GIGABYTE M27Q vs GreatWall G2568E
The short answer
GIGABYTE M27Q wins this comparison with a score of 58/100, ahead of GreatWall G2568E (57).
The main differences: Resolution (2560 x 1440 px vs 1920x1080) · Panel type (IPS, LCD+1, LED-backlit vs TN).
Scores are computed from 17 public spec attributes; missing values stay marked unknown in tables and are imputed at the category median, never passed off as measured.
At a glance
0–100 per group, normalized within the category · Tap a group for details
| GIGABYTE M27Q | GreatWall G2568E | |
|---|---|---|
| Display | 57 | 38 |
| Performance | 50 | 78 |
| Connectivity | 39 | 29 |
| Ergonomics | 88 | 88 |
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Where they differ
2 · Only rows that actually differ, with a verdict per row
| GIGABYTE M27Q | GreatWall G2568E | Where they differ | |
|---|---|---|---|
| Resolution | 2560 x 1440 px | 1920x1080 | Depends |
| Panel type | IPS, LCD+1, LED-backlit | TN | Depends |
The record
Grouped and collapsible · Tap a group to fold
Display8▾
| GIGABYTE M27Q | GreatWall G2568E | |
|---|---|---|
| Screen size | 27 in | — |
| Resolution | 2560 x 1440 px | 1920x1080 |
| Max refresh rate | 170 Hz | — |
| Response time | 0.5 ms | — |
| Brightness | 350 cd/m² | — |
| Panel type | IPS, LCD+1, LED-backlit | TN |
| sRGB coverage | 140 % | — |
| HDR support | ✓ | — |
Performance2▾
| GIGABYTE M27Q | GreatWall G2568E | |
|---|---|---|
| AMD FreeSync | ✓ | — |
| VESA Adaptive Sync | — | — |
Connectivity3▾
| GIGABYTE M27Q | GreatWall G2568E | |
|---|---|---|
| HDMI ports | 2 | — |
| DisplayPort ports | 1 | — |
| Built-in speakers | — | — |
Ergonomics4▾
| GIGABYTE M27Q | GreatWall G2568E | |
|---|---|---|
| Height adjustment | ✓ | — |
| VESA mounting | ✓ | — |
| Weight (with stand) | 5500 g | — |
| Energy per 1000 h | 26 kWh | — |
Data sources: GSMArena · Icecat
FAQ
Which is better, GIGABYTE M27Q or GreatWall G2568E?
GIGABYTE M27Q edges ahead 58 to 57.
What are the main differences?
The main differences: Resolution (2560 x 1440 px vs 1920x1080) · Panel type (IPS, LCD+1, LED-backlit vs TN).
How are the scores calculated?
We normalize 17 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.