diff --git a/psyneulink/core/components/functions/nonstateful/transferfunctions.py b/psyneulink/core/components/functions/nonstateful/transferfunctions.py
index da3bfd300b0..381d80d9c6b 100644
--- a/psyneulink/core/components/functions/nonstateful/transferfunctions.py
+++ b/psyneulink/core/components/functions/nonstateful/transferfunctions.py
@@ -1593,7 +1593,7 @@ def _gen_llvm_transfer(self, builder, index, ctx, vi, vo, params, state, *, tags
         builder.store(val, ptro)
 
     @handle_external_context()
-    def derivative(self, variable, output=None, context=None):
+    def derivative(self, input=None, output=None, context=None):
         """
         derivative(input)
 
@@ -1615,9 +1615,9 @@ def derivative(self, variable, output=None, context=None):
         leak = self._get_current_parameter_value(LEAK, context)
         bias = self._get_current_parameter_value(BIAS, context)
 
-        value = np.empty_like(variable)
-        value[(variable - bias) > 0] = gain
-        value[(variable - bias) <= 0] = gain * leak
+        value = np.empty_like(input)
+        value[(input - bias) > 0] = gain
+        value[(input - bias) <= 0] = gain * leak
 
         return value
 
@@ -2700,8 +2700,18 @@ def __gen_llvm_apply(self, ctx, builder, params, state, arg_in, arg_out, output_
     def _gen_llvm_function_derivative_body(self, ctx, builder, params, state, arg_in, arg_out, *, tags:frozenset):
         assert "derivative" in tags
         forward_tags = tags.difference({"derivative"})
+
+        # SoftMax derivative is calculated from the results. Recalculate them here
+        base_out = builder.alloca(arg_out.type.pointee)
+        builder = self._gen_llvm_function_body(ctx, builder, params, state, arg_in, base_out, output_type=self.output, tags=forward_tags)
+
+
         all_out = builder.alloca(arg_out.type.pointee)
-        builder = self._gen_llvm_function_body(ctx, builder, params, state, arg_in, all_out, output_type=ALL, tags=forward_tags)
+        builder = self._gen_llvm_function_body(ctx, builder, params, state, base_out, all_out, output_type=ALL, tags=forward_tags)
+
+        # The rest of the algorithm is for MAX_VAL and MAX_INDICATOR only
+        assert self.output in {MAX_VAL, MAX_INDICATOR}, \
+            "Derivative of SoftMax is only implemented for MAX_VAL and MAX_INDICATOR! ({})".format(self.output)
 
         max_pos_ptr = builder.alloca(ctx.int32_ty)
         builder.store(max_pos_ptr.type.pointee(-1), max_pos_ptr)
@@ -2819,9 +2829,12 @@ def derivative(self, input=None, output=None, context=None):
         derivative of values returned by SoftMax :  1d or 2d array (depending on *OUTPUT_TYPE* of SoftMax)
         """
 
+        if output is None:
+            output = self.function(input, context=context)
+
         output_type = self._get_current_parameter_value(OUTPUT_TYPE, context)
-        size = len(input)
-        sm = self.function(input, params={OUTPUT_TYPE: ALL}, context=context)
+        size = len(output)
+        sm = self.function(output, params={OUTPUT_TYPE: ALL}, context=context)
         sm = np.squeeze(sm)
 
         if output_type == ALL:
@@ -2839,7 +2852,7 @@ def derivative(self, input=None, output=None, context=None):
             # Return 1d array of derivatives for max element (i.e., the one chosen by SoftMax)
             derivative = np.empty(size)
             # Get the element of output returned as non-zero when output_type is not ALL
-            index_of_max = int(np.where(sm == np.max(sm))[0])
+            index_of_max = int(np.where(output == np.max(output))[0][0])
             max_val = sm[index_of_max]
             for i in range(size):
                 if i == index_of_max:
diff --git a/tests/functions/test_transfer.py b/tests/functions/test_transfer.py
index 47dc5c0a94e..3c4cef5beef 100644
--- a/tests/functions/test_transfer.py
+++ b/tests/functions/test_transfer.py
@@ -81,11 +81,11 @@ def test_execute(func, variable, params, expected, benchmark, func_mode):
     (Functions.ReLU, test_var, {'gain':RAND1, 'bias':RAND2, 'leak':RAND3}, np.where((test_var - RAND2) > 0, RAND1, RAND1 * RAND3)),
     (Functions.Tanh, test_var, {'gain':RAND1, 'bias':RAND2, 'offset':RAND3, 'scale':RAND4}, tanh_derivative_helper),
     (Functions.SoftMax, test_var, {'gain':RAND1, 'params':{kw.OUTPUT_TYPE:kw.MAX_VAL}, 'per_item': False},
-     [-0.010680386821751537, -0.011118109698906909, -0.01082040340318878, -0.010670257514724047, -0.010362498859374309,
-      -0.010933660158663306, -0.010397412260182806, -0.011602329078808718, 0.09684744183944892, -0.010262384043848513]),
+     [-0.010211427111966652, -0.010211427111966652, -0.010211427111966652, -0.010211427111966652, -0.010211427111966652,
+      -0.010211427111966652, -0.010211427111966652, -0.010211427111966652, 0.09190284400769985, -0.010211427111966652]),
     (Functions.SoftMax, test_var, {'gain':RAND1, 'params':{kw.OUTPUT_TYPE:kw.MAX_INDICATOR}, 'per_item': False},
-     [-0.010680386821751537, -0.011118109698906909, -0.01082040340318878, -0.010670257514724047, -0.010362498859374309,
-      -0.010933660158663306, -0.010397412260182806, -0.011602329078808718, 0.09684744183944892, -0.010262384043848513]),
+     [-0.012062786611097685, -0.012062786611097685, -0.012062786611097685, -0.012062786611097685, -0.012062786611097685,
+      -0.012062786611097685, -0.012062786611097685, -0.012062786611097685, 0.10856507949987917, -0.012062786611097685]),
 ]
 
 @pytest.mark.function