Open-source models publish weights and often training code; closed-source models expose only an API — each has trade-offs in transparency, cost, customization, and safety. Open-source models (like Llama and Mistral) enable local deployment, full customization, and community-driven improvements. Closed-source models (like GPT-4 and Claude) can offer stronger performance and safety guardrails but lock users into a provider. The debate extends to training data transparency, reproducibility, and whether open-sourcing powerful models accelerates beneficial or harmful uses.