diff --git a/mlprof/tasks/parameters.py b/mlprof/tasks/parameters.py
index 88fb66a..7f6fa74 100644
--- a/mlprof/tasks/parameters.py
+++ b/mlprof/tasks/parameters.py
@@ -4,7 +4,7 @@
 Collection of the recurrent luigi parameters for different tasks.
 """
 
-from __future__ import annotations
+# from __future__ import annotations
 
 import os
 from dataclasses import dataclass
@@ -144,7 +144,7 @@ class ModelParameters(BaseTask):
     model_label = luigi.Parameter(
         default=law.NO_STR,
         description="when set, use this label in plots; when empty, the 'network_name' field in the model json data is "
-        "used when existing, and full_model_name otherwise; default: empty",
+        "used when existing, and full_name otherwise; default: empty",
     )
 
     def __init__(self, *args, **kwargs):
@@ -152,8 +152,8 @@ def __init__(self, *args, **kwargs):
 
         self.model = Model(
             model_file=self.model_file,
-            name=self.name if self.name != law.NO_STR else None,
-            label=self.label if self.label != law.NO_STR else None,
+            name=self.model_name if self.model_name != law.NO_STR else None,
+            label=self.model_label if self.model_label != law.NO_STR else None,
         )
 
     def store_parts(self):
@@ -168,48 +168,69 @@ def store_parts(self):
         return parts
 
 
-# class MultiModelParameters(BaseTask):
-#     """
-#     General parameters for the model definition and the runtime measurement.
-#     """
-
-#     model_files = luigi.Parameter(
-#         default="$MLP_BASE/examples/simple_dnn/model.json",
-#         description="json file containing information of model to be tested; "
-#         "default: $MLP_BASE/examples/simple_dnn/model.json",
-#     )
-#     model_names = luigi.Parameter(
-#         default=law.NO_STR,
-#         description="when set, use this name for storing outputs instead of a hashed version of "
-#         "--model-file; default: empty",
-#     )
-#     model_labels = luigi.Parameter(
-#         default=law.NO_STR,
-#         description="when set, use this label in plots; when empty, the 'network_name' field in the "
-#         "model json data is used when existing, and full_model_name otherwise; default: empty",
-#     )
-
-#     def __init__(self, *args, **kwargs):
-#         super().__init__(*args, **kwargs)
-
-#         # TODO: check that lengths match ...
-#         pass
-
-#         self.models = [
-#             Model()
-#             for x, y, z in zip(...)
-#         ]
-
-#     def store_parts(self):
-#         parts = super().store_parts()
-
-#         # build a combined string that represents the significant parameters
-#         params = [
-#             f"model_{self.model.full_name}",
-#         ]
-#         parts.insert_before("version", "model_params", "__".join(params))
-
-#         return parts
+class MultiModelParameters(BaseTask):
+    """
+    General parameters for the model definition and the runtime measurement.
+    """
+
+    model_files = law.CSVParameter(
+        description="comma-separated list of json files containing information of models to be tested",
+        brace_expand=True,
+    )
+    model_names = law.CSVParameter(
+        default=law.NO_STR,
+        description="comma-separated list of names of models defined in --model-files to use in output paths "
+        "instead of a hashed version of model_files; when set, the number of names must match the number of "
+        "model files; default: ()",
+    )
+    model_labels = law.CSVParameter(
+        default=law.NO_STR,
+        description="when set, use this label in plots; when empty, the 'network_name' field in the "
+        "model json data is used when existing, and full_model_name otherwise; default: empty",
+    )
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # check that lengths match if initialized
+        from IPython import embed; embed()
+        if self.model_names[0] == law.NO_STR:
+            if self.model_labels[0] != law.NO_STR:
+                if len(self.model_files) != len(self.model_labels):
+                    raise ValueError("the length of model_files and model_labels muss be the same")
+        elif self.model_labels[0] == law.NO_STR:
+            if len(self.model_files) != len(self.model_names):
+                raise ValueError("the length of model_files and model_names muss be the same")
+        else:
+            if len({len(self.model_files), len(self.model_names), len(self.model_labels)}) != 1:
+                raise ValueError("the length of model_names, model_files and model_labels muss be the same")
+
+        # if not initialized, change size objects for them to match
+        if len(self.model_names) != len(self.model_files):
+            self.model_names = (law.NO_STR,) * len(self.model_files)
+        if len(self.model_labels) != len(self.model_files):
+            self.model_labels = (law.NO_STR,) * len(self.model_files)
+
+        # define Model objects
+        self.models = [
+            Model(
+                model_file=x,
+                name=y if y != law.NO_STR else None,
+                label=z if z != law.NO_STR else None,
+            )
+            for x, y, z in zip(self.model_files, self.model_names, self.model_labels)
+        ]
+
+    def store_parts(self):
+        parts = super().store_parts()
+
+        # build a combined string that represents the significant parameters
+        params = [
+            f"model_{model.full_name}" for model in self.models
+        ]
+        parts.insert_before("version", "model_params", "__".join(params))
+
+        return parts
 
 
 class BatchSizesParameters(BaseTask):
diff --git a/mlprof/tasks/runtime.py b/mlprof/tasks/runtime.py
index e13c79e..a8f49d0 100644
--- a/mlprof/tasks/runtime.py
+++ b/mlprof/tasks/runtime.py
@@ -12,7 +12,8 @@
 
 from mlprof.tasks.base import CommandTask, PlotTask, view_output_plots
 from mlprof.tasks.parameters import (
-    RuntimeParameters, ModelParameters, CMSSWParameters, BatchSizesParameters, CustomPlotParameters,
+    RuntimeParameters, ModelParameters, MultiModelParameters, CMSSWParameters, BatchSizesParameters,
+    CustomPlotParameters,
 )
 from mlprof.tasks.sandboxes import CMSSWSandboxTask
 from mlprof.plotting.plotter import plot_batch_size_several_measurements
@@ -176,16 +177,12 @@ def run(self):
         output = self.output()
         output.parent.touch()
 
-        # get name network for legend
-        model_data = self.model_data
-        network_name = model_data["network_name"]
-
         # create the plot
         plot_batch_size_several_measurements(
             self.batch_sizes,
             [self.input().path],
             output.path,
-            [network_name],
+            [self.model.full_model_label],
             self.custom_plot_params,
         )
         print("plot saved")
@@ -193,6 +190,7 @@ def run(self):
 
 class PlotRuntimesMultipleParams(
     RuntimeParameters,
+    MultiModelParameters,
     BatchSizesParameters,
     PlotTask,
     CustomPlotParameters,
@@ -205,15 +203,6 @@ class PlotRuntimesMultipleParams(
 
     sandbox = "bash::$MLP_BASE/sandboxes/plotting.sh"
 
-    model_files = law.CSVParameter(
-        description="comma-separated list of json files containing information of models to be tested",
-        brace_expand=True,
-    )
-    model_names = law.CSVParameter(
-        default=(),
-        description="comma-separated list of names of models defined in --model-files to use in output paths; "
-        "when set, the number of names must match the number of model files; default: ()",
-    )
     cmssw_versions = law.CSVParameter(
         default=(CMSSWParameters.cmssw_version._default,),
         description=f"comma-separated list of CMSSW versions; default: ({CMSSWParameters.cmssw_version._default},)",
@@ -272,6 +261,7 @@ def run(self):
 
         # create the plot
         # TODO: maybe adjust labels
+        from IPython import embed; embed()
         plot_batch_size_several_measurements(
             self.batch_sizes,
             input_paths,