The paper talks a lot about this, and compares directly in the second and third experiments (code included). Overall we made SymForce largely to improve on autodiff workflows like in Ceres. The combination of symbolic code in Python, automatic computation of tangent space Jacobians, and code generation can simultaneously make it much faster to iterate, much faster at runtime (there is no runtime chain rule as with autodiff libraries), and less code to maintain. We've seen this very directly in production on our drones.