Aya Neo Geek 1S vs Aya Neo Konkr Fit
Aya Neo Konkr Fit — Pick the Aya Neo Konkr Fit — it leads these groups: Display 1 Features 1
Tap a group name to see the proof · The scoring method is public
The short answer
Aya Neo Konkr Fit wins this comparison with a score of 70/100, ahead of Aya Neo Geek 1S (34).
The main differences: Output refresh (60 Hz vs 144 Hz) · Ray tracing (— vs ✓).
Scores are computed from 8 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
| Aya Neo Geek 1S | Aya Neo Konkr Fit | |
|---|---|---|
| Performance | 41 | 41 |
| Memory | 47 | 47 |
| Display | 0 | 100 |
| Features | 45 | 100 |
How to choose
Pick the Aya Neo Konkr Fit if you care about…
- Output refresh 144 Hz +84 Hz vs 60 Hz
- Ray tracing ✓ vs —
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Where they differ
2 · Only rows that actually differ, with a verdict per row
| Aya Neo Geek 1S | Aya Neo Konkr Fit | Where they differ | |
|---|---|---|---|
| Output refresh | 60 Hz | 144 Hz | Aya Neo Konkr Fit |
| Ray tracing | — | ✓ | Aya Neo Konkr Fit |
The record
Grouped and collapsible · Tap a group to fold
Performance4▾
| Aya Neo Geek 1S | Aya Neo Konkr Fit | |
|---|---|---|
| CPU cores | 8 | — |
| Base clock | 3.3 GHz | — |
| GPU performance | 4.1 TFLOPS | — |
| Shader units | 768 | — |
Memory1▾
| Aya Neo Geek 1S | Aya Neo Konkr Fit | |
|---|---|---|
| RAM | 32 GB | 32 GB |
Display1▾
| Aya Neo Geek 1S | Aya Neo Konkr Fit | |
|---|---|---|
| Output refresh | 60 Hz | 144 Hz |
Features2▾
| Aya Neo Geek 1S | Aya Neo Konkr Fit | |
|---|---|---|
| Ray tracing | — | ✓ |
| NVMe storage | ✓ | ✓ |
Data sources: GSMArena
FAQ
Which is better, Aya Neo Geek 1S or Aya Neo Konkr Fit?
Aya Neo Konkr Fit has the higher overall score (70 vs 34) and leads in Display, Features.
What are the main differences?
The main differences: Output refresh (60 Hz vs 144 Hz) · Ray tracing (— vs ✓).
How are the scores calculated?
We normalize 8 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.
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