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EM11 Pro Ergonomic Vertical Mouse vs EM15 Compact Portable Wireless Mouse

🏆 Settled

EM11 Pro Ergonomic Vertical Mouse — Pick the EM11 Pro Ergonomic Vertical Mouse — it leads these groups: Design 1

Tap a group name to see the proof · The scoring method is public

The short answer

EM11 Pro Ergonomic Vertical Mouse wins this comparison with a score of 50/100, ahead of EM15 Compact Portable Wireless Mouse (48).

The main differences: Buttons (8 vs 6).

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.

🏆 Overall winner EM11 Pro Ergonomic Vertical Mouse

EM11 Pro Ergonomic Vertical Mouse

ProtoArc
50score / 100
1 win · 2 tied
EM15 Compact Portable Wireless Mouse

EM15 Compact Portable Wireless Mouse

ProtoArc
48score / 100
0 wins · 3 tied

At a glance

0–100 per group, normalized within the category · Tap a group for details

EM11 Pro Ergonomic Vertical MouseEM15 Compact Portable Wireless Mouse
Performance 62 56
Design 38 33
Connect 63 63

How to choose

Pick the EM11 Pro Ergonomic Vertical Mouse if you care about…

  • Buttons 8 +2 vs 6

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Where they differ

1 · Only rows that actually differ, with a verdict per row

EM11 Pro Ergonomic Vertical MouseEM15 Compact Portable Wireless MouseWhere they differ
Buttons 86 EM11 Pro Ergonomic Vertical Mouse

The record

Grouped and collapsible · Tap a group to fold

Performance2▾
EM11 Pro Ergonomic Vertical MouseEM15 Compact Portable Wireless Mouse
Sensor Optical Optical
Weight — 68 g
Design1▾
EM11 Pro Ergonomic Vertical MouseEM15 Compact Portable Wireless Mouse
Buttons 8 6
Connect1▾
EM11 Pro Ergonomic Vertical MouseEM15 Compact Portable Wireless Mouse
Wired ✓ ✓

Data sources: Amazon

FAQ

Which is better, EM11 Pro Ergonomic Vertical Mouse or EM15 Compact Portable Wireless Mouse?

EM11 Pro Ergonomic Vertical Mouse edges ahead 50 to 48 and leads in Design.

What are the main differences?

The main differences: Buttons (8 vs 6).

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.