diff --git a/src/evaluation.jl b/src/evaluation.jl
index 807adbf..1b908dd 100644
--- a/src/evaluation.jl
+++ b/src/evaluation.jl
@@ -4,7 +4,6 @@
 @inline eval_pow(d::Minkowski, s) = abs(s)^d.p
 
 @inline eval_diff(::NonweightedMinowskiMetric, a, b, dim) = a - b
-@inline eval_diff(::Chebyshev, ::Any, b, dim) = b
 @inline eval_diff(m::WeightedMinowskiMetric, a, b, dim) = m.weights[dim] * (a-b)
 
 function evaluate_maybe_end(d::Distances.UnionMetrics, a::AbstractVector,
diff --git a/src/hyperrectangles.jl b/src/hyperrectangles.jl
index 261fac1..2ea3b3c 100644
--- a/src/hyperrectangles.jl
+++ b/src/hyperrectangles.jl
@@ -28,3 +28,16 @@ get_max_distance_no_end(m, rec, point) =
 
 get_min_distance_no_end(m, rec, point) =
     get_min_max_distance_no_end(distance_function_min, m, rec, point)
+
+@inline function update_new_min(M::Metric, old_min, hyper_rec, p_dim, split_dim, split_val)
+    @inbounds begin
+        lo = hyper_rec.mins[split_dim]
+        hi = hyper_rec.maxes[split_dim]
+    end
+    ddiff = distance_function_min(p_dim, hi, lo)
+    split_diff = abs(p_dim - split_val)
+    split_diff_pow = eval_pow(M, split_diff)
+    ddiff_pow = eval_pow(M, ddiff)
+    diff_tot = eval_diff(M, split_diff_pow, ddiff_pow, split_dim)
+    return old_min + diff_tot
+end
diff --git a/src/kd_tree.jl b/src/kd_tree.jl
index a6326ff..6147831 100644
--- a/src/kd_tree.jl
+++ b/src/kd_tree.jl
@@ -173,8 +173,6 @@ function knn_kernel!(tree::KDTree{V},
     split_dim = tree.split_dims[index]
     p_dim = point[split_dim]
     split_val = tree.split_vals[index]
-    lo = hyper_rec.mins[split_dim]
-    hi = hyper_rec.maxes[split_dim]
     split_diff = p_dim - split_val
     M = tree.metric
     # Point is to the right of the split value
@@ -183,21 +181,21 @@ function knn_kernel!(tree::KDTree{V},
         far = getleft(index)
         hyper_rec_far = HyperRectangle(hyper_rec.mins, @inbounds setindex(hyper_rec.maxes, split_val, split_dim))
         hyper_rec_close = HyperRectangle(@inbounds(setindex(hyper_rec.mins, split_val, split_dim)), hyper_rec.maxes)
-        ddiff = max(zero(eltype(V)), p_dim - hi)
     else
         close = getleft(index)
         far = getright(index)
         hyper_rec_far = HyperRectangle(@inbounds(setindex(hyper_rec.mins, split_val, split_dim)), hyper_rec.maxes)
         hyper_rec_close = HyperRectangle(hyper_rec.mins, @inbounds setindex(hyper_rec.maxes, split_val, split_dim))
-        ddiff = max(zero(eltype(V)), lo - p_dim)
     end
     # Always call closer sub tree
     knn_kernel!(tree, close, point, best_idxs, best_dists, min_dist, hyper_rec_close, skip)
 
-    split_diff_pow = eval_pow(M, split_diff)
-    ddiff_pow = eval_pow(M, ddiff)
-    diff_tot = eval_diff(M, split_diff_pow, ddiff_pow, split_dim)
-    new_min = eval_reduce(M, min_dist, diff_tot)
+    if M isa Chebyshev
+        new_min = get_min_distance_no_end(M, hyper_rec_far, point)
+    else
+        new_min = update_new_min(M, min_dist, hyper_rec, p_dim, split_dim, split_val)
+    end
+
     if new_min < best_dists[1]
         knn_kernel!(tree, far, point, best_idxs, best_dists, new_min, hyper_rec_far, skip)
     end
@@ -233,8 +231,6 @@ function inrange_kernel!(tree::KDTree,
 
     split_val = tree.split_vals[index]
     split_dim = tree.split_dims[index]
-    lo = hyper_rec.mins[split_dim]
-    hi = hyper_rec.maxes[split_dim]
     p_dim = point[split_dim]
     split_diff = p_dim - split_val
     M = tree.metric
@@ -246,13 +242,11 @@ function inrange_kernel!(tree::KDTree,
         far = getleft(index)
         hyper_rec_far = HyperRectangle(hyper_rec.mins, @inbounds setindex(hyper_rec.maxes, split_val, split_dim))
         hyper_rec_close = HyperRectangle(@inbounds(setindex(hyper_rec.mins, split_val, split_dim)), hyper_rec.maxes)
-        ddiff = max(zero(p_dim - hi), p_dim - hi)
     else # Point is to the left of the split value
         close = getleft(index)
         far = getright(index)
         hyper_rec_far = HyperRectangle(@inbounds(setindex(hyper_rec.mins, split_val, split_dim)), hyper_rec.maxes)
         hyper_rec_close = HyperRectangle(hyper_rec.mins, @inbounds setindex(hyper_rec.maxes, split_val, split_dim))
-        ddiff = max(zero(lo - p_dim), lo - p_dim)
     end
     # Call closer sub tree
     count += inrange_kernel!(tree, close, point, r, idx_in_ball, hyper_rec_close, min_dist)
@@ -263,10 +257,12 @@ function inrange_kernel!(tree::KDTree,
     # It would be interesting to benchmark this on some different data sets.
 
     # Call further sub tree with the new min distance
-    split_diff_pow = eval_pow(M, split_diff)
-    ddiff_pow = eval_pow(M, ddiff)
-    diff_tot = eval_diff(M, split_diff_pow, ddiff_pow, split_dim)
-    new_min = eval_reduce(M, min_dist, diff_tot)
+    if M isa Chebyshev
+        new_min = get_min_distance_no_end(M, hyper_rec_far, point)
+    else
+        new_min = update_new_min(M, min_dist, hyper_rec, p_dim, split_dim, split_val)
+    end
+
     count += inrange_kernel!(tree, far, point, r, idx_in_ball, hyper_rec_far, new_min)
     return count
 end
diff --git a/test/runtests.jl b/test/runtests.jl
index 584a271..29b2fae 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -80,4 +80,28 @@ using NearestNeighbors: HyperRectangle, get_min_distance_no_end, get_max_distanc
         @test get_min_distance_no_end(m, hr, point) ≈ NearestNeighbors.eval_pow(m, m(closest_point, point))
         @test get_max_distance_no_end(m, hr, point) ≈ NearestNeighbors.eval_pow(m, m(furthest_point, point))
     end
+
+    for m in ms
+        hyper_rec = NearestNeighbors.HyperRectangle{SVector{1, Float32}}(Float32[0.5553872], Float32[0.6169486])
+        point = [0.5]
+        min_dist = NearestNeighbors.get_min_distance_no_end(m, hyper_rec, point)
+        split_dim = 1
+        split_val = 0.5844354f0
+        hyper_rec_far = NearestNeighbors.HyperRectangle{SVector{1, Float32}}(Float32[0.5844354], Float32[0.6169486])
+        new_min = NearestNeighbors.update_new_min(m, min_dist, hyper_rec, point[split_dim], split_dim, split_val)
+        new_min_true = NearestNeighbors.get_min_distance_no_end(m, hyper_rec_far, point)
+        @test new_min ≈ new_min_true
+    end
+
+    for m in ms
+        hyper_rec = NearestNeighbors.HyperRectangle{SVector{2, Float64}}([0.07935189250034036, 0.682552911042077], [0.1619776648454222, 0.8046815005307764])
+        point = [0.06630748183735935, 0.7541470744398973]
+        min_dist = NearestNeighbors.get_min_distance_no_end(m, hyper_rec, point)
+        split_dim = 2
+        split_val = 0.7388396209627084
+        hyper_rec_far = NearestNeighbors.HyperRectangle{SVector{2, Float64}}([0.07935189250034036, 0.682552911042077], [0.1619776648454222, 0.7388396209627084])
+        new_min = NearestNeighbors.update_new_min(m, min_dist, hyper_rec, point[split_dim], split_dim, split_val)
+        new_min_true = NearestNeighbors.get_min_distance_no_end(m, hyper_rec_far, point)
+        @test new_min ≈ new_min_true broken = m isa Chebyshev
+    end
 end