In a non-photographic context, the differences between NN and bicubic interpolation - yes the correct term - can be quite dramatic depending on the data and for images you would not use NN interpolation at all. Bicubic will in general do a far superior result in filling in the data on the finer grid. I have to use interpolation a lot in my work, and only ever use bicubic interpolation. However, the quality of the results will depend crucially on the level of detail and how rapidly it is changing. This is why some advocate a multistage process, whereby you only uprez a little at a time. Something with coarse detail changing slowly will be far easier to interpolate more accurately. While data with rapidly changing values will be more problematic. In this case if I run my program on a finer grid and compare it to the interpolated data from the coarser grid, it depends whether the coarse grid data was not so coarse as to miss data, which it cannot therefore intepolate. So what I'm saying if the camera with the lower res, has a enough res to resolve fine detail, then using bicubic or more sophisticated, you should do a good job when you uprez this to the same pixel count as the higher res camera. But if the low res camera is too low res to reproduce fine detail, when upressed it will be poor in comparison. I guess a 13MP 5D is good enough to hold up very well to a 21MP 5D II in this test and only at very large prints where even the 5D II is upressed will the differences be great.
Richard are you sure you don't mean bilinear interpolation rather than NN, because for images bilinear will do a much much much better job. You wouldn't use NN.
Here are the definitions used of the methods used in PS.
Nearest Neighbor: The Nearest Neighbor interpolation is the fastest option. However, it is also the least sophisticated. It simply takes the color of a pixel and assigns it to the new pixels that are created from that pixel. Due to this rather simplistic approach, Nearest Neighbor interpolation does not create an anti-aliasing effect. This leads to problems with jaggies. Consequently, Nearest Neighbor interpolation is considered to be incapable of producing photographic quality work.
Bilinear: Bilinear interpolation uses the information from a pixel (let's call it the original pixel) and four of the pixels that touch it to determine the color of the new pixels that are created from the original pixel. Bilinear uses rather simple, linear calculations to do this. The Bilinear interpolation does have an anti-aliasing effect. However, it is not considered good enough for photo quality images.
Bicubic: Bicubic interpolation uses the information from an original pixel and sixteen of the surrounding pixels to determine the color of the new pixels that are created from the original pixel. Bicubic interpolation is a big improvement over the previous two interpolation methods for two reasons: (1) Bicubic interpolation uses data from a larger number of pixels and (2) Bicubic interpolation uses a Bicubic calculation that is more sophisticated than the calculations of the previous interpolation methods. Bicubic interpolation is capable of producing photo quality results and is probably the method most commonly used.