diff --git a/test/mcmc/gibbs.jl b/test/mcmc/gibbs.jl
index fecb9adec..0ee75efb6 100644
--- a/test/mcmc/gibbs.jl
+++ b/test/mcmc/gibbs.jl
@@ -57,7 +57,8 @@ has_dot_assume(::DynamicPPL.Model) = true
             end
             s = sum(y) - sum(z)
             obs1 ~ Normal(s, 1)
-            return obs2 ~ Poisson(y[3])
+            obs2 ~ Poisson(y[3])
+            return obs1, obs2, variance, z, y, s
         end
 
         model = test_model(1.2, 2, 10, 2.5)
@@ -68,53 +69,45 @@ has_dot_assume(::DynamicPPL.Model) = true
                 n -> Combinatorics.combinations(all_varnames, n), 1:length(all_varnames)
             ),
         )
-        for typed in (true, false)
-            for target_vns in target_vn_combinations
-                global_varinfo =
-                    typed ? DynamicPPL.VarInfo(model) : DynamicPPL.untyped_varinfo(model)
-                target_vns = collect(target_vns)
-                local_varinfo = DynamicPPL.subset(global_varinfo, target_vns)
-                ctx = Turing.Inference.GibbsContext(
-                    target_vns, Ref(global_varinfo), Turing.DefaultContext()
-                )
-
-                # Check that the correct varnames are conditioned, and that getting their
-                # values is type stable when the varinfo is.
-                for k in keys(global_varinfo)
-                    is_target = any(
-                        Iterators.map(vn -> DynamicPPL.subsumes(vn, k), target_vns)
-                    )
-                    @test Turing.Inference.is_target_varname(ctx, k) == is_target
-                    if !is_target && typed
-                        @inferred Turing.Inference.get_conditioned_gibbs(ctx, k)
-                    end
-                end
+        for target_vns in target_vn_combinations
+            global_varinfo = DynamicPPL.VarInfo(model)
+            target_vns = collect(target_vns)
+            local_varinfo = DynamicPPL.subset(global_varinfo, target_vns)
+            ctx = Turing.Inference.GibbsContext(
+                target_vns, Ref(global_varinfo), Turing.DefaultContext()
+            )
 
-                # Check the type stability also in the dot_tilde pipeline.
-                for k in all_varnames
-                    # The map(identity, ...) part is there to concretise the eltype.
-                    subkeys = map(
-                        identity,
-                        filter(vn -> DynamicPPL.subsumes(k, vn), keys(global_varinfo)),
-                    )
-                    is_target = (k in target_vns)
-                    @test Turing.Inference.is_target_varname(ctx, subkeys) == is_target
-                    if !is_target && typed
-                        @inferred Turing.Inference.get_conditioned_gibbs(ctx, subkeys)
-                    end
+            # Check that the correct varnames are conditioned, and that getting their
+            # values is type stable when the varinfo is.
+            for k in keys(global_varinfo)
+                is_target = any(Iterators.map(vn -> DynamicPPL.subsumes(vn, k), target_vns))
+                @test Turing.Inference.is_target_varname(ctx, k) == is_target
+                if !is_target
+                    @inferred Turing.Inference.get_conditioned_gibbs(ctx, k)
                 end
+            end
 
-                # Check that evaluate!! and the result it returns are type stable.
-                conditioned_model = DynamicPPL.contextualize(model, ctx)
-                _, post_eval_varinfo = @inferred DynamicPPL.evaluate!!(
-                    conditioned_model, local_varinfo
+            # Check the type stability also in the dot_tilde pipeline.
+            for k in all_varnames
+                # The map(identity, ...) part is there to concretise the eltype.
+                subkeys = map(
+                    identity, filter(vn -> DynamicPPL.subsumes(k, vn), keys(global_varinfo))
                 )
-                if typed
-                    for k in keys(post_eval_varinfo)
-                        @inferred post_eval_varinfo[k]
-                    end
+                is_target = (k in target_vns)
+                @test Turing.Inference.is_target_varname(ctx, subkeys) == is_target
+                if !is_target
+                    @inferred Turing.Inference.get_conditioned_gibbs(ctx, subkeys)
                 end
             end
+
+            # Check that evaluate!! and the result it returns are type stable.
+            conditioned_model = DynamicPPL.contextualize(model, ctx)
+            _, post_eval_varinfo = @inferred DynamicPPL.evaluate!!(
+                conditioned_model, local_varinfo
+            )
+            for k in keys(post_eval_varinfo)
+                @inferred post_eval_varinfo[k]
+            end
         end
     end
 end