Govee BR30 Smart Colored Bulbs E26 850 Lumens vs 15-Inch RGBWW Smart LED Ceiling Light Pro
15-Inch RGBWW Smart LED Ceiling Light Pro — Pick the 15-Inch RGBWW Smart LED Ceiling Light Pro — it leads these groups: Light 1
Tap a group name to see the proof · The scoring method is public
The short answer
15-Inch RGBWW Smart LED Ceiling Light Pro wins this comparison with a score of 55/100, ahead of Govee BR30 Smart Colored Bulbs E26 850 Lumens (50).
The main differences: Brightness (850 lm vs 2400 lm).
Scores are computed from 3 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
| Govee BR30 Smart Colored Bulbs E26 850 Lumens | 15-Inch RGBWW Smart LED Ceiling Light Pro | |
|---|---|---|
| Light | 9 | 25 |
| Efficiency | 99 | 97 |
How to choose
Pick the 15-Inch RGBWW Smart LED Ceiling Light Pro if you care about…
- Brightness 2400 lm +1550 lm vs 850 lm
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Where they differ
1 · Only rows that actually differ, with a verdict per row
| Govee BR30 Smart Colored Bulbs E26 850 Lumens | 15-Inch RGBWW Smart LED Ceiling Light Pro | Where they differ | |
|---|---|---|---|
| Brightness | 850 lm | 2400 lm | 15-Inch RGBWW Smart LED Ceiling Light Pro |
The record
Grouped and collapsible · Tap a group to fold
Light2▾
| Govee BR30 Smart Colored Bulbs E26 850 Lumens | 15-Inch RGBWW Smart LED Ceiling Light Pro | |
|---|---|---|
| Brightness | 850 lm | 2400 lm |
| Color output | — | — |
Efficiency1▾
| Govee BR30 Smart Colored Bulbs E26 850 Lumens | 15-Inch RGBWW Smart LED Ceiling Light Pro | |
|---|---|---|
| Power draw | 9.5 W | — |
Data sources: Amazon
FAQ
Which is better, Govee BR30 Smart Colored Bulbs E26 850 Lumens or 15-Inch RGBWW Smart LED Ceiling Light Pro?
15-Inch RGBWW Smart LED Ceiling Light Pro has the higher overall score (55 vs 50) and leads in Light.
What are the main differences?
The main differences: Brightness (850 lm vs 2400 lm).
How are the scores calculated?
We normalize 3 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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