This year, we've seen some remarkable leaps in the world of Large Language Models (LLMs). Models like O1, GPT-4o, and Claude Sonnet 3.5 have shown how far LLM capabilities have come, pushing the boundaries of coding, reasoning, and self-reflection. O1, in particular, is one of the best models on the market, known for its self-reflection capabilities, which allows it to iteratively improve its reasoning over time. GPT-4o offers a wide range of capabilities, making it incredibly versatile across tasks, while Claude Sonnet 3.5 excels at coding, solving complex problems with higher efficiency.
What many people don’t realize is that these high-performing models are essentially fine-tuned versions of underlying models. Fine-tuning allows these models to be optimized for specific tasks, making them more useful for things like analysis, coding, and decision-making
