[Aim Trainer Pack] X2 v1 Mini vs [BlueArchive Hoshino Edition] X2 CrazyLight Medium Gaming Mouse
The short answer
[BlueArchive Hoshino Edition] X2 CrazyLight Medium Gaming Mouse wins this comparison with a score of 54/100, ahead of [Aim Trainer Pack] X2 v1 Mini (50).
Scores are computed from 4 public spec attributes; missing values stay marked unknown in tables and are imputed at the category median, never passed off as measured.
![[Aim Trainer Pack] X2 v1 Mini](/static/img/products/pulsar-aim-trainer-pack-x2-mini.webp?v=1790604354)
![[BlueArchive Hoshino Edition] X2 CrazyLight Medium Gaming Mouse](/static/img/products/pulsar-bluearchive-hoshino-edition-x2-crazylight-gaming-mouse.webp?v=1790604633)
[BlueArchive Hoshino Edition] X2 CrazyLight Medium Gaming Mouse
PulsarAt a glance
0–100 per group, normalized within the category · Tap a group for details
| [Aim Trainer Pack] X2 v1 Mini | [BlueArchive Hoshino Edition] X2 CrazyLight Medium Gaming Mouse | |
|---|---|---|
| Performance | 63 | 75 |
| Design | 33 | 41 |
| Connect | 64 | 68 |
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The record
Grouped and collapsible · Tap a group to fold
Performance2▾
| [Aim Trainer Pack] X2 v1 Mini | [BlueArchive Hoshino Edition] X2 CrazyLight Medium Gaming Mouse | |
|---|---|---|
| Sensor | — | Optical |
| Weight | — | 36 g |
Design1▾
| [Aim Trainer Pack] X2 v1 Mini | [BlueArchive Hoshino Edition] X2 CrazyLight Medium Gaming Mouse | |
|---|---|---|
| Buttons | — | 7 |
Connect1▾
| [Aim Trainer Pack] X2 v1 Mini | [BlueArchive Hoshino Edition] X2 CrazyLight Medium Gaming Mouse | |
|---|---|---|
| Wired | — | — |
Data sources: Amazon
FAQ
Which is better, [Aim Trainer Pack] X2 v1 Mini or [BlueArchive Hoshino Edition] X2 CrazyLight Medium Gaming Mouse?
[BlueArchive Hoshino Edition] X2 CrazyLight Medium Gaming Mouse has the higher overall score (54 vs 50).
What are the main differences?
Scores are computed from 4 public spec attributes; missing values stay marked unknown in tables and are imputed at the category median, never passed off as measured.
How are the scores calculated?
We normalize 4 public spec attributes within the category and weight them by group into a 0–100 score; missing values are inherited from spec-identical twins or imputed at the category median, so a sparse spec sheet never earns an advantage. The methodology is public.