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Click To Win! πŸ”₯⚑ (Fastest Clicker Challenge)

Click To Win! πŸ”₯⚑ (Fastest Clicker Challenge)

A Roblox experience owned by C3's Studios.

Clicker & idleENclickerleaderboardarcade

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

Welcome to Click To Win!, the fastest and easiest way to prove your clicking skills. πŸ’₯ Smash the glowing button to rack up clicks! πŸ† Climb the global leaderboard and show off your speed. 🎢 Hyped background music keeps the energy going. ✨ Simple, fast, and fun β€” can YOU become the #1 clicker? How to Play: Click the big neon button as fast as you can. Watch your score climb higher and higher. Challenge friends and dominate the leaderboard! ⚑ Updates coming soon: upgrades, rebirths, and rewards. Stay tuned!

Last updated by the developer 14 Sept 2025, 06:17 UTC.

0
playing (measured 12 Aug 2026, 19:20 UTC)
2,556
lifetime visits
40%
liked (4 πŸ‘ / 6 πŸ‘Ž)
6
favourites (2.3 per 1k visits)
14 September 2025
created (per Roblox)

Play on Roblox β†’

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 β€” so this will fill in on its own rather than being estimated. We do not publish numbers we have not measured.

Measurement log β€” every players count we have recorded
Measured (UTC)PlayersChange
12 Aug 2026, 19:200first reading

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

Monetization β€” game passes

Autoclicker
150 R$
πŸ”₯ 10x Clicker Power! πŸ”₯
100 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

Welcome! Β· welcome2,283 Β· 0.00% win
100 Clicks! Β· progression618 Β· 0.00% win
10,000 Clicks! Β· progression22 Β· 0.00% win

How many players have ever earned each badge β€” 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.