diff --git a/promql/engine.go b/promql/engine.go
index 9e0a6b17c..770550dac 100644
--- a/promql/engine.go
+++ b/promql/engine.go
@@ -1279,29 +1279,19 @@ func (ev *evaluator) rangeEval(prepSeries func(labels.Labels, *EvalSeriesHelper)
 	return mat, warnings
 }
 
-func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping []string) (Matrix, annotations.Annotations) {
-	originalNumSamples := ev.currentSamples
+func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping []string, inputMatrix Matrix, param float64) (Matrix, annotations.Annotations) {
+	// Keep a copy of the original point slice so that it can be returned to the pool.
+	origMatrix := slices.Clone(inputMatrix)
+	defer func() {
+		for _, s := range origMatrix {
+			putFPointSlice(s.Floats)
+			putHPointSlice(s.Histograms)
+		}
+	}()
+
 	var warnings annotations.Annotations
 
-	// param is the number k for topk/bottomk, or q for quantile.
-	var param float64
-	if aggExpr.Param != nil {
-		val, ws := ev.eval(aggExpr.Param)
-		warnings.Merge(ws)
-		param = val.(Matrix)[0].Floats[0].F
-	}
-	// Now fetch the data to be aggregated.
-	// ev.currentSamples will be updated to the correct value within the ev.eval call.
-	val, ws := ev.eval(aggExpr.Expr)
-	warnings.Merge(ws)
-	inputMatrix := val.(Matrix)
-
-	// Keep a copy of the original point slice so that it can be returned to the pool.
-	origMatrix := inputMatrix
-
-	biggestLen := len(inputMatrix)
 	enh := &EvalNodeHelper{}
-	seriess := make(map[uint64]Series, biggestLen) // Output series by series hash.
 	tempNumSamples := ev.currentSamples
 
 	// Create a mapping from input series to output groups.
@@ -1325,6 +1315,8 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
 		seriesToResult[si] = index
 	}
 
+	seriess := make(map[uint64]Series, len(inputMatrix)) // Output series by series hash.
+
 	for ts := ev.startTimestamp; ts <= ev.endTimestamp; ts += ev.interval {
 		if err := contextDone(ev.ctx, "expression evaluation"); err != nil {
 			ev.error(err)
@@ -1340,8 +1332,6 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
 
 		// If this could be an instant query, shortcut so as not to change sort order.
 		if ev.endTimestamp == ev.startTimestamp {
-			ev.currentSamples = originalNumSamples + result.TotalSamples()
-			ev.samplesStats.UpdatePeak(ev.currentSamples)
 			return result, warnings
 		}
 		if ev.currentSamples > ev.maxSamples {
@@ -1349,18 +1339,11 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
 		}
 	}
 
-	// Reuse the original point slice.
-	for _, s := range origMatrix {
-		putFPointSlice(s.Floats)
-		putHPointSlice(s.Histograms)
-	}
 	// Assemble the output matrix. By the time we get here we know we don't have too many samples.
 	mat := make(Matrix, 0, len(seriess))
 	for _, ss := range seriess {
 		mat = append(mat, ss)
 	}
-	ev.currentSamples = originalNumSamples + mat.TotalSamples()
-	ev.samplesStats.UpdatePeak(ev.currentSamples)
 	return mat, warnings
 }
 
@@ -1434,7 +1417,26 @@ func (ev *evaluator) eval(expr parser.Expr) (parser.Value, annotations.Annotatio
 			}, e.Expr)
 		}
 
-		return ev.rangeEvalAgg(e, sortedGrouping)
+		var warnings annotations.Annotations
+		originalNumSamples := ev.currentSamples
+		// param is the number k for topk/bottomk, or q for quantile.
+		var fParam float64
+		if param != nil {
+			val, ws := ev.eval(param)
+			warnings.Merge(ws)
+			fParam = val.(Matrix)[0].Floats[0].F
+		}
+		// Now fetch the data to be aggregated.
+		val, ws := ev.eval(e.Expr)
+		warnings.Merge(ws)
+		inputMatrix := val.(Matrix)
+
+		result, ws := ev.rangeEvalAgg(e, sortedGrouping, inputMatrix, fParam)
+		warnings.Merge(ws)
+		ev.currentSamples = originalNumSamples + result.TotalSamples()
+		ev.samplesStats.UpdatePeak(ev.currentSamples)
+
+		return result, warnings
 
 	case *parser.Call:
 		call := FunctionCalls[e.Func.Name]