEqual(config.per_layer_config["full_attention"].num_key_value_heads, 4)
+        self.assertEqual(config.per_layer_config["sliding_attention"].num_key_value_heads, 2)
+
+    def test_indexing_homogeneous_config_by_layer_type_returns_global_config(self):
+        config = _tiny_llama_config(layer_types=["full_attention", "sliding_attention"] * 2)
+
+        self.assertIs(config.per_layer_config["sliding_attention"], config)
+
+    def test_indexing_by_layer_type_ignores_other_layer_types(self):
+        """Layers of a different type may differ, only the requested type has to be homogeneous."""
+        config = _tiny_llama_config(
+            per_layer_config={0: {"intermediate_size": 32}, 2: {"intermediate_size": 256}},
+            layer_types=["full_attention", "sliding_attention"] * 2,
+        )
+
+        self.assertEqual(config.per_layer_config["sliding_attention"].intermediate_size, 128)
+
+    def test_indexing_by_heterogeneous_layer_type_raises(self):
+        config = _tiny_llama_config(
+            per_layer_config={0: {"intermediate_size": 32}},
+            layer_types=["full_attention", "sliding_attention"] * 2,
+        )
+
+        with self.assertRaisesRegex(ValueError, "'full_attention' is not homogeneous across layers"):
+            config.per_layer_config["full_attention"]
+
+    def test_indexing_by_unknown_layer_type_raises(self):
+        config = _tiny_llama_config(layer_types=["full_attention"] * 4)
+
+        with self.assertRaisesRegex(ValueError, "'sliding_attention' not found in config.layer_types"):
+            config.per_layer_config["sliding_attention"]
+
+    def test_indexing_by_layer_type_without_layer_types_raises(self):
+        config = _tiny_llama_config(per_layer_config={0: {"intermediate_size": 32}})
+
+        with self.assertRaisesRegex(ValueError, "config.layer_types is not defined"):
+            config.per_layer_config["full_attention"]
+
     def test_explicit_serialization_restores_pruned_global_values(self):
         per_layer = {layer_idx: {"num_key_value_heads": 4} for layer_idx in range(4)}
         sparse_config = _tiny_llama_config(per_layer_config=per_layer)