Moisturising Rich Cream vs CeraVe Moisturizing Cream
The short answer
Moisturising Rich Cream wins this comparison with a score of 42/100, ahead of CeraVe Moisturizing Cream (41).
The main differences: Ingredient count (29 vs 24) · Star ingredients (2 vs 1) · Hyaluronic acid (— vs ✓).
Scores are computed from 12 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
How to choose
Pick the Moisturising Rich Cream if you care about…
- Star ingredients 2 +1 vs 1
- AHA exfoliants ✓ vs —
Pick the CeraVe Moisturizing Cream if you care about…
- Ingredient count 24 +5 vs 29
- Hyaluronic acid ✓ vs —
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Where they differ
4 · Only rows that actually differ, with a verdict per row
| Moisturising Rich Cream | CeraVe Moisturizing Cream | Where they differ | |
|---|---|---|---|
| Ingredient count | 29 | 24 | CeraVe Moisturizing Cream |
| Star ingredients | 2 | 1 | Moisturising Rich Cream |
| Hyaluronic acid | — | ✓ | CeraVe Moisturizing Cream |
| AHA exfoliants | ✓ | — | Moisturising Rich Cream |
The record
Grouped and collapsible · Tap a group to fold
Formula4▾
| Moisturising Rich Cream | CeraVe Moisturizing Cream | |
|---|---|---|
| Ingredient count | 29 | 24 |
| Star ingredients | 2 | 1 |
| Ingredients to avoid | 16 | 16 |
| Fragrance-free | ✓ | ✓ |
Actives8▾
| Moisturising Rich Cream | CeraVe Moisturizing Cream | |
|---|---|---|
| Niacinamide | — | — |
| Hyaluronic acid | — | ✓ |
| Vitamin C | — | — |
| Ceramides | ✓ | ✓ |
| Retinoid | — | — |
| Salicylic acid | — | — |
| AHA exfoliants | ✓ | — |
| Clay | — | — |
Data sources: INCIDecoder
FAQ
Which is better, Moisturising Rich Cream or CeraVe Moisturizing Cream?
Moisturising Rich Cream edges ahead 42 to 41. Choose CeraVe Moisturizing Cream instead if Ingredient count matters more to you.
What are the main differences?
The main differences: Ingredient count (29 vs 24) · Star ingredients (2 vs 1) · Hyaluronic acid (— vs ✓).
How are the scores calculated?
We normalize 12 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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