A Comparative Analysis of Large Language Models

Large Language Models (LLMs) are widely used for question answering, summarisation, coding, and research support, yet choosing a suitable model remains difficult because models differ in performance, context capacity, cost, privacy, and deployment options. This paper presents a document-based comparative analysis of five LLMs: GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3.1 405B Instruct, and Mistral Large 2. Using published technical reports, official documentation, model cards, and recognised benchmark studies, the models are examined under a common framework covering response quality, reasoning, coding, language understanding, context-window capacity, speed, computational requirements, cost, privacy, and deployment flexibility. No new experiments are conducted, and results obtained under different evaluation conditions are not treated as directly equivalent. The analysis shows that model selection involves trade-offs: hosted models offer easier access, whereas models with downloadable weights give greater deployment control but require more computing resources. No single model is found to be best for every task, which supports a task-specific approach to selecting LLMs.