Managing Function Calls With Reasoning Models

# Managing Function Calls With Reasoning Models OpenAI now offers function calling using [reasoning models](https://platform.openai.com/docs/guides/reasoning?api-mode=responses). Reasoning models are trained to follow logical chains of thought, making them better suited for complex or multi-step tasks. > _Reasoning models like o3 and o4-mini are LLMs trained with reinforcement learning to perform reasoning. Reasoning models think before they answer, producing a long internal chain of thought before responding to the user. Reasoning models excel in complex problem solving, coding, scientific reasoning, and multi-step planning for agentic workflows. They're also the best models for Codex CLI, our lightweight coding agent._ For the most part, using these models via the API is very simple and comparable to using familiar 'chat' models. However, there are some nuances to bear in mind, particularly when it comes to using features such as function calling. All examples in this notebook use the newer [Responses API](https://community.openai.com/t/introducing-the-responses-api/1140929) which provides convenient abstractions for managing conversation state. However the principles here are relevant when using the older chat completions API.