The fine-tuned models consistently outperform the non-fine-tuned base models across all tasks, demonstrating that fine-tuning can significantly enhance performance for specific tasks. Fine-tuned models also have the potential to be more cost-effective in the long run compared to using general-purpose models like GPT-4 or Llama-2 chat models, as they may require fewer tokens and thus lower costs during serving.