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[100K!] Backrooms – K. Pixels

[100K!] Backrooms – K. Pixels

A Roblox experience owned by GMod Games.

HorrorENbackroomshorrorsurvival

Description (written by the developer; not a statement by this register)

The Backrooms... Survive different levels and escape from entities by finding the exit door... _____________________________________________________ Special thanks to Arnau for helping me with the map, lightning, effects and details. BRO A NEW TRAILER?

Last updated by the developer 26 Mar 2024, 15:51 UTC.

0
playing (measured 12 Aug 2026, 09:50 UTC)
134,793
lifetime visits
52%
liked (82 👍 / 75 👎)
483
favourites (3.6 per 1k visits)
27 May 2022
created (per Roblox)

Play on Roblox →

Live players over time

Growth

Growth history is still building. We hold one measurement for this entry. A trend needs at least two, taken days apart — so this will fill in on its own rather than being estimated. We do not publish numbers we have not measured.

Measurement log — every players count we have recorded
Measured (UTC)PlayersChange
12 Aug 2026, 09:500first reading

Concurrent players at each of our measurements (insert-on-change).

Monetization — game passes

M4
350 R$
VIP
350 R$
Machine Gun
250 R$
Magnum
150 R$
AK
100 R$
Rope
49 R$
Donation because I'm rich
20 R$

Game passes for sale and their prices, measured from the Roblox passes API. Developer products bought inside the game aren’t publicly listable.

Badge funnel

Find Him... · secret30,367 · 0.00% win
Open Lights! · other9,289 · 0.00% win
Close Door · other9,139 · 0.00% win
Sus? · other8,505 · 0.00% win
Pitfalls · progression2,377 · 0.00% win
Find The Exit... · progression1,210 · 0.00% win
Guest Hat · collection546 · 0.00% win
Escape Level ! · progression376 · 0.00% win
Escape Poolrooms! · progression367 · 0.00% win
Found Nikilis! · collection103 · 0.00% win

How many players have ever earned each badge — a proxy for how far players get before they stop. Steep drop-offs mark where a game loses people. Win rate is the past-day share of players who earned it; categories are assigned by a language model from each badge’s own name and description.