You are totally right. Image counts alone are not everything when it comes to mapping or when one tries to measure or quantify the amount of contributions any specific contributor has made. It is relatively easy to produce and upload lots of low quality useless images.
There is a lot to unpack here, so I am going to try to add some structure to your questions.
360° images are counted no different than planar images towards global contributor counters: 1 planar image == 1 360° image. However, 360° images are tracked in the Mapillary database as such. This primarily enables filtering for 360° versus planar images later on. It also enables computing any potential 360° versus planar image counters and stats. But, like already mentioned, there are no public or contributor facing bespoke 360° image counters.
Unfortunately, not every 360° camera embeds metadata the same way in video files. While most of the Exif metadata required for Mapillary (or mapping in general) has been standardized for JPEG images, it has not been standardized for video files. However, Mapillary supports GoPro’s GPMF metadata streams in video files for easy workflows. Hence, your best 360° option for Mapillary currently is indeed GoPro MAX2. MAX2 may not boast highest resolutions or fps counts on the market but it is the camera for mapping due to the simple workflow it enables for most contributors. Just turn on the built‑in GPS receiver, set photo time lapse or video capture mode, capture a street or road, and upload image or video files directly to Mapillary. That’s it! 
360° images are generally neither better nor worse than planar images. It depends. The same goes for image counts, or rather image capture density. It depends too. Both aspects depend on the methods used to extract data from imagery and how imagery is used for spatial reconstruction. However, there are a few simple key metrics and rules of thumb that always apply:
- More data usually wins in the long run
- High angular pixel density wins immediately (also more data per image)
- Higher resolution wins immediately (also more data per image)
- Short baselines (distance between images; high fps or slow moving camera) win in the long run (ties in with 1)
Mapillary currently uses photogrammetry, more specifically structure‑from‑motion to spatially reconstruct imagery and thus pinpoint features visible in the imagery. In simple words, this spatial reconstruction method favors high resolutions and long(er) baselines (or longer focal lengths but lets keep it simple). High resolutions and long baselines make sure that overlapping images are sufficiently different from each other to create stable reconstruction.
On the other hand, AI reconstruction models thrive on repetitive data (short baselines) and can easily compensate for lower resolutions (thus also lower angular pixel density). The same goes for AI pattern recognition and detection models. They too thrive on temporally repetitive data.
Due to how 360° cameras are usually built, they suffer from considerably lower angular pixel densities than planar cameras. However, 360° cameras capture more space than planar cameras per image. These are significant tradeoffs to consider when capturing for mapping.
Having said that, imho 1 image every 3 meters is okayish for smartphone captures as an overall capture density target for the platform. However, it is too sparse in urban environments and also far from future proof. If you really want to make best use of your rather expensive 360° camera then make sure to capture as many high resolution images per meter as possible. 2 fps in high resolution image mode is absolutely fine, even when driving at urban speeds. When capturing video, make sure that the Mapillary uploader or server-backed extracts every frame from your preciously captured video file because you can always drop or delete data but you cannot add data retroactively once you have passed a location (you may not be able to revisit it). And no, please do not capture videos in 50 or 60 fps for Mapillary. 25 or 30 fps is plenty enough. More fps is not necessarily better for mapping because it heavily depends on the speed the camera is moving at and video recording bit rate. 25 fps is really the sweet spot for most vehicle speeds. 5 fps is perfect for walking but often unavailable.
I hope this helps. 