For this week’s battle, two online language learning apps will oppose: Babbel vs Duolingo.
At weighing Babbel is the heavier application with a weight of 90 MB. Its opponent Duolingo is lighter with a weight of 56 MB, or 61% less.
All the lights are now turned on the fighters and the match can finally begin.
In the first part of the battle to measure the impact of the launch phase of the application, Babbel (1.5 mAh) wins the first round by consuming 25% less than his opponent Duolingo (2 mAh). In the second round that corresponds to the use scenario, Babbel (13.5 mAh) still leads to Duolingo (19.5 mAh) with a 31% lower consumption. To end this confrontation, we have set up two decisive rounds of observation of the rest phases of each opponent. Babbel is still the leader of the battle with a consumption of less than 34% for the background inactivity phase and 54% for the foreground inactivity phase.
The bell rings, end of the match!
Without any surprise, the app Babbel wins this game on a global score of 17.2 mAh at 25.5 mAh, or 32% less battery consumed compared to his opponent Duolingo. Note that Babbel is also much less consumer in terms of data exchanged, 224 KB against 4.9 MB on the side of Duolingo.
For those who like numbers
|Application||Version||Downloads||Playstore Grade||App weight (MB)||Exchanged data (KB)||Memory consumption (MB)||Energy consumption (mAh)|
|Babbel||20.36.0||10 000 000+||4.5||90||0.224||147.9||17.2|
|Duolingo||4.37.1||100 000 000+||4.7||56||4.9||230.7||25.5|
On a 1-minute usage scenario, Babbel has a consumption equivalent to that of a video games app such as Candy Crush Saga. As for Duolingo, its consumption is similar to a browser app such as Opera Mini.(Source: Study Consumption of top 30 most popular mobile applications)
The measurements were carried out by our laboratory on the basis of a standardized protocol, respecting a specific user scenario (launch of the app, first lesson). The other scenarios are the launch of the application (20”), inactivity in the foreground (20”) and inactivity in the background (20”). This methodology makes it possible to estimate the embedded application complexity and its energy impact during the use phase.
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