In terms of FLOPS, Ryzen is ~1,000,000 times faster than a 486.
For serial branchy code, it isn't a million times faster, but that has almost nothing to do with legacy and everything to do with the nature of serial code and that you can't linearly improve serial execution with architecture and transistor counts (you can sublinearly improve it), but rather with Denard scaling.
It is worth noting, though, that purely via Denard scaling, Ryzen is already >100x faster, though! And via architecture (those transistors) it is several multiples beyond that.
In general compute, if you could clock it down at 33 or 66MHz, a Ryzen would be much faster than a 486, due to using those transistors for ILP (instruction-level parallelism) and TLP (thread-level parallelism). But you won't see any TLP in a single serial program that a 486 would have been running, and you won't get any of the SIMD benefits either, so you won't get anywhere near that in practice on 486 code.
The key to contemporary high performance computing is having more independent work to do, and organizing the data/work to expose the independence to the software/hardware.
For serial branchy code, it isn't a million times faster, but that has almost nothing to do with legacy and everything to do with the nature of serial code and that you can't linearly improve serial execution with architecture and transistor counts (you can sublinearly improve it), but rather with Denard scaling.
It is worth noting, though, that purely via Denard scaling, Ryzen is already >100x faster, though! And via architecture (those transistors) it is several multiples beyond that.
In general compute, if you could clock it down at 33 or 66MHz, a Ryzen would be much faster than a 486, due to using those transistors for ILP (instruction-level parallelism) and TLP (thread-level parallelism). But you won't see any TLP in a single serial program that a 486 would have been running, and you won't get any of the SIMD benefits either, so you won't get anywhere near that in practice on 486 code.
The key to contemporary high performance computing is having more independent work to do, and organizing the data/work to expose the independence to the software/hardware.