How do the Assistant API and custom GPTs differ in function calling and development workflow?

Below is a concise explanation:

Similarities and Differences

• Underlying Models & Capabilities
  Both the Assistant API and custom GPTs in the UI are ultimately built on OpenAI’s language models. They allow you to customize behavior (via instructions) and to call functions or tools. In that sense, many underlying capabilities are similar.

• Interface & Integration
  The Assistant API is designed as a programmatic endpoint. You configure it by sending a JSON structure that includes instructions, tool definitions, and other parameters. It’s optimized for server-to-server interactions and can streamline iterative development if you’re comfortable working directly with API responses.

  In contrast, custom GPTs in the UI are set up through a user interface that requires defining an OpenAPI specification for function calling. This additional layer is intended to expose functions in a structured, discoverable way for end users.

• Development Trade-offs
  Since trial and error on the UI can be slow, many developers may prefer working directly with the Assistant API during initial development for faster iteration. Once you’ve nailed the final prompt and function calls (in a JSON-like structure), you might then translate or adapt them into the OpenAPI spec format required by the custom GPTs in the UI.

Conclusion

They’re not exactly the same—they offer similar functionalities but differ in how you define and interact with function calling (JSON-based vs. OpenAPI spec) and in the intended integration workflows. So while you can iterate quickly using the Assistant API and later adapt settings for custom GPTs in the UI, you’ll need to account for the differences in structure and integration points.