diff --git a/libc/benchmarks/automemcpy/include/automemcpy/ResultAnalyzer.h b/libc/benchmarks/automemcpy/include/automemcpy/ResultAnalyzer.h
--- a/libc/benchmarks/automemcpy/include/automemcpy/ResultAnalyzer.h
+++ b/libc/benchmarks/automemcpy/include/automemcpy/ResultAnalyzer.h
@@ -60,7 +60,8 @@
 struct FunctionData {
   FunctionId Id;
   StringMap<PerDistributionData> PerDistributionData;
-  GradeHistogram GradeHisto = {};           // GradeEnum indexed array
+  double ScoresGeoMean;           // Geomean of scores for each distribution.
+  GradeHistogram GradeHisto = {}; // GradeEnum indexed array
   Grade::GradeEnum FinalGrade = Grade::BAD; // Overall grade for this function
 };
 
diff --git a/libc/benchmarks/automemcpy/lib/ResultAnalyzer.cpp b/libc/benchmarks/automemcpy/lib/ResultAnalyzer.cpp
--- a/libc/benchmarks/automemcpy/lib/ResultAnalyzer.cpp
+++ b/libc/benchmarks/automemcpy/lib/ResultAnalyzer.cpp
@@ -168,14 +168,17 @@
 }
 
 void castVotes(MutableArrayRef<FunctionData> Functions) {
-  for (FunctionData &Function : Functions)
+  for (FunctionData &Function : Functions) {
+    Function.ScoresGeoMean = 1.0;
     for (const auto &Pair : Function.PerDistributionData) {
       const StringRef Distribution = Pair.getKey();
       const double Score = Pair.getValue().Score;
+      Function.ScoresGeoMean *= Score;
       const auto G = Grade::judge(Score);
       ++(Function.GradeHisto[G]);
       Function.PerDistributionData[Distribution].Grade = G;
     }
+  }
 
   for (FunctionData &Function : Functions) {
     const auto &GradeHisto = Function.GradeHisto;
diff --git a/libc/benchmarks/automemcpy/lib/ResultAnalyzerMain.cpp b/libc/benchmarks/automemcpy/lib/ResultAnalyzerMain.cpp
--- a/libc/benchmarks/automemcpy/lib/ResultAnalyzerMain.cpp
+++ b/libc/benchmarks/automemcpy/lib/ResultAnalyzerMain.cpp
@@ -126,10 +126,11 @@
   fillScores(Functions);
   castVotes(Functions);
 
-  // TODO: Implement tie breaking algorithm.
   std::sort(Functions.begin(), Functions.end(),
             [](const FunctionData &A, const FunctionData &B) {
-              return A.FinalGrade < B.FinalGrade;
+              return A.FinalGrade == B.FinalGrade
+                         ? (A.ScoresGeoMean > B.ScoresGeoMean)
+                         : (A.FinalGrade < B.FinalGrade);
             });
 
   // Present data by function type.
diff --git a/libc/benchmarks/automemcpy/unittests/ResultAnalyzerTest.cpp b/libc/benchmarks/automemcpy/unittests/ResultAnalyzerTest.cpp
--- a/libc/benchmarks/automemcpy/unittests/ResultAnalyzerTest.cpp
+++ b/libc/benchmarks/automemcpy/unittests/ResultAnalyzerTest.cpp
@@ -139,27 +139,35 @@
   EXPECT_THAT(Data[1].Id, Foo2);
   EXPECT_THAT(Data[2].Id, Foo3);
 
+  const auto GetDistData = [&Data](size_t Index, StringRef Name) {
+    return Data[Index].PerDistributionData.lookup(Name);
+  };
+
   // Distribution A
   // Throughput is 0, 1 and 7, so normalized scores are 0, 1/7 and 1.
-  EXPECT_NEAR(Data[0].PerDistributionData.lookup("A").Score, 0, kAbsErr);
-  EXPECT_NEAR(Data[1].PerDistributionData.lookup("A").Score, 1. / 7, kAbsErr);
-  EXPECT_NEAR(Data[2].PerDistributionData.lookup("A").Score, 1, kAbsErr);
+  EXPECT_THAT(GetDistData(0, "A").Score, DoubleNear(0, kAbsErr));
+  EXPECT_THAT(GetDistData(1, "A").Score, DoubleNear(1. / 7, kAbsErr));
+  EXPECT_THAT(GetDistData(2, "A").Score, DoubleNear(1, kAbsErr));
   // which are turned into grades BAD,  MEDIOCRE and EXCELLENT.
-  EXPECT_THAT(Data[0].PerDistributionData.lookup("A").Grade, Grade::BAD);
-  EXPECT_THAT(Data[1].PerDistributionData.lookup("A").Grade, Grade::MEDIOCRE);
-  EXPECT_THAT(Data[2].PerDistributionData.lookup("A").Grade, Grade::EXCELLENT);
+  EXPECT_THAT(GetDistData(0, "A").Grade, Grade::BAD);
+  EXPECT_THAT(GetDistData(1, "A").Grade, Grade::MEDIOCRE);
+  EXPECT_THAT(GetDistData(2, "A").Grade, Grade::EXCELLENT);
 
   // Distribution B
   // Throughput is 30, 100 and 100, so normalized scores are 0, 1 and 1.
-  EXPECT_NEAR(Data[0].PerDistributionData.lookup("B").Score, 0, kAbsErr);
-  EXPECT_NEAR(Data[1].PerDistributionData.lookup("B").Score, 1, kAbsErr);
-  EXPECT_NEAR(Data[2].PerDistributionData.lookup("B").Score, 1, kAbsErr);
+  EXPECT_THAT(GetDistData(0, "B").Score, DoubleNear(0, kAbsErr));
+  EXPECT_THAT(GetDistData(1, "B").Score, DoubleNear(1, kAbsErr));
+  EXPECT_THAT(GetDistData(2, "B").Score, DoubleNear(1, kAbsErr));
   // which are turned into grades BAD, EXCELLENT and EXCELLENT.
-  EXPECT_THAT(Data[0].PerDistributionData.lookup("B").Grade, Grade::BAD);
-  EXPECT_THAT(Data[1].PerDistributionData.lookup("B").Grade, Grade::EXCELLENT);
-  EXPECT_THAT(Data[2].PerDistributionData.lookup("B").Grade, Grade::EXCELLENT);
+  EXPECT_THAT(GetDistData(0, "B").Grade, Grade::BAD);
+  EXPECT_THAT(GetDistData(1, "B").Grade, Grade::EXCELLENT);
+  EXPECT_THAT(GetDistData(2, "B").Grade, Grade::EXCELLENT);
 
   // Now looking from the functions point of view.
+  EXPECT_THAT(Data[0].ScoresGeoMean, DoubleNear(0, kAbsErr));
+  EXPECT_THAT(Data[1].ScoresGeoMean, DoubleNear(1. * (1. / 7), kAbsErr));
+  EXPECT_THAT(Data[2].ScoresGeoMean, DoubleNear(1, kAbsErr));
+
   // Note the array is indexed by GradeEnum values (EXCELLENT=0 / BAD = 6)
   EXPECT_THAT(Data[0].GradeHisto, ElementsAre(0, 0, 0, 0, 0, 0, 2));
   EXPECT_THAT(Data[1].GradeHisto, ElementsAre(1, 0, 0, 0, 0, 1, 0));