diff --git a/h2o-algos/src/main/java/hex/glm/GLMMetricBuilder.java b/h2o-algos/src/main/java/hex/glm/GLMMetricBuilder.java
index 5bcb85e6238f..e6418a0ba5ea 100644
--- a/h2o-algos/src/main/java/hex/glm/GLMMetricBuilder.java
+++ b/h2o-algos/src/main/java/hex/glm/GLMMetricBuilder.java
@@ -189,7 +189,7 @@ public final long resDOF() {
 
   protected void computeAIC(GLMModel gm) {
     if (gm._parms._calc_like && gm._finalScoring) { // uses likelihood which is calculated for the final scoring
-      _aic = -2 * _log_likelihood + 2 * Arrays.stream(gm.beta()).filter(b -> b != 0).count();
+      _aic = 2 * _log_likelihood + 2 * Arrays.stream(gm.beta()).filter(b -> b != 0).count();
     } else { // original calculation for the model build
       _aic = 0;
       switch (_glmf._family) {
diff --git a/h2o-algos/src/test/java/hex/glm/GLMTestAICLikelihood.java b/h2o-algos/src/test/java/hex/glm/GLMTestAICLikelihood.java
index 40197ea0a5dd..99d864ee2490 100644
--- a/h2o-algos/src/test/java/hex/glm/GLMTestAICLikelihood.java
+++ b/h2o-algos/src/test/java/hex/glm/GLMTestAICLikelihood.java
@@ -61,7 +61,7 @@ public void testGaussianAICLikelihood() {
       }
       assertTrue("Dispersion parameter estimation from model: "+model._parms._dispersion_estimated+".  Manual dispersion estimation: "+dispersion_estimated_manual+" and they are different.", Math.abs(dispersion_estimated_manual-model._parms._dispersion_estimated)<1e-6);
       assertTrue("Log likelihood from model: "+((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood+".  Manual loglikelihood: "+logLike+" and they are different.", Math.abs(logLike-((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood)<1e-6);
-      double aic = -2*logLike + 2*model._output.rank();
+      double aic = 2*logLike + 2*model._output.rank();
       assertTrue("AIC from model: "+((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC+".  Manual AIC: "+aic+" and they are different.", Math.abs(aic-((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC)<1e-6);
       System.out.println(((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood + " " + ((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC);
       System.out.println(logLike + " " + aic);
@@ -99,7 +99,7 @@ public void testBinomialAICLikelihood() {
         logLike +=  w * (yr * log(probabilityOf1) + (1-yr) * log(1 - probabilityOf1));
       }
       assertTrue("Log likelihood from model: "+((ModelMetricsBinomialGLM) model._output._training_metrics)._loglikelihood+".  Manual loglikelihood: "+logLike+" and they are different.", Math.abs(logLike-((ModelMetricsBinomialGLM) model._output._training_metrics)._loglikelihood)<1e-6);
-      double aic = -2*logLike + 2*model._output.rank();
+      double aic = 2*logLike + 2*model._output.rank();
       assertTrue("AIC from model: "+((ModelMetricsBinomialGLM) model._output._training_metrics)._AIC+".  Manual AIC: "+aic+" and they are different.", Math.abs(aic-((ModelMetricsBinomialGLM) model._output._training_metrics)._AIC)<1e-6);
       System.out.println(((ModelMetricsBinomialGLM) model._output._training_metrics)._loglikelihood + " " + ((ModelMetricsBinomialGLM) model._output._training_metrics)._AIC);
       System.out.println(logLike + " " + aic);
@@ -144,7 +144,7 @@ public void testQuasibinomialAICLikelihood() {
         }
       }
       assertTrue("Log likelihood from model: "+((ModelMetricsBinomialGLM) model._output._training_metrics)._loglikelihood+".  Manual loglikelihood: "+logLike+" and they are different.", Math.abs(logLike-((ModelMetricsBinomialGLM) model._output._training_metrics)._loglikelihood)<1e-6);
-      double aic = -2*logLike + 2*model._output.rank();
+      double aic = 2*logLike + 2*model._output.rank();
       assertTrue("AIC from model: "+((ModelMetricsBinomialGLM) model._output._training_metrics)._AIC+".  Manual AIC: "+aic+" and they are different.", Math.abs(aic-((ModelMetricsBinomialGLM) model._output._training_metrics)._AIC)<1e-6);
       System.out.println(((ModelMetricsBinomialGLM) model._output._training_metrics)._loglikelihood + " " + ((ModelMetricsBinomialGLM) model._output._training_metrics)._AIC);
       System.out.println(logLike + " " + aic);
@@ -186,7 +186,7 @@ public void testFractionalbinomialAICLikelihood() {
         }
       }
       assertTrue("Log likelihood from model: "+((ModelMetricsBinomialGLM) model._output._training_metrics)._loglikelihood+".  Manual loglikelihood: "+logLike+" and they are different.", Math.abs(logLike-((ModelMetricsBinomialGLM) model._output._training_metrics)._loglikelihood)<1e-6);
-      double aic = -2*logLike + 2*model._output.rank();
+      double aic = 2*logLike + 2*model._output.rank();
       assertTrue("AIC from model: "+((ModelMetricsBinomialGLM) model._output._training_metrics)._AIC+".  Manual AIC: "+aic+" and they are different.", Math.abs(aic-((ModelMetricsBinomialGLM) model._output._training_metrics)._AIC)<1e-6);
       System.out.println(((ModelMetricsBinomialGLM) model._output._training_metrics)._loglikelihood + " " + ((ModelMetricsBinomialGLM) model._output._training_metrics)._AIC);
       System.out.println(logLike + " " + aic);
@@ -225,7 +225,7 @@ public void testPoissonAICLikelihood() {
         }
       }
       assertTrue("Log likelihood from model: "+((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood+".  Manual loglikelihood: "+logLike+" and they are different.", Math.abs(logLike-((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood)<1e-6);
-      double aic = -2*logLike + 2*model._output.rank();
+      double aic = 2*logLike + 2*model._output.rank();
       assertTrue("AIC from model: "+((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC+".  Manual AIC: "+aic+" and they are different.", Math.abs(aic-((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC)<1e-6);
       System.out.println(((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood + " " + ((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC);
       System.out.println(logLike + " " + aic);
@@ -264,7 +264,7 @@ public void testNegativeBinomialAICLikelihood() {
                 + log(Gamma.gamma(yr + 1/inv_theta_estimated) / (Gamma.gamma(yr + 1) * Gamma.gamma(1/inv_theta_estimated)));
       }
       assertTrue("Log likelihood from model: "+((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood+".  Manual loglikelihood: "+logLike+" and they are different.", Math.abs(logLike-((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood)<1e-6);
-      double aic = -2*logLike + 2*model._output.rank();
+      double aic = 2*logLike + 2*model._output.rank();
       assertTrue("AIC from model: "+((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC+".  Manual AIC: "+aic+" and they are different.", Math.abs(aic-((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC)<1e-6);
       System.out.println(((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood + " " + ((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC);
       System.out.println(logLike + " " + aic);
@@ -306,7 +306,7 @@ public void testGammaAICLikelihood() {
         }
       }
       assertTrue("Log likelihood from model: "+((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood+".  Manual loglikelihood: "+logLike+" and they are different.", Math.abs(logLike-((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood)<1e-6);
-      double aic = -2*logLike + 2*model._output.rank();
+      double aic = 2*logLike + 2*model._output.rank();
       assertTrue("AIC from model: "+((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC+".  Manual AIC: "+aic+" and they are different.", Math.abs(aic-((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC)<1e-6);
       System.out.println(((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood + " " + ((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC);
       System.out.println(logLike + " " + aic);
@@ -346,7 +346,7 @@ public void testMultinomialAICLikelihood() {
         logLike += log(predictedProbabilityOfActualClass);
       }
       assertTrue("Log likelihood from model: "+((ModelMetricsBinomialGLM.ModelMetricsMultinomialGLM) model._output._training_metrics)._loglikelihood+".  Manual loglikelihood: "+logLike+" and they are different.", Math.abs(logLike-((ModelMetricsBinomialGLM.ModelMetricsMultinomialGLM) model._output._training_metrics)._loglikelihood)<1e-6);
-      double aic = -2*logLike + 2*model._output.rank();
+      double aic = 2*logLike + 2*model._output.rank();
       assertTrue("AIC from model: "+((ModelMetricsBinomialGLM.ModelMetricsMultinomialGLM) model._output._training_metrics)._AIC+".  Manual AIC: "+aic+" and they are different.", Math.abs(aic-((ModelMetricsBinomialGLM.ModelMetricsMultinomialGLM) model._output._training_metrics)._AIC)<1e-6);
       System.out.println(((ModelMetricsBinomialGLM.ModelMetricsMultinomialGLM) model._output._training_metrics)._loglikelihood + " " + ((ModelMetricsBinomialGLM.ModelMetricsMultinomialGLM) model._output._training_metrics)._AIC);
       System.out.println(logLike + " " + aic);
@@ -378,7 +378,7 @@ public void testTweedieAICLikelihood() {
       // only check that loglikelihood is calculated
       double logLike = ((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood;
       assertNotEquals("Log likelihood from model: "+((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood, 0.0, logLike);
-      double aic = -2*logLike + 2*model._output.rank();
+      double aic = 2*logLike + 2*model._output.rank();
       assertTrue("AIC from model: "+((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC+".  Manual AIC: "+aic+" and they are different.", Math.abs(aic-((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC)<1e-6);
       System.out.println(((ModelMetricsRegressionGLM) model._output._training_metrics)._loglikelihood + " " + ((ModelMetricsRegressionGLM) model._output._training_metrics)._AIC);
       System.out.println(logLike + " " + aic);
diff --git a/h2o-r/tests/testdir_algos/glm/runit_PUBDEV_8947_GLM_likelihoods_aic.R b/h2o-r/tests/testdir_algos/glm/runit_PUBDEV_8947_GLM_likelihoods_aic.R
index d4ed423950f8..dc139c1fbedb 100644
--- a/h2o-r/tests/testdir_algos/glm/runit_PUBDEV_8947_GLM_likelihoods_aic.R
+++ b/h2o-r/tests/testdir_algos/glm/runit_PUBDEV_8947_GLM_likelihoods_aic.R
@@ -16,7 +16,7 @@ test.glm.aic.likelihood <- function() {
   dev <- 157465
   rRank <- 9
   loglikeR <- -0.5*((nobs-1) + nobs*log(2*pi*dev/(nobs-1)))
-  aicR <- -2*loglikeR+2*rRank
+  aicR <- 2*loglikeR+2*rRank
   perf <- h2o.performance(model.h2o.gaussian.identity)
   print("GLM Gaussian")
   print("H2O AIC")
diff --git a/h2o-r/tests/testdir_algos/glm/runit_gh_15891_tweedie_aic.R b/h2o-r/tests/testdir_algos/glm/runit_gh_15891_tweedie_aic.R
new file mode 100644
index 000000000000..40e09578e130
--- /dev/null
+++ b/h2o-r/tests/testdir_algos/glm/runit_gh_15891_tweedie_aic.R
@@ -0,0 +1,85 @@
+setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
+source("../../../scripts/h2o-r-test-setup.R")
+
+test_glm_tweedies <- function() {
+    require(statmod)
+    require(tweedie)
+    if (requireNamespace("tweedie")) {
+        num_rows <- 1000
+        num_cols <- 5
+        f1 <- random_dataset_real_only(num_rows, num_cols) # generate dataset containing the predictors.
+        f1R <- as.data.frame(h2o.abs(f1))
+        weights <- c(0.1, 0.2, 0.3, 0.4, 0.5, 1) # weights to generate the mean
+        mu <- generate_mean(f1R, num_rows, num_cols, weights)
+        pow <- c(1.1) # variance power range
+        phi <- c(1) # dispersion factor range
+        y <- "resp" # response column
+        x <- c("abs.C1.", "abs.C2.", "abs.C3.", "abs.C4.", "abs.C5.")
+        for (ind in c(1:length(pow))) { # generate dataset with each variance power and dispersion factor
+            trainF <- generate_dataset(f1R, num_rows, num_cols, pow[ind], phi[ind], mu)
+            print(paste("Compare H2O, R GLM model coefficients and standard error for var_power=", pow[ind], "link_power=", 1 - pow[ind], sep = " "))
+            compareH2ORGLM(pow[ind], 1 - pow[ind], x, y, trainF, as.data.frame(trainF), phi[ind])
+        }
+    } else {
+        print("test_glm_tweedies is skipped. Need to install tweedie package.")
+    }
+}
+
+generate_dataset <- function(f1R, numRows, numCols, pow, phi, mu) {
+    resp <- tweedie::rtweedie(numRows, xi = pow, mu, phi, power = pow)
+    f1h2o <- as.h2o(f1R)
+    resph2o <- as.h2o(as.data.frame(resp))
+    finalFrame <- h2o.cbind(f1h2o, resph2o)
+    return(finalFrame)
+}
+
+generate_mean <- function(f1R, numRows, numCols, weights) {
+    y <- c(1:numRows)
+    for (rowIndex in c(1:numRows)) {
+        tempResp = 0.0
+        for (colIndex in c(1:numCols)) {
+            tempResp = tempResp + weights[colIndex] * f1R[rowIndex, colIndex]
+        }
+        y[rowIndex] = tempResp
+    }
+    return(y)
+}
+
+compareH2ORGLM <-
+    function(vpower, lpower, x, y, hdf, df, truedisp, tolerance = 2e-4) {
+        print("Define formula for R")
+        formula <- (df[, "resp"] ~ .)
+        rmodel <- glm(
+            formula = formula,
+            data = df[, x],
+            family = tweedie(var.power = vpower, link.power =
+                lpower),
+            na.action = na.omit
+        )
+        rAIC <- AICtweedie(rmodel)
+        h2omodel <-
+            h2o.glm(
+                x = x,
+                y = y,
+                training_frame = hdf,
+                family = "tweedie",
+                link = "tweedie",
+                tweedie_variance_power = vpower,
+                tweedie_link_power = lpower,
+                alpha = 0.5,
+                lambda = 0,
+                nfolds = 0,
+                compute_p_values = TRUE,
+                calc_like = TRUE,
+                fix_tweedie_variance_power = TRUE
+            )
+        h2oAIC <- h2o.aic(h2omodel)
+        print("Comparing H2O and R GLM model AIC.")
+        print("R AIC")
+        print(rAIC)
+        print("h2o model AIC")
+        print(h2oAIC)
+        expect_true(abs(rAIC - h2oAIC) / h2oAIC < 1e-2)
+    }
+
+doTest("Comparison of H2O to R TWEEDIE family AIC with tweedie dataset", test_glm_tweedies)