A Comprehensive Review of Qwen and DeepSeek LLMs: Architecture, Performance and Applications

This study analyzes the mid-2025 LLM landscape by benchmarking both open-source and proprietary models across key dimensions including performance, computational efficiency, and operational cost, offering insights into current trade-offs and deployment strategies. This paper presents a comprehensive review of the Qwen and DeepSeek large language model families, analyzing their architectural innovations, performance characteristics, and practical applications within the broader AI landscape. Through systematic evaluation of recent research and industry benchmarks, we identify several key trends shaping open-source LLM development: the rise of hybrid mixture-of-experts architectures that dramatically improve inference efficiency, breakthrough techniques enabling cost-effective model customization, and growing parity between open-source and proprietary systems in specialized domains. The review compares model designs across multiple scales, from 7B to 235B parameters, highlighting unique approaches to memory optimization, context handling, and multi-task learning. Performance analysis reveals consistent strengths in mathematical reasoning and coding tasks, while identifying remaining gaps in creative generation and multilingual capabilities. Practical deployment considerations are examined, including local execution optimizations, document processing pipelines, and security vulnerabilities. Emerging impacts on industry verticals are discussed, with case studies demonstrating successful applications in finance, healthcare, and scientific research. The paper concludes with projections about the evolving LLM ecosystem, including the growing importance of specialized models, hardware-software co-design, and geopolitical dimensions of open-source AI development. This synthesis of technical literature and empirical results provides researchers and practitioners with a unified reference for understanding these influential model families and their role in advancing the field.