💪Giant Simulator
A Roblox experience owned by Awesome Fun Studios.
SimulatorENgymstrength trainingpet collection
💪Giant Simulator is a simulator experience created on 2024-06-06. It has accumulated 3,708,630 lifetime visits and 453,846 favorites. The game holds a 91% like percentage. Currently, zero users are playing. The description mentions lifting mechanics and exclusive pets, inspired by similar titles.
Description (written by the developer; not a statement by this register)
💪Welcome to Giant Simulator💪 🏆Lift to train and become the strongest in the game🏆 🔥Become massive and destroy everyone else🔥 👍Play for 20 minutes and get an EXCLUSIVE PET👍 ✅Join our group and👍like the game for FREE 3X Multiplier! https://www.roblox.com/groups/32572102 [New updates coming soon!💪] Inspired by Gym League & Lifting Simulator Ignore Tags: Pet, Simulator, Clicker, Anime, Dragon Ball, DBZ, Fireball Simulator, Kamehameha Simulator, KI, Kamehameha, Energy Beam Simulator, Laser Simulator, Shoot Beam Simulator, Titan, Titan Simulator, Giant Simulator, Buff, Giant, Weightlifting, Weightlifting Simulator, Training, Lifting Simulator, Lifting, Evolution, Evolution Simulator, Gym, Gym League
Last updated by the developer 10 Aug 2024, 02:13 UTC.
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 We do not publish numbers we have not measured.
| Measured (UTC) | Players | Change |
|---|---|---|
| 10 Aug 2026, 14:03 | 0 | first reading |
Concurrent players at each of our measurements (insert-on-change).
Monetization — game passes
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
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.