## 5 Simple Task: Data Cleaning and Statistical Computation
### Intl Calculation  
We then applied the INFINITE methodology to compute the InI index and evaluate the performance of the three LLM frameworks: GPT, OAI1, and OAI3. To establish the parameters for assessing each framework's effectiveness, we tracked several key metrics, including the number of attempts required to obtain a working code snippet, the total number of queries submitted (regardless of whether an SB response or a valid answer was returned), the response times between query submission and model output, and the relative errors between the framework-generated predictions and the ground truth (R\({}^{2}\) is not pertinent in this case, as single values are predicted by the generated model). As ground truth, we used the statistical analysis results (mean, median, and standard deviation) obtained by the authors. Since all three frameworks produced identical outputs to the reference implementation, we assigned a MAPE value of 0% across all models. Notably, INFINITE does not incorporate an index for code style, as this aspect is subjective; instead, it strictly evaluates the accuracy of the generated code in fulfilling the specified task. The final results are presented in Table 3. \(ARTpQ\) is calculated using Equation (1).  
By substituting the values of Table 3 into Equations (2)-(8), we derived Table 4. For better visualization, InI is shown in Figure 3 for the three frameworks.  
Applying the INFINITE methodology, GPT and OAI3 both achieved the highest possible score of 1, while OAI1 scored 0.83 due to its significantly longer response time. Despite this difference, all three models demonstrated exceptional performance in the simple coding task, successfully generating correct and executable code on the first attempt without requiring any modifications. Given that all models produced identical results in terms of accuracy and functionality, response time and accessibility become key differentiating factors. From a practical standpoint, GPT emerges as the most accessible option, as it is freely available and does not require the same level of access restrictions as OAI1 or OAI3.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \(\mathbf{E_{SRR}}\) & \(\mathbf{E_{ART}}\) & \(\mathbf{E}\) & \(\mathbf{C}\) & \(\mathbf{A}\) & **InI** \\ \hline GPT & 1 & 1 & 1 & 1 & 1 \\ OAI1 & 1 & 0 & 0.5 & 1 & 1 & 0.83 \\ OAI3 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Efficiency, Consistency, Accuracy, and InIs for GPT, OAI1, and OAI3 performance, as determined through INFINITE methodology for data cleaning and statistical computation task.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Attempts Until Correct Answer** & **Total Queries** & **SB Responses** & \(ARTpQ\) (s)** & **Average MAPE (\%)** \\ \hline GPT & 1 & 1 & 0 & 7 & 0 \\ OAI1 & 1 & 1 & 0 & 91 & 0 \\ OAI3 & 1 & 1 & 0 & 7 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Important parameters collected during the execution of the procedure for generating code for data cleaning and statistical computation using GPT, OAI, and OAI3.  
This makes it the most convenient and widely usable choice for general users who need reliable coding assistance without additional constraints.