Open-source Large Language Models (LLMs) are transforming code intelligence by enabling developers to automate tasks such as bug detection and code optimization, although they face challenges in competing with proprietary models like OpenAI's GPT-4 due to resource constraints and dataset limitations. Initiatives such as DeepSeek-Coder and Qwen2.5-Coder are pivotal in democratizing access to these technologies, offering robust models with advanced features like repository-level training and Fill-In-the-Middle (FIM) techniques for improved code completion. DeepSeek-Coder is noted for its extensive multilingual support and long-context handling, while Qwen2.5-Coder excels in tokenization and context management, achieving competitive performance on code-specific benchmarks. Despite their promise, open-source models struggle with scalability and performance parity with proprietary systems, but they continue to evolve through collaborative efforts, focusing on bridging these gaps. The open-source community aims to refine these models for ethical and accessible AI applications, with a focus on specialized use cases and enhanced fine-tuning techniques to align with real-world coding challenges.