Which AI Code Assistance Tool is Best? GitHub Copilot, Tabnine, Replit Ghostwriter, or Codeium?

There are a couple of AI-powered code assistants out there, and maybe you have tried a few of them. Maybe one of them is your favorite and you might still be in search of a suitable AI coding assistant for you. It is hard to find the best one due to many edited examples of PR purposes. But your search is over today.

We will do a quick analysis of four leading AI code assistants such as Github Copilot, Tabnine, Replit Ghostwriter, and Codeium with distinctive perspectives. Hence, you can find the best one for you that aligns with your requirements. We will compare all of them based on features, prizes, latency, and quality.

Price:

The price of all four leading AI code assistants is given in the following:

 Functionality:

Supported IDEs:

Github Copilot, Tabnine, Replit Ghostwriter, and Codeium offer distinctive supported IDEs that are:

• Github Copilot facilitates with VS Code, Visual Studio, Vim/Neovim, and JetBrains for their supported IDEs.
• Tabnine Pro facilitates with VSCode, JetBrains, Neovim, Eclipse, and Sublime Text for supported IDEs.
• Replit Ghostwriter only facilitates with Replit for supported IDEs.
• However, Codeium facilitates multiple supported IDEs that include VSCode, JetBrains, Visual Studio, Jupyter / Colab / Deepnote / Databricks Notebooks, Vim / Neovim, Emacs, Eclipse, Sublime Text, VSCode Web IDEs (ex. Gitpod), and Chrome Extension.

Overall, Codeium offers maximum supported IDEs as compared to other AI coding assistants.

Supported Coding languages:

Each model offers different coding languages that make them best for different developers as per their preferred coding languages.

• Github Copilot offers assistance in the coding languages of C, C++, C#, Go, Java, JS, PHP, Python, Ruby, Scala, TS, and many others but with potentially lower-quality codes.
• Tabnine Pro offers assistance in coding languages of C, C++, C#, CSS, Dart, Go, Haskell, Java, JS, Kotlin, Perl, PHP, Python, Ruby, Rust, Scala, and TS.
• Replit Ghostwriter has supported languages of Bash, C, C++, C#, CSS, Go, HTML, Java, JS, PHP, Perl, Python, R, Ruby, Rust, and SQL.
• Codeium offers the supported languages of Assembly, C, C++, C#, Clojure, CMake, CoffeeScript, CSS, CUDA, Dart, Delphi, Dockerfile, Elixir, F#, Go, Groovy, Haskell, HCL, HTML, Java, JavaScript, Julia, JSON, Kotlin, LISP, Less, Lua, Makefile, MATLAB, Objective-C, PHP, Protobuf, Python, Perl, Powershell, R, Ruby, Rust, Sass, Scala, SCSS, Perl, Powershell, R, Ruby, Rust, Sass, Scala, SCSS, shell, Solidity, SQL, Starlark, Swift, Typescript, TSX, VBA, Vue, YAML, and few more but with comparatively lower quality of codes.

Security & Privacy Policies

• Github Copilot:

It allows you to opt out of code snippet telemetry as you can stop it to collect data about the code snippets you are using. It will also imperatively reduce how often our code matches with public code. Hence, protects our privacy.  

• Tabnine Pro:

Tabnine Pro never trains its generative model on your private code, unless you are using the enterprise version. It follows SOC2 compliance standards as its best security and privacy criteria. This AI assistant does not train on not permissively licensed code.  

• Replit Ghostwriter:

The security and privacy policy of Replit Ghostwriter is still unclear. As, their security practices aren’t well documented and not clear.

• Codeium:

For Codeium, you are opt-out for code snippet telemetry. It doesn’t use your code for training its model. This coding assistance follows SOC2 compliance standards as Tabnine pro. It doesn’t train on non-permissively licensed code also.  

However, all of this information and comparison is based on going off company claims. Different functionalities and features work perfectly for their own model. You need to opt for what is best for you.  

Latency in Code Completion Tools

Latency is crucial for Code completion and it affects the working flow of a programmer. Users require suggestions about the code within 150ms to maintain the flow and productivity. However, many large models facilitate their users with high-quality suggestions but a bit slower. However, latency not only correlates with quality as there can be many differences in model architecture, training of data, and optimizations. Moreover, factors like network overhead and client-side aching also influence user-perceived latency.   

However, all of these models are fairly compared with standard coding tasks such as creation of a linked list class in Python, adding nodes, searching for data, and writing a test.

GitHub Copilot:

Github Copilot sometimes missed a few expected suggestions that increased time spent cycling by providing options. However, it was generally quick and few latency issues with multi-line functions were witnessed.

Tabnine:

Tabnine coding tool was quite fast but it suggested unexpected code outside the current method scope. It is a hybrid model but its potential network latency is reducing.  

Replit Ghostwriter:

The Replit Ghostwriter showed reasonable latency as compared to others, but it works slower while building functions line-by-line.

Codeium:

Codeium shows similar issues as Copilot and few missed suggestions in expected places. It has the potential to complete accurate multi-line code with reasonable latency.

Quality in Code Completion Tools

Quality is subjective but crucial; a fast but useless feature is not helpful. To avoid bias, we tested each tool using handpicked examples from their front pages, covering various languages and tasks:

• GitHub Copilot: External APIs (TypeScript)
• Tabnine: Machine Learning (Python)
• Replit Ghostwriter: Boilerplate/Unit Tests (JavaScript)
• Codeium: General Performance

GitHub Copilot:

• External APIs (TypeScript): Generated multiple functioning method bodies, cycling between options smoothly.
• Databases (Go/SQL): Accurate and bug-free, requiring no edits.
• Machine Learning (Python): Some issues with sklearn imports, but overall good results.
• Boilerplate/Unit Tests (JavaScript): Struggled with simple boilerplate, requiring more manual work.

Tabnine:

• External APIs (TypeScript): Suggested irrelevant localhost URLs and unnecessary fields.
• Databases (Go/SQL): Failed to understand table semantics, with incorrect fields and missing error handling.
• Machine Learning (Python): Import issues with sklearn and used incorrect imports.
• Boilerplate/Unit Tests (JavaScript): Added irrelevant test cases and had scope issues.

Replit Ghostwriter:

• External APIs (TypeScript): Needed explicit prompts for certain tasks.
• Databases (Go/SQL): Suggested invalid queries and needed manual changes.
• Machine Learning (Python): Import issues and incorrect assumptions about data.
• Boilerplate/Unit Tests (JavaScript): Small mistakes but generally clean.

Codeium:

• External APIs (TypeScript): Worked as expected with error handling.
• Databases (Go/SQL): Missed some aggregations but mostly accurate.
• Machine Learning (Python): Avoided import issues and provided expected results.
• Boilerplate/Unit Tests (JavaScript): Missed semicolons but otherwise accurate.