According to God of Prompt on Twitter, recent research challenges the common belief that larger AI models are always superior for agentic tasks. Researchers fine-tuned a compact 350M-parameter model specifically for tool-using tasks, focusing solely on selecting the correct tool, passing arguments, and completing assignments. This model achieved a 77.55% pass rate on the ToolBench benchmark, significantly outperforming much larger models—such as ChatGPT-CoT (26%), ToolLLaMA (around 30%), and Claude-CoT (not competitive). The study demonstrates that large models, designed to be generalists, often underperform in specialized, structured tasks due to diluted parameter focus. In contrast, smaller models with targeted fine-tuning excel in precision and efficiency for agentic applications. This finding signals a shift in business strategy for AI deployment: companies can leverage smaller, task-specific models that are cheaper, faster, and more reliable for agentic tool calling, reducing operational costs and improving robustness. The future of agentic AI systems may lie in orchestrating multiple specialized models rather than relying on monolithic generalists (Source: God of Prompt, Twitter, Dec 22, 2025).