Swapping LLMs isn’t plug-and-play: Inside the hidden cost of model migration
1 min read
Summary
OpenAI, Anthropic, and Google’s Gemini are among the many large language models (LLMs) on the market today, and switching between them is not as simple as it seems.
Each model uses different tokenization strategies, prefers different formatting, and has various response structures, so migrating between them is not straightforward.
Tokenization costs can be misleading, and different models perform differently depending on the context window, so it’s important to consider these variables ahead of time.
Developers should be aware of the formatting preferences and response structures of each model and refine their prompts accordingly to ensure a smooth transition.
Major companies like Google, Microsoft, and AWS are working on tools to help manage multiple LLMs, including flexible model orchestration and robust prompt management.
In order to effectively migrate models in the future, it is important to invest in robust evaluation frameworks, maintain documentation of model behaviors, and collaborate closely with product teams. GPT-4, Claude, and Gemini are all popular large language models (LLMs) that have gained a lot of traction in the AI community.