diff --git a/tests/DETEST/run.py b/tests/DETEST/run.py
index 8f3e9ca9c..0842f53aa 100644
--- a/tests/DETEST/run.py
+++ b/tests/DETEST/run.py
@@ -22,8 +22,8 @@ def __call__(self, t, y):
 def main():
 
     sol = dict()
-    for method in ['dopri5', 'adams']:
-        for tol in [1e-3, 1e-6, 1e-9]:
+    for method in ['dopri5', 'classic_dopri5']:
+        for tol in [1e-3, 1e-4, 1e-5, 1e-6]:
             print('======= {} | tol={:e} ======='.format(method, tol))
             nfes = []
             times = []
diff --git a/torchdiffeq/_impl/dopri5.py b/torchdiffeq/_impl/dopri5.py
index 1a925ef1d..bcc610632 100644
--- a/torchdiffeq/_impl/dopri5.py
+++ b/torchdiffeq/_impl/dopri5.py
@@ -1,18 +1,39 @@
 import torch
 from .rk_common import _ButcherTableau, RKAdaptiveStepsizeODESolver
 
+alpha=torch.tensor([1 / 5, 3 / 10, 4 / 5, 8 / 9, 1., 1.], dtype=torch.float64)
+beta=[
+    torch.tensor([1 / 5], dtype=torch.float64),
+    torch.tensor([3 / 40, 9 / 40], dtype=torch.float64),
+    torch.tensor([44 / 45, -56 / 15, 32 / 9], dtype=torch.float64),
+    torch.tensor([19372 / 6561, -25360 / 2187, 64448 / 6561, -212 / 729], dtype=torch.float64),
+    torch.tensor([9017 / 3168, -355 / 33, 46732 / 5247, 49 / 176, -5103 / 18656], dtype=torch.float64),
+    torch.tensor([35 / 384, 0, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84], dtype=torch.float64),
+]
+c_sol=torch.tensor([35 / 384, 0, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84, 0], dtype=torch.float64)
+
+
+_CLASSIC_DORMAND_PRINCE_SHAMPINE_TABLEAU = _ButcherTableau(
+    alpha=alpha, beta=beta, c_sol=c_sol,
+    c_error=torch.tensor([
+        35 / 384 - 	5179 / 57600,
+        0,
+        500 / 1113 - 7571 / 16695,
+        125 / 192 - 393 / 640,
+        -2187 / 6784 - -92097 / 339200,
+        11 / 84 - 187 / 2100,
+        -1 / 40,
+    ], dtype=torch.float64),
+)
+
+DPS_C_MID = torch.tensor([
+    6025192743 / 30085553152 / 2, 0, 51252292925 / 65400821598 / 2, -2691868925 / 45128329728 / 2,
+    187940372067 / 1594534317056 / 2, -1776094331 / 19743644256 / 2, 11237099 / 235043384 / 2
+], dtype=torch.float64)
+
 
 _DORMAND_PRINCE_SHAMPINE_TABLEAU = _ButcherTableau(
-    alpha=torch.tensor([1 / 5, 3 / 10, 4 / 5, 8 / 9, 1., 1.], dtype=torch.float64),
-    beta=[
-        torch.tensor([1 / 5], dtype=torch.float64),
-        torch.tensor([3 / 40, 9 / 40], dtype=torch.float64),
-        torch.tensor([44 / 45, -56 / 15, 32 / 9], dtype=torch.float64),
-        torch.tensor([19372 / 6561, -25360 / 2187, 64448 / 6561, -212 / 729], dtype=torch.float64),
-        torch.tensor([9017 / 3168, -355 / 33, 46732 / 5247, 49 / 176, -5103 / 18656], dtype=torch.float64),
-        torch.tensor([35 / 384, 0, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84], dtype=torch.float64),
-    ],
-    c_sol=torch.tensor([35 / 384, 0, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84, 0], dtype=torch.float64),
+    alpha=alpha, beta=beta, c_sol=c_sol,
     c_error=torch.tensor([
         35 / 384 - 1951 / 21600,
         0,
@@ -24,13 +45,16 @@
     ], dtype=torch.float64),
 )
 
-DPS_C_MID = torch.tensor([
-    6025192743 / 30085553152 / 2, 0, 51252292925 / 65400821598 / 2, -2691868925 / 45128329728 / 2,
-    187940372067 / 1594534317056 / 2, -1776094331 / 19743644256 / 2, 11237099 / 235043384 / 2
-], dtype=torch.float64)
-
 
 class Dopri5Solver(RKAdaptiveStepsizeODESolver):
     order = 5
     tableau = _DORMAND_PRINCE_SHAMPINE_TABLEAU
     mid = DPS_C_MID
+
+    
+class ClassicDopri5Solver(RKAdaptiveStepsizeODESolver):
+    order = 5
+    tableau = _CLASSIC_DORMAND_PRINCE_SHAMPINE_TABLEAU
+    mid = DPS_C_MID
+    
+    
diff --git a/torchdiffeq/_impl/odeint.py b/torchdiffeq/_impl/odeint.py
index a174219ad..2b47eaf48 100644
--- a/torchdiffeq/_impl/odeint.py
+++ b/torchdiffeq/_impl/odeint.py
@@ -1,6 +1,6 @@
 import torch
 from torch.autograd.functional import vjp
-from .dopri5 import Dopri5Solver
+from .dopri5 import ClassicDopri5Solver, Dopri5Solver
 from .bosh3 import Bosh3Solver
 from .adaptive_heun import AdaptiveHeunSolver
 from .fehlberg2 import Fehlberg2
@@ -13,6 +13,7 @@
 SOLVERS = {
     'dopri8': Dopri8Solver,
     'dopri5': Dopri5Solver,
+    'classic_dopri5': ClassicDopri5Solver,
     'bosh3': Bosh3Solver,
     'fehlberg2': Fehlberg2,
     'adaptive_heun': AdaptiveHeunSolver,