I'm not a regular OR-Tools user, but it allows you to call its own CP solver or external MIP solvers. I can provide a few general comments about what you'll find applying different techniques to TSP solving:
1. Nothing beats Concorde for solving pure TSPs: http://www.math.uwaterloo.ca/tsp/concorde.html
2. Mixed integer programming solvers do very well because you can relax the subtour elimination constraints to solve a simpler problem (such as an assignment or a 2-matching), and add them to the problem representation as they are violated in the search tree. With tight bounds the solver can fathom away most of the search space.
3. All of this breaks apart when you need to route multiple vehicles or handle side constraints (precedence, time windows, capacity). This is where CP, DDs, and Hybrid Optimization techniques start to really shine.
It's (3) that we're most interested in because realistic problems tend not follow simple, pure representations like the TSP. So when we do publish benchmarks (and their associated models) it will likely be for problems like the Li & Lim PDPTW instances (https://www.sintef.no/projectweb/top/pdptw/li-lim-benchmark/).
Another important aspect is time to find high quality solutions. Most people doing on-demand routing need good solutions that are actionable quickly. DDs have already shown a lot of promise in this area.