Scilab vs Julia
Side-by-side comparison for macOS
Scilab
7.0Software for numerical computation
Julia
8.0Programming language for technical computing
| Metric | Scilab | Julia |
|---|---|---|
| Category | Science | Developer Tools |
| AI Score | 7.0 | 8.0 |
| 30-day Installs | 32 | 88 |
| 90-day Installs | 82 | 215 |
| 365-day Installs | 357 | 1.1K |
| Version | 2026.0.1 | 1.12.6 |
| Auto-updates | No | No |
| Deprecated | No | No |
| GitHub Stars | 121 | 48.6K |
| GitHub Forks | 50 | 5.8K |
| Open Issues | - | 4.7K |
| License | — | MIT |
| Language | Scilab | Julia |
| Last GitHub Commit | 4y ago | 1mo ago |
| First Seen | May 26, 2014 | Jun 23, 2013 |
Reviews
Scilab
Scilab is a powerful open-source tool for numerical computation, offering a comprehensive library for engineering and scientific tasks. Its ability to handle complex mathematical operations makes it a valuable resource for engineers and scientists seeking a robust, free alternative to commercial software.
Scilab performs numerical computation and analysis for engineering and scientific applications.
Pros
- + Open-source and free to use
- + Extensive library for numerical computations
- + Cross-platform compatibility
Cons
- - Lack of auto-updates
- - Limited recent community activity
Julia
Julia is a high-performance programming language designed for technical computing, data science, and machine learning. It offers a unique blend of high-level language features and speed, making it ideal for researchers and developers who need both productivity and performance.
Julia provides a programming environment for technical computing, data analysis, and machine learning.
Pros
- + High performance for numerical and technical computing
- + High-level, user-friendly syntax
- + Strong community and ecosystem for data science and machine learning
Cons
- - No auto-update feature
- - Some syntax changes may cause breaking issues