In the progression of integrating machine learning with chemical research, a pivotal enhancement is the utilization of LLM to automatically generate code for the practical implementation of ML models. Synthetic chemists are often not experts in data science and can have limited coding experience necessary for leveraging machine learning effectively. Consequently, methods that not only understand the chemical context but also automate the coding process needed to process and analyze data could be helpful to the chemistry community. LLMs such as Llama , GPT , and Claude 26 models offer a promising solution by generating executable code from natural language prompts, potentially lowering the barrier to computational techniques in chemistry and enhancing chemists' productivity in using ML models without extensive coding experience. Toward this end, as the first step, we developed a "prompt-to-code" framework and used it to evaluate the performance of different open-source and proprietary LLMs in tool-making and tool-using (Figure ). The core objective was to assess the reliability and accuracy of code produced by LLMs across four distinct tasks in the context of this study: (1) ML model training using a dataset on C-H oxidation, (2) development of code for tuning synthesis conditions and optimizing reaction yields, (3) interpretation of documentation and application of existing Python package for yield optimization, and (4) direct interaction with laboratory hardware to prepare solutions based on generated synthesis parameters (Figure ). These tasks were designed to span a range of practical applications, from data handling to physical lab automation, reflecting the diverse ways LLMs can implement code for ML to support chemical research (Figure ). Notably, previous evaluations of LLM code-writing performance have often relied on qualitative assessments by human reviewers and have usually been based on one-time conversations, which could introduce bias and did not account for the inherent variability and occasional inaccuracies (hallucinations) in LLM outputs. To address these issues, we developed a rigorous, quantitative benchmarking process using four Python-based code evaluators tailored for each task to not only check the executability of the code but also assess its correctness in a simulated environment (Section S5 of the Supporting Information). To this regard, 10 LLMs were chosen (Full list shown in Tables S12-14 of the Supporting Information) and each LLM was evaluated by repeatedly generating code for the same task 100 times independently, a robust sample size that mitigates performance variability and reflects the long-term reliability of each model across seven different LLMs.