You can't really compare them on a FLOPS basis. Firstly because our and Jäkel's algorithms are completely different. In fact within the FPGA accelerator itself we don't even use a single multiply, all our operations are boolean logic and counting. Whereas Jäkel was able to exploit the GPU's strong preference for matrix multiplication. All his operations were integer multiplications.

In fact, in terms of raw operation count, Jäkel actually did more. There appears to be this tradeoff within the Jumping Formulas, where any jump you pick keeps the same fundamental complexity, with even a slight preference towards smaller jumps. It is just that GPU development is several decades ahead of FPGA development, thanks to ML and Rendering hype, which more than compensates for the slightly worse fundamental complexity.

As a sidenote, raw FLOP counts from FPGA vendors are wildly inflated. The issue with FPGA designs is that getting this theorethical FLOP count is nigh-impossible, because getting all components running at the theorethical clock frequency limit is incredibly difficult, compare that with GPUs, where at least your processing frequency is a given.