Top IDE index

The Top IDE Index is created by analyzing how often IDEs' download page are searched on Google

The more an IDE is searched, the more popular the IDE is assumed to be. The raw data comes from Google Trends.

If you believe in collective wisdom, the Top IDE index can help you decide which IDE to use for your software development project.


Worldwide, Apr 2026 :

Rank
ChangeIDEShare1-year trend
1Visual Studio24.44 %-4.1 %
2Visual Studio Code12.2 %-3.2 %
3Cursor12.07 %+11.7 %
4Android Studio9.85 %-1.3 %
5Eclipse8.88 %-1.2 %
6pyCharm6.2 %-3.9 %
7IntelliJ5.71 %-1.4 %
8Antigravity4.79 %+4.8 %
9Xcode3.27 %+0.3 %
10RStudio2.42 %-0.4 %
11NetBeans2.37 %-1.2 %
12Atom2.23 %+0.3 %
13Sublime Text1.48 %-0.9 %
14Code::Blocks1.26 %-0.3 %
15Light Table0.85 %+0.8 %
16Vim0.75 %-0.1 %
17Komodo0.33 %+0.2 %
18Emacs0.25 %+0.0 %
19Qt Creator0.23 %-0.1 %
20PhpStorm0.21 %-0.1 %
21geany0.1 %-0.0 %
22Xamarin0.04 %+0.0 %
23Eric Python0.01 %+0.0 %
24RAD Studio0.01 %-0.0 %
25JDeveloper0.01 %-0.0 %
26Aptana0.01 %+0.0 %
27Coda 20.01 %+0.0 %
28Monkey Studio0.0 %+0.0 %
29JCreator0.0 %-0.0 %
30SharpDevelop0.0 %+0.0 %
31RubyMine0.0 %+0.0 %
31Julia Studio0.0 %+0.0 %
33MonoDevelop0.0 %+0.0 %
34DrJava0.0 %+0.0 %
© Pierre Carbonnelle, 2023


PYPL Index

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This chart uses a logarithmic scale. It can show your favorite IDE in a country



PYPL Index

FAQ

The Top IDE Index is created by analyzing how often Integrated Development Environment (IDE) downloads are searched on Google : the more an IDE is searched, the more popular the IDE is assumed to be. The raw data comes from Google Trends.

If you believe in collective wisdom, the TOP IDE Top IDE index can help you decide which IDE to use for your software development project.

The following principles were used:

    • just the IDE name, e.g. Atom, would lead to inconsistent results, because Atom has many other meanings;
    • the same search phrase should be used for all IDE, for consistency.
    • the number of "download" search is a good leading indicator.

Some IDE are not listed because their name has other meanings, or because there is not enough data for them on Google Trends.

We export the data from Google Trends in CSV format, [s] and analyze it with python's pandas. We first calculate the interest of each IDE relative to Visual Studio every month. Normalizing the total to 100% yields the share of interest in each IDE, i.e. their popularity, which we smooth over 6 months.
Contact © Pierre Carbonnelle, 2023, CC BY 3.0.