diff --git a/.gitignore b/.gitignore
index 1a7eb04..3ea6f07 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,3 +5,6 @@ Manifest.toml
 *.sublime-workspace
 *.bat
 *.sublime-project
+*.mof.json
+*.prt
+*.txt
diff --git a/Project.toml b/Project.toml
index 6cf9a57..cc6a143 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,21 +1,13 @@
 name = "QuadraticToBinary"
 uuid = "014a38d5-7acb-4e20-b6c0-4fe5c2344fd1"
 authors = ["Joaquim Garcia <joaquimgarcia@psr-inc.com>"]
-version = "0.3.0"
+version = "0.4.0"
 
 [deps]
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 
 [compat]
-MathOptInterface = "0.10.6"
+MathOptInterface = "1"
 DataStructures = "0.17.10, 0.18.0"
-julia = "1"
-
-[extras]
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
-
-[targets]
-test = ["Test", "Cbc"]
-
+julia = "1.6"
diff --git a/README.md b/README.md
index 7d26b97..3adaa9b 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ Non-convex quadratic programs are extremely hard to solve. This problem class ca
 be solved by Global Solvers such as [Couenne](https://projects.coin-or.org/Couenne).
 Another possibility is to rely on binary expasion of products terms that appear in the
 problem, in this case the problem is *approximated* and can be solved by off-the-shelf
-MIP solvers such as [Cbc](https://github.com/JuliaOpt/Cbc.jl), [CPLEX](https://github.com/JuliaOpt/CPLEX.jl), [GLPK](https://github.com/JuliaOpt/GLPK.jl), [Gurobi](https://github.com/JuliaOpt/Gurobi.jl), [Xpress](https://github.com/JuliaOpt/Xpress.jl).
+MIP solvers such as [Cbc](https://github.com/jump-dev/Cbc.jl), [CPLEX](https://github.com/jump-dev/CPLEX.jl), [GLPK](https://github.com/jump-dev/GLPK.jl), [Gurobi](https://github.com/jump-dev/Gurobi.jl), [HiGHS](https://github.com/jump-dev/HiGHS.jl), [Xpress](https://github.com/jump-dev/Xpress.jl).
 
 ## Example
 
@@ -77,15 +77,15 @@ objective_value(model) # ≈ 9.0
 @assert value(c) ≈ 4.0
 ```
 
-### MathOptInterface with Cbc solver
+### MathOptInterface with HiGHS solver
 
 ```julia
 using MathOptInterface
 using QuadraticToBinary
 const MOI = MathOptInterface
-using Cbc
+using HiGHS
 
-optimizer = MOI.instantiate(Cbc.Optimizer, with_bridge_type = Float64)
+optimizer = MOI.instantiate(HiGHS.Optimizer, with_bridge_type = Float64)
 
 model = QuadraticToBinary.Optimizer{Float64}(optimizer)
 
@@ -118,7 +118,8 @@ MOI.optimize!(model)
 @assert MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 4.0
 ```
 
-Note that duals are not available because the problem was approximated as a MIP.
+Note:
+that duals are not available because the problem was approximated as a MIP.
 
 ## QuadraticToBinary.Optimizer Attributes
 
@@ -140,6 +141,16 @@ MOI.set(model, QuadraticToBinary.VariablePrecision(), vi, val)
 The precision for each varible will be `val * (UB - LB)`. Where `UB` and `LB` are,
 respectively, the upper and lower bound of the variable.
 
+Note:
+binary expansion problem can be numerically challenging for high precision. You
+might need to modify solver options accordingly. In the case of HiGHS:
+
+```julia
+tol = 1e-9
+MOI.set(highs_high_tol, MOI.RawOptimizerAttribute("mip_feasibility_tolerance"), tol)
+MOI.set(highs_high_tol, MOI.RawOptimizerAttribute("primal_feasibility_tolerance"), tol)
+```
+
 ### Bounds
 
 For the sake of simplicity, the following two attributes are made available:
diff --git a/src/moi.jl b/src/moi.jl
index 9357eba..3107620 100644
--- a/src/moi.jl
+++ b/src/moi.jl
@@ -229,7 +229,7 @@ end
 
 # MOI.supports(model::Optimizer, args...) = MOI.supports(model.optimizer, args...)
 
-# TODO, call this on inner solver
+# Q2B is always incremental because it uses add_constraint to cacehc onversions
 MOI.supports_incremental_interface(::Optimizer) = true
 
 function MOI.supports(model::Optimizer, attr::MOI.VariableName, tp::Type{MOI.VariableIndex})
@@ -270,6 +270,7 @@ function MOI.set(model::Optimizer, attr::Union{
     MOI.Silent,
     MOI.NumberOfThreads,
     MOI.TimeLimitSec,
+    MOI.RawOptimizerAttribute,
 }, val
 )
     return MOI.set(model.optimizer, attr, val)
@@ -307,8 +308,8 @@ quadratic_type(::Type{MOI.VectorAffineFunction{T}}) where T = MOI.VectorQuadrati
 
 
 function MOI.get(model::Optimizer, ::MOI.SolverName)
-    return "Binary reformulation of quadratic model with solver " *
-        MOI.get(model.optimizer, MOI.SolverName()) * " attached"
+    return "QuadraticToBinary [" *
+        MOI.get(model.optimizer, MOI.SolverName()) * "]"
 end
 
 function MOI.supports_add_constrained_variables(
@@ -340,20 +341,8 @@ function MOI.supports_add_constrained_variables(
     return MOI.supports_add_constrained_variables(model.optimizer, S)
 end
 
-# function MOI.set(model::Optimizer, param::MOI.RawOptimizerAttribute, value)
-#     # if in a subset of the q2b save it
-#     # otherwise pass it forward
-# end
-
-# function MOI.get(model::Optimizer, param::MOI.RawOptimizerAttribute)
-# end
-
-function MOI.Utilities.supports_default_copy_to(model::Optimizer, val::Bool)
-    return true # val # MOI.Utilities.supports_default_copy_to(model.optimizer, val)
-end
-
-function MOI.copy_to(dest::Optimizer, src::MOI.ModelLike; kwargs...)
-    return MOI.Utilities.automatic_copy_to(dest, src; kwargs...)
+function MOI.copy_to(dest::Optimizer, src::MOI.ModelLike)
+    return MOI.Utilities.default_copy_to(dest, src)
 end
 
 
@@ -989,25 +978,14 @@ end
 
 function delete_additional_constraints(model)
     ch = model.index_cache
-    if true
-        MOI.delete(model.optimizer, ch.sa_eq)
-        MOI.delete(model.optimizer, ch.sa_lt)
-        MOI.delete(model.optimizer, ch.sa_gt)
-        MOI.delete(model.optimizer, ch.vi)
-    else
-        for ci in ch.sa_eq
-            MOI.delete(model.optimizer, ci)
-        end
-        for ci in ch.sa_lt
-            MOI.delete(model.optimizer, ci)
-        end
-        for ci in ch.sa_gt
-            MOI.delete(model.optimizer, ci)
-        end
-        for vi in ch.vi
-            MOI.delete(model.optimizer, vi)
-        end
-    end
+    MOI.delete(model.optimizer, ch.sa_eq)
+    MOI.delete(model.optimizer, ch.sa_lt)
+    MOI.delete(model.optimizer, ch.sa_gt)
+    MOI.delete(model.optimizer, ch.sv_zo)
+    MOI.delete(model.optimizer, ch.sv_lt)
+    MOI.delete(model.optimizer, ch.sv_gt)
+    MOI.delete(model.optimizer, ch.vi)
+    return nothing
 end
 
 function lower(model, info)
@@ -1481,7 +1459,8 @@ function MOI.set(
     ::GlobalVariablePrecision,
     value::Union{Nothing, Float64}
 )
-    val = value === nothing ? NaN : value
+    # default global precision is 1e-4
+    val = value === nothing ? 1e-4 : value
     @assert 0 < val <= 1
     model.global_initial_precision = val
     return
diff --git a/test/Project.toml b/test/Project.toml
new file mode 100644
index 0000000..54bbab4
--- /dev/null
+++ b/test/Project.toml
@@ -0,0 +1,7 @@
+[deps]
+HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
+MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[compat]
+HiGHS = "1.1.0"
\ No newline at end of file
diff --git a/test/moi.jl b/test/moi.jl
index 2728218..c11044d 100644
--- a/test/moi.jl
+++ b/test/moi.jl
@@ -1,12 +1,11 @@
 
-function ncqcp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
+function ncqcp1test_mod(model::MOI.ModelLike, config)
     atol = config.atol
     rtol = config.rtol
     # Max 2x + y
     # s.t. x * y <= 4 (c)
     #      x, y >= 1
 
-    # @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     @test MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}())
     @test MOI.supports_constraint(model, MOI.ScalarQuadraticFunction{Float64}, MOI.LessThan{Float64})
 
@@ -35,38 +34,34 @@ function ncqcp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
     MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
     @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE
 
-    if config.query
-        @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c)
-    end
+    @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c)
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ 9.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ 9.0 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.VariablePrimal(), x) ≈ 4.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.VariablePrimal(), y) ≈ 1.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), x) ≈ 4.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), y) ≈ 1.0 atol=atol rtol=rtol
+
+    @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 4.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 4.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 1.0 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 4.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 4.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 1.0 atol=atol rtol=rtol
-    end
 end
 
-function ncqcp1test_mod2(model::MOI.ModelLike, config::MOIT.Config)
+function ncqcp1test_mod2(model::MOI.ModelLike, config)
     atol = config.atol
     rtol = config.rtol
     # Max 2x + y
     # s.t. x * y <= 4 (c)
     #      x, y >= 1
 
-    # @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     @test MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}())
     @test MOI.supports_constraint(model, MOI.ScalarQuadraticFunction{Float64}, MOI.LessThan{Float64})
 
@@ -102,31 +97,28 @@ function ncqcp1test_mod2(model::MOI.ModelLike, config::MOIT.Config)
     MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
     @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE
 
-    if config.query
-        @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c)
-    end
+    @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c)
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ 9.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ 9.0 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.VariablePrimal(), x) ≈ 4.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.VariablePrimal(), y) ≈ 1.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), x) ≈ 4.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), y) ≈ 1.0 atol=atol rtol=rtol
+
+    @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 4.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 4.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 1.0 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 4.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 4.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 1.0 atol=atol rtol=rtol
-    end
 end
 
-function ncqcp2test_mod(model::MOI.ModelLike, config::MOIT.Config)
+function ncqcp2test_mod(model::MOI.ModelLike, config)
     atol = config.atol
     rtol = config.rtol
     # Find x, y
@@ -134,7 +126,6 @@ function ncqcp2test_mod(model::MOI.ModelLike, config::MOIT.Config)
     #      x * x == 4 (c2)
     #      x, y >= 0
 
-    # @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     @test MOI.supports_constraint(model, MOI.ScalarQuadraticFunction{Float64}, MOI.EqualTo{Float64})
 
     MOI.empty!(model)
@@ -164,31 +155,28 @@ function ncqcp2test_mod(model::MOI.ModelLike, config::MOIT.Config)
 
     @test MOI.get(model, MOI.NumberOfConstraints{MOI.ScalarQuadraticFunction{Float64}, MOI.EqualTo{Float64}}()) == 2
 
-    if config.query
-        @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c)
-        @test cf2 ≈ MOI.get(model, MOI.ConstraintFunction(), c2)
-    end
+    @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c)
+    @test cf2 ≈ MOI.get(model, MOI.ConstraintFunction(), c2)
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.VariablePrimal(), x) ≈ 2.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.VariablePrimal(), y) ≈ 2.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), x) ≈ 2.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), y) ≈ 2.0 atol=atol rtol=rtol
+
+    @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 4.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), c2) ≈ 4.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 2.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 2.0 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 4.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), c2) ≈ 4.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 2.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 2.0 atol=atol rtol=rtol
-    end
 end
 
-function qp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
+function qp1test_mod(model::MOI.ModelLike, config)
     atol = config.atol
     rtol = config.rtol
     # homogeneous quadratic objective
@@ -197,7 +185,6 @@ function qp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
     #     x +  y      >= 1 (c2)
     #     x, y, z \in R
 
-    @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}())
     MOI.supports_constraint(model, MOI.ScalarAffineFunction{Float64}, MOI.GreaterThan{Float64})
 
@@ -237,40 +224,36 @@ function qp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
     obj = 1.0 * x * x + 1.0 * x * y + 1.0 * y * y + 1.0 * y * z + 1.0 * z * z
     MOI.set(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}(), obj)
 
-    if config.query
-        @test obj ≈ MOI.get(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}())
+    @test obj ≈ MOI.get(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}())
 
-        @test cf1 ≈ MOI.get(model, MOI.ConstraintFunction(), c1)
+    @test cf1 ≈ MOI.get(model, MOI.ConstraintFunction(), c1)
 
-        @test MOI.GreaterThan(4.0) == MOI.get(model, MOI.ConstraintSet(), c1)
-    end
+    @test MOI.GreaterThan(4.0) == MOI.get(model, MOI.ConstraintSet(), c1)
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ 13/7 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.VariablePrimal(), v) ≈ [4/7, 3/7, 6/7] atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ 13/7 atol=atol rtol=rtol
+
+    @test MOI.get(model, MOI.VariablePrimal(), v) ≈ [4/7, 3/7, 6/7] atol=atol rtol=rtol
+
+    # @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+    # # The dual constraint gives
+    # # [λ_c1 + λ_c2, 2λ_c1 + λ_c2, 3λ_c1] = [2 1 0; 1 2 1; 0 1 2] * x
+    # #                                    = [11, 16, 15] / 7
+    # # hence λ_c1 = 5/7 and λ_c2 = 6/7.
+    # @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 5/7 atol=atol rtol=rtol
+    # @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ 6/7 atol=atol rtol=rtol
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-            # The dual constraint gives
-            # [λ_c1 + λ_c2, 2λ_c1 + λ_c2, 3λ_c1] = [2 1 0; 1 2 1; 0 1 2] * x
-            #                                    = [11, 16, 15] / 7
-            # hence λ_c1 = 5/7 and λ_c2 = 6/7.
-            @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 5/7 atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ 6/7 atol=atol rtol=rtol
-        end
-    end
 end
 
-function qp2test_mod(model::MOI.ModelLike, config::MOIT.Config)
+function qp2test_mod(model::MOI.ModelLike, config)
     atol = config.atol
     rtol = config.rtol
     # Same as `qp1` but with duplicate terms then change the objective and sense
@@ -280,7 +263,6 @@ function qp2test_mod(model::MOI.ModelLike, config::MOIT.Config)
     #     x +  y      >= 1 (c2)
     #     x, y, z \in R
 
-    @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     @test MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}())
     @test MOI.supports_constraint(model, MOI.ScalarAffineFunction{Float64}, MOI.GreaterThan{Float64})
 
@@ -322,33 +304,30 @@ function qp2test_mod(model::MOI.ModelLike, config::MOIT.Config)
         0.0)
     MOI.set(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}(), obj)
 
-    if config.query
-        @test obj ≈ MOI.get(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}())
+    @test obj ≈ MOI.get(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}())
 
-        @test c1f ≈ MOI.get(model, MOI.ConstraintFunction(), c1)
+    @test c1f ≈ MOI.get(model, MOI.ConstraintFunction(), c1)
 
-        @test MOI.GreaterThan(4.0) == MOI.get(model, MOI.ConstraintSet(), c1)
-    end
+    @test MOI.GreaterThan(4.0) == MOI.get(model, MOI.ConstraintSet(), c1)
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ 13/7 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.VariablePrimal(), v) ≈ [4/7,3/7,6/7] atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ 13/7 atol=atol rtol=rtol
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-            @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 5/7 atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ 6/7 atol=atol rtol=rtol
-        end
-    end
+    @test MOI.get(model, MOI.VariablePrimal(), v) ≈ [4/7,3/7,6/7] atol=atol rtol=rtol
+
+    # if config.duals
+    #     @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+    #     @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 5/7 atol=atol rtol=rtol
+    #     @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ 6/7 atol=atol rtol=rtol
+    # end
 
     # change objective to Max -2(x^2 + xy + y^2 + yz + z^2)
     # First clear the objective, this is needed if a `Bridges.Objective.SlackBridge` is used.
@@ -364,30 +343,25 @@ function qp2test_mod(model::MOI.ModelLike, config::MOIT.Config)
         0.0)
     MOI.set(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}(), obj2)
 
-    if config.query
-        @test obj2 ≈ MOI.get(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}())
-    end
+    @test obj2 ≈ MOI.get(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}())
 
-    if config.solve
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ -2*13/7 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ -2*13/7 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.VariablePrimal(), v) ≈ [4/7, 3/7, 6/7] atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), v) ≈ [4/7, 3/7, 6/7] atol=atol rtol=rtol
+
+    # @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+    # @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 10/7 atol=atol rtol=rtol
+    # @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ 12/7 atol=atol rtol=rtol
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-            @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 10/7 atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ 12/7 atol=atol rtol=rtol
-        end
-    end
 end
 
-function qp3test_mod(model::MOI.ModelLike, config::MOIT.Config)
+function qp3test_mod(model::MOI.ModelLike, config)
     atol = config.atol
     rtol = config.rtol
     # non-homogeneous quadratic objective
@@ -395,7 +369,6 @@ function qp3test_mod(model::MOI.ModelLike, config::MOIT.Config)
     #       s.t.  x, y >= 0
     #             x + y = 1
 
-    @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     @test MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}())
     MOI.supports_constraint(model, MOI.VariableIndex, MOI.GreaterThan{Float64})
     MOI.supports_constraint(model, MOI.ScalarAffineFunction{Float64}, MOI.EqualTo{Float64})
@@ -434,31 +407,31 @@ function qp3test_mod(model::MOI.ModelLike, config::MOIT.Config)
     )
     MOI.set(model, MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}(), obj)
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    h = MOI.get(model.optimizer, MOI.RawSolver())
+    HiGHS.Highs_writeOptions(h, "options.txt")
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ 2.875 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.VariablePrimal(), [x, y]) ≈ [0.25, 0.75] atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 0.25 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 0.75 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-            # The dual constraint gives [λ_vc1 + λ_c1, λ_vc2 + λ_c1] = [4 1; 1 2] * x + [1, 1] = [11, 11] / 4
-            # since `vc1` and `vc2` are not active, `λ_vc1` and `λ_vc2` are
-            # zero so `λ_c1 = 11/4`.
-            @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 11 / 4 atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), vc1) ≈ 0 atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), vc2) ≈ 0 atol=atol rtol=rtol
-        end
-    end
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ 2.875 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), [x, y]) ≈ [0.25, 0.75] atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 0.25 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 0.75 atol=atol rtol=rtol
+
+    # @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+    # # The dual constraint gives [λ_vc1 + λ_c1, λ_vc2 + λ_c1] = [4 1; 1 2] * x + [1, 1] = [11, 11] / 4
+    # # since `vc1` and `vc2` are not active, `λ_vc1` and `λ_vc2` are
+    # # zero so `λ_c1 = 11/4`.
+    # @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 11 / 4 atol=atol rtol=rtol
+    # @test MOI.get(model, MOI.ConstraintDual(), vc1) ≈ 0 atol=atol rtol=rtol
+    # @test MOI.get(model, MOI.ConstraintDual(), vc2) ≈ 0 atol=atol rtol=rtol
 
+    #=
     # change back to linear
     #        max 2x + y + 1
     #       s.t.  x, y >= 0
@@ -470,28 +443,25 @@ function qp3test_mod(model::MOI.ModelLike, config::MOIT.Config)
     objf = MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([2.0, 1.0], [x, y]), 1.0)
     MOI.set(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(), objf)
 
-    if config.solve
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ 3.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.VariablePrimal(), [x,y]) ≈ [1.0, 0.0] atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 1.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 0.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ 3.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), [x,y]) ≈ [1.0, 0.0] atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc1) ≈ 1.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc2) ≈ 0.0 atol=atol rtol=rtol
+=#
+    # @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+    # @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ -2 atol=atol rtol=rtol
+    # @test MOI.get(model, MOI.ConstraintDual(), vc1) ≈ 0 atol=atol rtol=rtol
+    # @test MOI.get(model, MOI.ConstraintDual(), vc2) ≈ 1 atol=atol rtol=rtol
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-            @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ -2 atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), vc1) ≈ 0 atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), vc2) ≈ 1 atol=atol rtol=rtol
-        end
-    end
 end
 
-function qcp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
+function qcp1test_mod(model::MOI.ModelLike, config)
     atol = config.atol
     rtol = config.rtol
     # quadratic constraint
@@ -502,7 +472,6 @@ function qcp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
     # Optimal solution
     # x = 1/2, y = 7/4
 
-    @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     @test MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}())
     @test MOI.supports_constraint(model, MOI.VectorAffineFunction{Float64}, MOI.Nonnegatives)
     @test MOI.supports_constraint(model, MOI.ScalarQuadraticFunction{Float64}, MOI.LessThan{Float64})
@@ -546,37 +515,31 @@ function qcp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
     MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
     @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE
 
-    if config.query
-        @test c2f ≈ MOI.get(model, MOI.ConstraintFunction(), c2) atol=atol rtol=rtol
-    end
+    @test c2f ≈ MOI.get(model, MOI.ConstraintFunction(), c2) atol=atol rtol=rtol
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ 2.25 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ 2.25 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.VariablePrimal(), [x, y]) ≈ [0.5, 1.75] atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), [x, y]) ≈ [0.5, 1.75] atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.ConstraintPrimal(), c1) ≈ [5/4, 9/4] atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), c2) ≈ 2 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), c1) ≈ [5/4, 9/4] atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), c2) ≈ 2 atol=atol rtol=rtol
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-            # The dual constraint gives
-            # [-1, -1] = [-λ_c1[1] + λ_c1[2], λ_c1[1] + λ_c1[2]] + λ_c2 * ([2 0; 0 0] * x + [0, 1])
-            #          = [-λ_c1[1] + λ_c1[2] + λ_c2, λ_c1[1] + λ_c1[2] + λ_c2]
-            # By complementary slackness, we have `λ_c1 = [0, 0]` so
-            # `λ_c2 = -1`.
-            @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ [0, 0] atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ -1 atol=atol rtol=rtol
-        end
-    end
+    # @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+    # # The dual constraint gives
+    # # [-1, -1] = [-λ_c1[1] + λ_c1[2], λ_c1[1] + λ_c1[2]] + λ_c2 * ([2 0; 0 0] * x + [0, 1])
+    # #          = [-λ_c1[1] + λ_c1[2] + λ_c2, λ_c1[1] + λ_c1[2] + λ_c2]
+    # # By complementary slackness, we have `λ_c1 = [0, 0]` so
+    # # `λ_c2 = -1`.
+    # @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ [0, 0] atol=atol rtol=rtol
+    # @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ -1 atol=atol rtol=rtol
 
     # try delete quadratic constraint and go back to linear
 
@@ -585,20 +548,19 @@ function qcp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
     #
     # MOI.optimize!(model)
     #
-    # @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    # @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
     #
     # @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
     #
     # @test MOI.get(model, MOI.ObjectiveValue()) ≈ 0.0 atol=atol rtol=rtol
 end
 
-function qcp2test_mod(model::MOI.ModelLike, config::MOIT.Config)
+function qcp2test_mod(model::MOI.ModelLike, config)
     atol = config.atol
     rtol = config.rtol
     # Max x
     # s.t. x^2 <= 2 (c)
 
-    @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     @test MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}())
     @test MOI.supports_constraint(model, MOI.ScalarQuadraticFunction{Float64},MOI.LessThan{Float64})
 
@@ -631,42 +593,36 @@ function qcp2test_mod(model::MOI.ModelLike, config::MOIT.Config)
     MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
     @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE
 
-    if config.query
-        @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c) atol=atol rtol=rtol
-    end
+    @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c) atol=atol rtol=rtol
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ √2 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ √2 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.VariablePrimal(), x) ≈ √2 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), x) ≈ √2 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 2 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 2 atol=atol rtol=rtol
+
+    # @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+    # # The dual constraint gives
+    # # -1 = λ * (2 * x) = λ * 2√2
+    # # hence λ = -1/(2√2).
+    # @test MOI.get(model, MOI.ConstraintDual(), c) ≈ -1 / (2 * √2) atol=atol rtol=rtol
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-            # The dual constraint gives
-            # -1 = λ * (2 * x) = λ * 2√2
-            # hence λ = -1/(2√2).
-            @test MOI.get(model, MOI.ConstraintDual(), c) ≈ -1 / (2 * √2) atol=atol rtol=rtol
-        end
-    end
 end
 
-function qcp3test_mod(model::MOI.ModelLike, config::MOIT.Config)
+function qcp3test_mod(model::MOI.ModelLike, config)
     atol = config.atol
     rtol = config.rtol
     # Min -x
     # s.t. x^2 <= 2
 
-    @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     @test MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}())
     @test MOI.supports_constraint(model, MOI.ScalarQuadraticFunction{Float64}, MOI.LessThan{Float64})
 
@@ -700,47 +656,39 @@ function qcp3test_mod(model::MOI.ModelLike, config::MOIT.Config)
     MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
     @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MIN_SENSE
 
-    if config.query
-        @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c) atol=atol rtol=rtol
-    end
+    @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c) atol=atol rtol=rtol
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-        end
+    # @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ -√2 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ -√2 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.VariablePrimal(), x) ≈ √2 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), x) ≈ √2 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 2 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ 2 atol=atol rtol=rtol
+
+    # @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+    # # The dual constraint gives
+    # # -1 = λ * (2 * x) = λ * 2√2
+    # # hence λ = -1/(2√2).
+    # @test MOI.get(model, MOI.ConstraintDual(), c) ≈ -1 / (2 * √2) atol=atol rtol=rtol
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-            # The dual constraint gives
-            # -1 = λ * (2 * x) = λ * 2√2
-            # hence λ = -1/(2√2).
-            @test MOI.get(model, MOI.ConstraintDual(), c) ≈ -1 / (2 * √2) atol=atol rtol=rtol
-        end
-    end
 end
 
-function _qcp4test_mod(model::MOI.ModelLike, config::MOIT.Config, less_than::Bool)
+function _qcp4test_mod(model::MOI.ModelLike, config, less_than::Bool)
     atol = config.atol
     rtol = config.rtol
     # Max  x
     # s.t. x^2 + x * y + y^2 <= 3 (c)
     #      y == 1
 
-    @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     @test MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}())
     quad_set = less_than ? MOI.LessThan(3.0) : MOI.GreaterThan(-3.0)
     @test MOI.supports_constraint(model, MOI.ScalarQuadraticFunction{Float64}, typeof(quad_set))
@@ -782,45 +730,40 @@ function _qcp4test_mod(model::MOI.ModelLike, config::MOIT.Config, less_than::Boo
     MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
     @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE
 
-    if config.query
-        @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c) atol=atol rtol=rtol
-        @test quad_set == MOI.get(model, MOI.ConstraintSet(), c)
-    end
+    @test cf ≈ MOI.get(model, MOI.ConstraintFunction(), c) atol=atol rtol=rtol
+    @test quad_set == MOI.get(model, MOI.ConstraintSet(), c)
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ 1.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ 1.0 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.VariablePrimal(), x) ≈ 1.0 atol=atol rtol=rtol
-        @test MOI.get(model, MOI.VariablePrimal(), y) ≈ 1.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), x) ≈ 1.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), y) ≈ 1.0 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ MOI.constant(quad_set) atol=atol rtol=rtol
-        @test MOI.get(model, MOI.ConstraintPrimal(), vc) ≈ 1.0 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), c) ≈ MOI.constant(quad_set) atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ConstraintPrimal(), vc) ≈ 1.0 atol=atol rtol=rtol
+
+    # @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+    # # The dual constraint gives
+    # # [-1, 0] = λ_c * [2 1; 1 2] * x + [0, λ_vc]
+    # #         = [3λ_c, 3λ_c + λ_vc]
+    # # hence `λ_c = -1/3` and `λ_vc = 1`.
+    # λ_c = less_than ? -1/3 : 1/3
+    # @test MOI.get(model, MOI.ConstraintDual(), c) ≈ λ_c atol=atol rtol=rtol
+    # @test MOI.get(model, MOI.ConstraintDual(), vc) ≈ 1 atol=atol rtol=rtol
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-            # The dual constraint gives
-            # [-1, 0] = λ_c * [2 1; 1 2] * x + [0, λ_vc]
-            #         = [3λ_c, 3λ_c + λ_vc]
-            # hence `λ_c = -1/3` and `λ_vc = 1`.
-            λ_c = less_than ? -1/3 : 1/3
-            @test MOI.get(model, MOI.ConstraintDual(), c) ≈ λ_c atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), vc) ≈ 1 atol=atol rtol=rtol
-        end
-    end
 end
 
-qcp4test_mod(model::MOI.ModelLike, config::MOIT.Config) = _qcp4test_mod(model, config, true)
-qcp5test_mod(model::MOI.ModelLike, config::MOIT.Config) = _qcp4test_mod(model, config, false)
+qcp4test_mod(model::MOI.ModelLike, config) = _qcp4test_mod(model, config, true)
+qcp5test_mod(model::MOI.ModelLike, config) = _qcp4test_mod(model, config, false)
 
-function socp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
+function socp1test_mod(model::MOI.ModelLike, config)
     atol = config.atol
     rtol = config.rtol
     # min t
@@ -828,7 +771,6 @@ function socp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
     #      x^2 + y^2 <= t^2 (c2)
     #      t >= 0 (bound)
 
-    @test MOIU.supports_default_copy_to(model, #=copy_names=# false)
     @test MOI.supports(model, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}())
     @test MOI.supports_constraint(model, MOI.ScalarQuadraticFunction{Float64}, MOI.LessThan{Float64})
     @test MOI.supports_constraint(model, MOI.ScalarAffineFunction{Float64}, MOI.GreaterThan{Float64})
@@ -877,37 +819,32 @@ function socp1test_mod(model::MOI.ModelLike, config::MOIT.Config)
     MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
     @test MOI.get(model, MOI.ObjectiveSense()) == MOI.MIN_SENSE
 
-    if config.query
-        @test c1f ≈ MOI.get(model, MOI.ConstraintFunction(), c1)
+    @test c1f ≈ MOI.get(model, MOI.ConstraintFunction(), c1)
 
-        @test c2f ≈ MOI.get(model, MOI.ConstraintFunction(), c2)
-    end
+    @test c2f ≈ MOI.get(model, MOI.ConstraintFunction(), c2)
 
-    if config.solve
-        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
 
-        MOI.optimize!(model)
+    MOI.optimize!(model)
 
-        @test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
 
-        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+    @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
 
-        @test MOI.get(model, MOI.ObjectiveValue()) ≈ 1/√2 atol=atol rtol=rtol
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ 1/√2 atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.VariablePrimal(), [x,y,t]) ≈ [0.5,0.5,1/√2] atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), [x,y,t]) ≈ [0.5,0.5,1/√2] atol=atol rtol=rtol
 
-        @test MOI.get(model, MOI.VariablePrimal(), [t,x,y,t]) ≈ [1/√2,0.5,0.5,1/√2] atol=atol rtol=rtol
+    @test MOI.get(model, MOI.VariablePrimal(), [t,x,y,t]) ≈ [1/√2,0.5,0.5,1/√2] atol=atol rtol=rtol
+
+    # @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+    # # The dual constraint gives
+    # # [1, 0, 0] = [λ_bound, λ_c1, λ_c1] + λ_c2 * [-2 0 0; 0 2 0; 0 0 2] * x
+    # #           = [λ_bound - √2*λ_c2, λ_c1 + λ_c2, λ_c1 + λ_c2]
+    # # and since `bound` is not active, `λ_bound = 0`.
+    # # This gives `λ_c2 = -1/√2` and `λ_c1 = 1/√2`.
+    # @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 1/√2 atol=atol rtol=rtol
+    # @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ -1/√2 atol=atol rtol=rtol
+    # @test MOI.get(model, MOI.ConstraintDual(), bound) ≈ 0 atol=atol rtol=rtol
 
-        if config.duals
-            @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
-            # The dual constraint gives
-            # [1, 0, 0] = [λ_bound, λ_c1, λ_c1] + λ_c2 * [-2 0 0; 0 2 0; 0 0 2] * x
-            #           = [λ_bound - √2*λ_c2, λ_c1 + λ_c2, λ_c1 + λ_c2]
-            # and since `bound` is not active, `λ_bound = 0`.
-            # This gives `λ_c2 = -1/√2` and `λ_c1 = 1/√2`.
-            @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 1/√2 atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ -1/√2 atol=atol rtol=rtol
-            @test MOI.get(model, MOI.ConstraintDual(), bound) ≈ 0 atol=atol rtol=rtol
-        end
-    end
 end
\ No newline at end of file
diff --git a/test/runtests.jl b/test/runtests.jl
index f4e6a21..319970c 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -2,30 +2,31 @@ using QuadraticToBinary, Test, MathOptInterface
 
 const MOI = MathOptInterface
 const MOIU = MOI.Utilities
-const MOIT = MOI.DeprecatedTest
 const MOIB = MOI.Bridges
 
 optimizers = []
+optimizers_high_tol = []
 # include("solvers/gurobi.jl")
-include("solvers/cbc.jl")
+# include("solvers/xpress.jl")
+# include("solvers/cbc.jl")
+include("solvers/highs.jl")
+
+@assert length(optimizers) > 0
+@assert length(optimizers_high_tol) > 0
 
 include("moi.jl")
 
-const CONFIG_LOW_TOL = MOIT.Config{Float64}(atol = 1e-3, rtol = 1e-2, duals = false, infeas_certificates = false)
-const CONFIG_VERY_LOW_TOL = MOIT.Config{Float64}(atol = 5e-3, rtol = 5e-2, duals = false, infeas_certificates = false)
-const CONFIG = MOIT.Config{Float64}(duals = false, infeas_certificates = false)
+const CONFIG_LOW_TOL = (atol = 1e-3, rtol = 1e-2)
+const CONFIG_VERY_LOW_TOL = (atol = 5e-3, rtol = 5e-2)
 
-function test_runtests()
-    optimizer = MOI.instantiate(Cbc.Optimizer, with_bridge_type = Float64)
+function test_runtests(optimizer)
     model = QuadraticToBinary.Optimizer{Float64}(optimizer)
     MOI.set(model, MOI.Silent(), true)
     MOI.Test.runtests(
         model,
         MOI.Test.Config(
-            atol = 1e-3, rtol = 1e-2,
+            atol = 1e-3, rtol = 1e-2, # LOW_TOL
             exclude = Any[
-                # MOI.DualStatus,
-                # Cbc limitations
                 MOI.ConstraintDual,
                 MOI.DualObjectiveValue,
                 MOI.ConstraintBasisStatus,
@@ -33,7 +34,6 @@ function test_runtests()
             ]
             ),
         exclude = String[
-            # require bounds on variables:
             "test_quadratic_nonhomogeneous",
             "test_quadratic_nonconvex_constraint_integration",
             "test_quadratic_nonconvex_constraint_basic",
@@ -49,30 +49,19 @@ function test_runtests()
             "test_objective_qp_ObjectiveFunction_edge_cases",
             "test_constraint_qcp_duplicate_off_diagonal",
             "test_constraint_qcp_duplicate_diagonal",
-            # TODO: broken in Cbc:
-            # https://github.com/jump-dev/Cbc.jl/blob/master/test/MOI_wrapper.jl
-            # TODO(odow): bug in Cbc.jl
-            "test_model_copy_to_UnsupportedAttribute",
-            "test_model_ModelFilter_AbstractConstraintAttribute",
-            # TODO(odow): bug in MOI
-            "test_model_LowerBoundAlreadySet",
-            "test_model_UpperBoundAlreadySet",
-            # TODO(odow): upstream bug in Cbc
-            "_Indicator_",
-            "test_linear_SOS1_integration",
-            "test_linear_SOS2_integration",
-            "test_solve_SOS2_add_and_delete",
-            # Can't prove infeasible.
-            "test_conic_NormInfinityCone_INFEASIBLE",
-            "test_conic_NormOneCone_INFEASIBLE",
-            "test_solve_TerminationStatus_DUAL_INFEASIBLE",
         ],
     )
     #=
-        Hard
+        bounds required
     =#
-    optimizer = MOI.instantiate(Cbc.Optimizer, with_bridge_type = Float64)
-    model = QuadraticToBinary.Optimizer{Float64}(optimizer)
+    config = MOI.Test.Config(
+        atol = 1e-3,
+        rtol = 1e-2,
+        exclude = Any[
+            MOI.ConstraintDual,
+            MOI.DualObjectiveValue
+        ],
+    )
     MOI.set(model, QuadraticToBinary.FallbackUpperBound(), +10.0)
     MOI.set(model, QuadraticToBinary.FallbackLowerBound(), -10.0)
     MOI.set(model, QuadraticToBinary.GlobalVariablePrecision(), 1e-5)
@@ -80,14 +69,7 @@ function test_runtests()
     MOI.set(model, MOI.TimeLimitSec(), 20.0)
     MOI.Test.runtests(
         model,
-        MOI.Test.Config(
-            atol = 5e-3,
-            rtol = 5e-2,
-            exclude = Any[
-                MOI.ConstraintDual,
-                MOI.DualObjectiveValue
-            ],
-        ),
+        config,
         include = String[
             "test_quadratic_nonhomogeneous",
             "test_quadratic_nonconvex_constraint_integration",
@@ -101,44 +83,32 @@ function test_runtests()
             "test_objective_qp_ObjectiveFunction_edge_cases",
             "test_constraint_qcp_duplicate_off_diagonal",
             "test_constraint_qcp_duplicate_diagonal",
+            "test_quadratic_SecondOrderCone_basic",
+            "test_quadratic_integration",
+            # "test_quadratic_duplicate_terms",
         ]
     )
-    #=
-        more precise bounds
-    =#
-    MOI.set(model, QuadraticToBinary.FallbackUpperBound(), 1.0)
-    MOI.set(model, QuadraticToBinary.FallbackLowerBound(), 0.0)
-    MOI.set(model, QuadraticToBinary.GlobalVariablePrecision(), 1e-5)
-    MOI.set(model, MOI.Silent(), true)
-    MOI.set(model, MOI.TimeLimitSec(), 80.0)
+    config = MOI.Test.Config(
+        atol = 5e-3,
+        rtol = 5e-2,
+        exclude = Any[
+            MOI.ConstraintDual,
+            MOI.DualObjectiveValue
+        ],
+    )
     MOI.Test.runtests(
         model,
-        MOI.Test.Config(
-            atol = 5e-3,
-            rtol = 5e-2,
-            optimal_status = MOI.OPTIMAL,
-            exclude = Any[
-                MOI.ConstraintDual,
-                MOI.DualObjectiveValue
-            ],
-        ),
+        config,
         include = String[
-            "test_quadratic_SecondOrderCone_basic",
-            "test_quadratic_integration",
             "test_quadratic_duplicate_terms",
         ]
     )
-    
     return
 end
-@testset "MOI Unit" begin
-    @time test_runtests()
-end
 
-@testset "basic_constraint_tests" begin
-    for opt in optimizers
-        MODEL = QuadraticToBinary.Optimizer{Float64}(opt)
-        MOIT.basic_constraint_tests(MODEL, CONFIG)
+@testset "MOI Unit" begin
+    for opt in optimizers_high_tol
+        @time test_runtests(opt)
     end
 end
 
@@ -180,71 +150,6 @@ end
     end
 end
 
-@testset "MOI Deprecated Unit" begin
-    for opt in optimizers
-        MODEL = QuadraticToBinary.Optimizer{Float64}(opt)
-        MOIT.unittest(MODEL, CONFIG, [
-            "number_threads", # FIXME implement `MOI.NumberOfThreads`
-            # INFEASIBLE_OR_UNBOUNDED instead of DUAL_INFEASIBLEÇ
-            "solve_unbounded_model",
-            # No quadratics (because of no bounds):
-            "delete_soc_variables",
-            "solve_qcp_edge_cases",
-            "solve_qp_edge_cases",
-            "solve_qp_zero_offdiag",
-        ])
-    end
-end
-
-@testset "ModelLike" begin
-    # @test MOI.get(CACHED, MOI.SolverName()) == "COIN Branch-and-Cut (Cbc)"
-    for opt in optimizers
-        MODEL = QuadraticToBinary.Optimizer{Float64}(opt)
-        @testset "default_objective_test" begin
-            MOIT.default_objective_test(MODEL)
-        end
-        @testset "default_status_test" begin
-            MOIT.default_status_test(MODEL)
-        end
-        @testset "nametest" begin
-            MOIT.nametest(MODEL)
-        end
-        @testset "validtest" begin
-            MOIT.validtest(MODEL)
-        end
-        @testset "emptytest" begin
-            MOIT.emptytest(MODEL)
-        end
-        @testset "orderedindicestest" begin
-            MOIT.orderedindicestest(MODEL)
-        end
-        @testset "copytest" begin
-            # Requires VectorOfVariables
-            # MOIT.copytest(MODEL, MOIU.CachingOptimizer(
-            #     ModelForCachingOptimizer{Float64}(),
-            #     Cbc.Optimizer()
-            # ))
-        end
-    end
-end
-
-@testset "Continuous Linear" begin
-    for opt in optimizers
-        MODEL = QuadraticToBinary.Optimizer{Float64}(opt)
-        MOIT.contlineartest(MODEL, CONFIG)
-    end
-end
-
-@testset "Integer Linear" begin
-    for opt in optimizers
-        MODEL = QuadraticToBinary.Optimizer{Float64}(opt)
-        MOIT.intlineartest(MODEL, CONFIG, [
-            "indicator1", "indicator2", "indicator3", "indicator4",
-            "int2",
-        ])
-    end
-end
-
 @testset "From MOI ncqcp" begin
     for opt in optimizers
         MODEL = QuadraticToBinary.Optimizer{Float64}(opt)
@@ -263,7 +168,6 @@ end
         qcp3test_mod(MODEL, CONFIG_LOW_TOL)
         qcp4test_mod(MODEL, CONFIG_LOW_TOL)
         qcp5test_mod(MODEL, CONFIG_LOW_TOL)
-        #
         MOI.set(MODEL, QuadraticToBinary.GlobalVariablePrecision(), 1e-5)
         socp1test_mod(MODEL, CONFIG_LOW_TOL)
     end
@@ -276,6 +180,13 @@ end
         qp1test_mod(MODEL, CONFIG_LOW_TOL)
         MOI.set(MODEL, QuadraticToBinary.GlobalVariablePrecision(), 1e-4)
         qp2test_mod(MODEL, CONFIG_VERY_LOW_TOL)
+    end
+end
+
+@testset "From MOI qp" begin
+    for opt in optimizers_high_tol
+        MODEL = QuadraticToBinary.Optimizer{Float64}(opt)
+        MOI.set(MODEL, QuadraticToBinary.GlobalVariablePrecision(), 1e-6)
         qp3test_mod(MODEL, CONFIG_LOW_TOL)
     end
 end
diff --git a/test/solvers/highs.jl b/test/solvers/highs.jl
new file mode 100644
index 0000000..bf93322
--- /dev/null
+++ b/test/solvers/highs.jl
@@ -0,0 +1,14 @@
+using HiGHS
+
+highs = MOI.instantiate(HiGHS.Optimizer, with_bridge_type = Float64)
+MOI.set(highs, MOI.Silent(), true)
+push!(optimizers, highs)
+
+highs_high_tol = MOI.instantiate(HiGHS.Optimizer, with_bridge_type = Float64)
+MOI.set(highs_high_tol, MOI.Silent(), true)
+tol = 1e-9
+# MOI.set(highs_high_tol, MOI.RawOptimizerAttribute("small_matrix_value"), 1e-12)
+MOI.set(highs_high_tol, MOI.RawOptimizerAttribute("mip_feasibility_tolerance"), tol)
+MOI.set(highs_high_tol, MOI.RawOptimizerAttribute("primal_feasibility_tolerance"), tol)
+# MOI.set(highs_high_tol, MOI.RawOptimizerAttribute("presolve"), "off")
+push!(optimizers_high_tol, highs_high_tol)
\ No newline at end of file
diff --git a/test/solvers/xpress.jl b/test/solvers/xpress.jl
new file mode 100644
index 0000000..0c4b063
--- /dev/null
+++ b/test/solvers/xpress.jl
@@ -0,0 +1,7 @@
+using Xpress
+
+XPRESS_OPTIMIZER = MOI.Bridges.full_bridge_optimizer(
+    Xpress.Optimizer(), Float64
+)
+push!(optimizers_high_tol, XPRESS_OPTIMIZER)
+push!(optimizers, XPRESS_OPTIMIZER)