A Comparative Survey of Large Language Models: Foundation, Instruction-Tuned, and Multimodal Variants

The rapid evolution of large language models (LLMs) has transformed natural language processing, enabling machines to perform complex language understanding, generation, and reasoning tasks with unprecedented fluency and adaptability. This survey presents a comprehensive comparative analysis of three major classes of LLMs: foundation models, instruction-tuned models, and multimodal variants. We first define and contextualize each category—foundation models as the general-purpose pretrained backbones, instruction-tuned models as task-optimized derivatives guided by human or synthetic instructions, and multimodal models as those extending language understanding to vision, audio, and other modalities. The paper examines architectural innovations, training methodologies, benchmark performances, and real-world applications across these model types. Through systematic comparison, we highlight the trade-offs in generality, alignment, efficiency, and modality integration. We further discuss deployment trends, ethical considerations, and emerging challenges, offering insights into the future trajectory of unified, scalable, and human-aligned language models. This survey aims to serve researchers and practitioners by clarifying the landscape and guiding informed decisions in the design and application of LLMs.