diff --git a/cubed/core/optimization.py b/cubed/core/optimization.py
index 346d8e0c..5ec5064c 100644
--- a/cubed/core/optimization.py
+++ b/cubed/core/optimization.py
@@ -11,6 +11,9 @@
 
 logger = logging.getLogger(__name__)
 
+DEFAULT_MAX_TOTAL_SOURCE_ARRAYS = 4
+DEFAULT_MAX_TOTAL_NUM_INPUT_BLOCKS = 10
+
 
 def simple_optimize_dag(dag, array_names=None):
     """Apply map blocks fusion."""
@@ -154,8 +157,8 @@ def can_fuse_predecessors(
     name,
     *,
     array_names=None,
-    max_total_source_arrays=4,
-    max_total_num_input_blocks=None,
+    max_total_source_arrays=DEFAULT_MAX_TOTAL_SOURCE_ARRAYS,
+    max_total_num_input_blocks=DEFAULT_MAX_TOTAL_NUM_INPUT_BLOCKS,
     always_fuse=None,
     never_fuse=None,
 ):
@@ -242,8 +245,8 @@ def fuse_predecessors(
     name,
     *,
     array_names=None,
-    max_total_source_arrays=4,
-    max_total_num_input_blocks=None,
+    max_total_source_arrays=DEFAULT_MAX_TOTAL_SOURCE_ARRAYS,
+    max_total_num_input_blocks=DEFAULT_MAX_TOTAL_NUM_INPUT_BLOCKS,
     always_fuse=None,
     never_fuse=None,
 ):
@@ -297,8 +300,8 @@ def multiple_inputs_optimize_dag(
     dag,
     *,
     array_names=None,
-    max_total_source_arrays=4,
-    max_total_num_input_blocks=None,
+    max_total_source_arrays=DEFAULT_MAX_TOTAL_SOURCE_ARRAYS,
+    max_total_num_input_blocks=DEFAULT_MAX_TOTAL_NUM_INPUT_BLOCKS,
     always_fuse=None,
     never_fuse=None,
 ):
diff --git a/cubed/tests/test_optimization.py b/cubed/tests/test_optimization.py
index 6d635f0c..ebb94022 100644
--- a/cubed/tests/test_optimization.py
+++ b/cubed/tests/test_optimization.py
@@ -11,6 +11,8 @@
 from cubed.backend_array_api import namespace as nxp
 from cubed.core.ops import elemwise, merge_chunks, partial_reduce
 from cubed.core.optimization import (
+    DEFAULT_MAX_TOTAL_NUM_INPUT_BLOCKS,
+    DEFAULT_MAX_TOTAL_SOURCE_ARRAYS,
     fuse_all_optimize_dag,
     fuse_only_optimize_dag,
     fuse_predecessors,
@@ -223,7 +225,11 @@ def fuse_one_level(arr, *, always_fuse=None):
     )
 
 
-def fuse_multiple_levels(*, max_total_source_arrays=4, max_total_num_input_blocks=None):
+def fuse_multiple_levels(
+    *,
+    max_total_source_arrays=DEFAULT_MAX_TOTAL_SOURCE_ARRAYS,
+    max_total_num_input_blocks=DEFAULT_MAX_TOTAL_NUM_INPUT_BLOCKS,
+):
     # use multiple_inputs_optimize_dag to test multiple levels of fusion
     return partial(
         multiple_inputs_optimize_dag,
@@ -899,8 +905,7 @@ def test_fuse_merge_chunks_unary(spec):
     b = xp.negative(a)
     c = merge_chunks(b, chunks=(3, 2))
 
-    # specify max_total_num_input_blocks to force c to fuse
-    opt_fn = fuse_multiple_levels(max_total_num_input_blocks=3)
+    opt_fn = fuse_multiple_levels()
 
     c.visualize(optimize_function=opt_fn)
 
@@ -921,6 +926,16 @@ def test_fuse_merge_chunks_unary(spec):
     result = c.compute(optimize_function=opt_fn)
     assert_array_equal(result, -np.ones((3, 2)))
 
+    # now set max_total_num_input_blocks=None which means
+    # "only fuse if ops have same number of tasks", which they don't here
+    opt_fn = fuse_multiple_levels(max_total_num_input_blocks=None)
+    optimized_dag = c.plan.optimize(optimize_function=opt_fn).dag
+
+    # merge_chunks uses a hidden op and array for block ids - ignore when comparing structure
+    assert not structurally_equivalent(
+        optimized_dag, expected_fused_dag, remove_hidden=True
+    )
+
 
 # merge chunks with different number of tasks (c has more tasks than d)
 #
@@ -936,8 +951,7 @@ def test_fuse_merge_chunks_binary(spec):
     c = xp.add(a, b)
     d = merge_chunks(c, chunks=(3, 2))
 
-    # specify max_total_num_input_blocks to force d to fuse
-    opt_fn = fuse_multiple_levels(max_total_num_input_blocks=6)
+    opt_fn = fuse_multiple_levels()
 
     d.visualize(optimize_function=opt_fn)
 
@@ -963,6 +977,16 @@ def test_fuse_merge_chunks_binary(spec):
     result = d.compute(optimize_function=opt_fn)
     assert_array_equal(result, 2 * np.ones((3, 2)))
 
+    # now set max_total_num_input_blocks=None which means
+    # "only fuse if ops have same number of tasks", which they don't here
+    opt_fn = fuse_multiple_levels(max_total_num_input_blocks=None)
+    optimized_dag = d.plan.optimize(optimize_function=opt_fn).dag
+
+    # merge_chunks uses a hidden op and array for block ids - ignore when comparing structure
+    assert not structurally_equivalent(
+        optimized_dag, expected_fused_dag, remove_hidden=True
+    )
+
 
 # like test_fuse_merge_chunks_unary, except uses partial_reduce
 def test_fuse_partial_reduce_unary(spec):
@@ -970,8 +994,7 @@ def test_fuse_partial_reduce_unary(spec):
     b = xp.negative(a)
     c = partial_reduce(b, np.sum, split_every={0: 3})
 
-    # specify max_total_num_input_blocks to force c to fuse
-    opt_fn = fuse_multiple_levels(max_total_num_input_blocks=3)
+    opt_fn = fuse_multiple_levels()
 
     c.visualize(optimize_function=opt_fn)
 
@@ -996,8 +1019,7 @@ def test_fuse_partial_reduce_binary(spec):
     c = xp.add(a, b)
     d = partial_reduce(c, np.sum, split_every={0: 3})
 
-    # specify max_total_num_input_blocks to force d to fuse
-    opt_fn = fuse_multiple_levels(max_total_num_input_blocks=6)
+    opt_fn = fuse_multiple_levels()
 
     d.visualize(optimize_function=opt_fn)
 
@@ -1176,7 +1198,7 @@ def test_optimize_stack(spec):
     c = xp.stack((a, b), axis=0)
     d = c + 1
     # try to fuse all ops into one (d will fuse with c, but c won't fuse with a and b)
-    d.compute(optimize_function=fuse_multiple_levels(max_total_num_input_blocks=10))
+    d.compute(optimize_function=fuse_multiple_levels())
 
 
 def test_optimize_concat(spec):
@@ -1186,4 +1208,4 @@ def test_optimize_concat(spec):
     c = xp.concat((a, b), axis=0)
     d = c + 1
     # try to fuse all ops into one (d will fuse with c, but c won't fuse with a and b)
-    d.compute(optimize_function=fuse_multiple_levels(max_total_num_input_blocks=10))
+    d.compute(optimize_function=fuse_multiple_levels())
diff --git a/docs/user-guide/optimization.md b/docs/user-guide/optimization.md
index b8e3a458..ec556546 100644
--- a/docs/user-guide/optimization.md
+++ b/docs/user-guide/optimization.md
@@ -112,9 +112,9 @@ e.visualize(optimize_function=opt_fn)
 
 The `max_total_num_input_blocks` argument to `multiple_inputs_optimize_dag` specifies the maximum number of input blocks (chunks) that are allowed in the fused operation.
 
-Again, this is to limit the number of reads that an individual task must perform. The default is `None`, which means that operations are fused only if they have the same number of tasks. If set to an integer, then this limitation is removed, and tasks with a different number of tasks will be fused - as long as the total number of input blocks does not exceed the maximum. This setting is useful for reductions, and can be set using `functools.partial`:
+Again, this is to limit the number of reads that an individual task must perform. If set to `None`, operations are fused only if they have the same number of tasks. If set to an integer (the default is 10), then tasks with a different number of tasks will be fused - as long as the total number of input blocks does not exceed the maximum. This setting is useful for reductions, and can be changed using `functools.partial`:
 
 ```python
-opt_fn = partial(multiple_inputs_optimize_dag, max_total_num_input_blocks=10)
+opt_fn = partial(multiple_inputs_optimize_dag, max_total_num_input_blocks=20)
 e.visualize(optimize_function=opt_fn)
 ```