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MCHOSE G75 V2 vs Centauri60/80 Hall Effect Gaming Keyboard

🏆 Settled

MCHOSE G75 V2 — Pick the MCHOSE G75 V2 — it leads these groups: Design 1

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

The short answer

MCHOSE G75 V2 wins this comparison with a score of 69/100, ahead of Centauri60/80 Hall Effect Gaming Keyboard (67).

The main differences: Layout (75%配列 vs QWERTY) · Key count (75 vs 68) · Switch type (冰蓝轴 vs Mechanical).

Scores are computed from 7 public spec attributes; missing values stay marked unknown in tables and are imputed at the category median, never passed off as measured.

🏆 Overall winner MCHOSE G75 V2

MCHOSE G75 V2

MCHOSE
69score / 100
1 win · 4 tied
Centauri60/80 Hall Effect Gaming Keyboard

Centauri60/80 Hall Effect Gaming Keyboard

MelGeek
67score / 100
0 wins · 5 tied

At a glance

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

MCHOSE G75 V2Centauri60/80 Hall Effect Gaming Keyboard
Design 67 85
Connect 87 66

How to choose

Pick the MCHOSE G75 V2 if you care about…

  • Key count 75 +7 vs 68

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Where they differ

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

MCHOSE G75 V2Centauri60/80 Hall Effect Gaming KeyboardWhere they differ
Layout 75%配列QWERTY Depends
Key count 7568 MCHOSE G75 V2
Switch type 冰蓝轴Mechanical Depends

The record

Grouped and collapsible · Tap a group to fold

Design4▾
MCHOSE G75 V2Centauri60/80 Hall Effect Gaming Keyboard
Layout 75%配列 QWERTY
Hot-swappable — ✓
Per-key RGB — ✓
Key count 75 68
Connect2▾
MCHOSE G75 V2Centauri60/80 Hall Effect Gaming Keyboard
Wired — ✓
2.4GHz wireless ✓ —
Feel1▾
MCHOSE G75 V2Centauri60/80 Hall Effect Gaming Keyboard
Switch type 冰蓝轴 Mechanical

Data sources: Amazon · GSMArena

FAQ

Which is better, MCHOSE G75 V2 or Centauri60/80 Hall Effect Gaming Keyboard?

MCHOSE G75 V2 edges ahead 69 to 67 and leads in Design.

What are the main differences?

The main differences: Layout (75%配列 vs QWERTY) · Key count (75 vs 68) · Switch type (冰蓝轴 vs Mechanical).

How are the scores calculated?

We normalize 7 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.