diff --git a/python/tvm/relax/backend/contrib/cublas.py b/python/tvm/relax/backend/contrib/cublas.py
index e5bc55c32751..faec63e1907a 100644
--- a/python/tvm/relax/backend/contrib/cublas.py
+++ b/python/tvm/relax/backend/contrib/cublas.py
@@ -21,6 +21,7 @@
 
 import tvm
 from tvm import DataType
+from tvm.arith import Analyzer
 from tvm.relax import transform
 from tvm.relax.transform import PatternCheckContext
 
@@ -119,6 +120,8 @@ def _check_matmul(context: PatternCheckContext) -> bool:
             # cuBLAS only supports bias vector
             return False
 
+    analyzer = Analyzer()
+
     # cuBLASLt does not seem to support batched GEMM with one of matrices having
     # one batch (with batch_stride 0). So for batched GEMM, the two batch counts
     # must be equal. If lhs is batched but rhs is not, we can use the regular GEMM by
@@ -126,7 +129,7 @@ def _check_matmul(context: PatternCheckContext) -> bool:
     return (
         isinstance(lhs_batches, tvm.tir.Var)
         or isinstance(rhs_batches, tvm.tir.Var)
-        or (int(lhs_batches) == int(rhs_batches))
+        or (analyzer.can_prove_equal(lhs_batches, rhs_batches))
         or (lhs_batches >= 1 and rhs_batches == 1)
     )
 
diff --git a/tests/python/relax/test_codegen_cublas.py b/tests/python/relax/test_codegen_cublas.py
index 4ff498ae2b93..1b19c891863f 100644
--- a/tests/python/relax/test_codegen_cublas.py
+++ b/tests/python/relax/test_codegen_cublas.py
@@ -149,6 +149,8 @@ def get_relax_matmul_dequantize_module(
         ((_vars["a"], 32, 8), (_vars["a"], 8, 10), True, "gelu"),
         # ND x ND
         ((5, 3, 32, 8), (5, 3, 8, 10), True, "relu"),
+        ((_vars["a"], 3, 32, 8), (_vars["a"], 3, 8, 10), True, "relu"),
+        ((_vars["a"], _vars["b"], 32, 8), (_vars["a"], _vars["b"], 8, 10), True, "relu"),
         # ND x 2D
         ((5, 3, 32, 8), (8, 10), False, "none"),
     ],