RadicalRampage is a free browser-based vocabulary game built around rapid recognition. Each round places a source term in the center of the board and surrounds it with possible target-language answers. Match as many terms as you can before the clock expires, build a combo through accurate play, and review or share the completed run afterward.
A standard run starts with three minutes and a 4x4 board containing 12 answers around the prompt. Correct answers add score, while a wrong answer breaks the current combo and costs ten seconds. The format is designed for quick decisions and repeated exposure rather than slow flashcard recall.
English is the fixed source language. Simplified Chinese, Traditional Chinese, Japanese, and German are available as target languages. Readings and translations remain available during review when the dictionary data supports them.
Choose native frequency-based vocabulary or learner-focused HSK and TOCFL levels for Chinese, JLPT levels for Japanese, or CEFR levels for German. Levels are presented from easier to harder, while Native and learner modes use different base-score scales.
Consecutive correct answers build a combo. Reaching combo milestones adds bonus seconds to the clock, giving accurate players more time to extend a strong run. The game review records which rounds earned those bonuses.
Rampage expands the board to 6x6 and doubles the score. Berserk cuts the starting time from three minutes to ninety seconds and multiplies the score by 2.5. Both reward stronger play while making mistakes more costly.
You can play without an account. Signed-in runs are saved to My Games and can establish personal bests.
Every finished game has a shareable review page. It shows the final score, player, personal-best result, exact boards, correct and chosen answers, points, answer times, and combo seconds earned for each round. Readings and translations are available from the board when present.
The game connects terms across languages through shared concepts and combines dictionary, frequency, and learner-level data. Language data is never perfect, so signed-in players can report a questionable round directly from their own game review. Those reports help identify translations, readings, and term choices that need attention.