diff --git a/test/ad.jl b/test/ad.jl
index f53dd9835..dcfe4ef46 100644
--- a/test/ad.jl
+++ b/test/ad.jl
@@ -237,34 +237,8 @@ end
     end
 end
 
-@testset verbose = true "AD / SamplingContext" begin
-    # AD tests for gradient-based samplers need to be run with SamplingContext
-    # because samplers can potentially use this to define custom behaviour in
-    # the tilde-pipeline and thus change the code executed during model
-    # evaluation.
-    @testset "adtype=$adtype" for adtype in ADTYPES
-        @testset "alg=$alg" for alg in [
-            HMC(0.1, 10; adtype=adtype),
-            HMCDA(0.8, 0.75; adtype=adtype),
-            NUTS(1000, 0.8; adtype=adtype),
-            SGHMC(; learning_rate=0.02, momentum_decay=0.5, adtype=adtype),
-            SGLD(; stepsize=PolynomialStepsize(0.25), adtype=adtype),
-        ]
-            @info "Testing AD for $alg"
-
-            @testset "model=$(model.f)" for model in DEMO_MODELS
-                rng = StableRNG(123)
-                spl_model = DynamicPPL.contextualize(
-                    model, DynamicPPL.SamplingContext(rng, DynamicPPL.Sampler(alg))
-                )
-                @test run_ad(spl_model, adtype; test=true, benchmark=false) isa Any
-            end
-        end
-    end
-end
-
 @testset verbose = true "AD / GibbsContext" begin
-    # Gibbs sampling also needs extra AD testing because the models are
+    # Gibbs sampling needs some extra AD testing because the models are
     # executed with GibbsContext and a subsetted varinfo. (see e.g.
     # `gibbs_initialstep_recursive` and `gibbs_step_recursive` in
     # src/mcmc/gibbs.jl -- the code here mimics what happens in those
@@ -283,10 +257,7 @@ end
                     model, varnames, deepcopy(global_vi)
                 )
                 rng = StableRNG(123)
-                spl_model = DynamicPPL.contextualize(
-                    model, DynamicPPL.SamplingContext(rng, DynamicPPL.Sampler(HMC(0.1, 10)))
-                )
-                @test run_ad(spl_model, adtype; test=true, benchmark=false) isa Any
+                @test run_ad(model, adtype; test=true, benchmark=false) isa Any
             end
         end
     end