Here is a solution profile from the benchmark in the paper:
Lundell, A. Kronqvist, J. and Westerlund, T. The Supporting Hyperplane Optimization Toolkit: A Polyhedral Outer Approximation Based Convex MINLP Solver Utilizing a Single Branching Tree Approach (2018). http://www.optimization-online.org/DB_FILE/2018/06/6680.pdf
where some commercial and noncommercial solvers were tested on 406 convex MINLP instances from MINLPLib. The commercial version of SHOT uses CPLEX and CONOPT as subsolvers, while the noncommercial version uses Cbc and IPOPT.
Some benchmarks (for an older version of SHOT 0.9.2) is available in the open-access journal paper:
Kronqvist, J., Bernal, D.E, Lundell, A. and Grossmann, I.E. T. A review and comparison of solvers for convex MINLP. Optimization and Engineering 20(2), pp. 397-455 (2018). https://link.springer.com/article/10.1007/s11081-018-9411-8.
The files for the benchmarks are also available at: https://andreaslundell.github.io/minlpbenchmarks/.
Benchmarks for SHOT 1.0 on a test set of 326 nonconvex MINLP and MIQCQP problems are available in the preprint:
Lundell, A. and Kronqvist, J., Polyhedral Approximation Strategies in Nonconvex Mixed-Integer Nonlinear Programming. Optimization Online (2020). http://www.optimization-online.org/DB_HTML/2020/03/7691.html