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Swapping LLMs isn’t plug-and-play: Inside the hidden cost of model migration
Swapping large language models (LLMs) is supposed to be easy, isn’t it? After all, if they all speak “natural language,” switching from GPT-4o to Claude or Gemini should be as simple as changing an API key… right? In reality, each model interprets and responds to prompts differently, making the transition anything but seamless. Enterprise teams […]
Enterprise teams who treat model switching as a “plug-and-play” operation often grapple with unexpected regressions: broken outputs, ballooning token costs or shifts in reasoning quality. This story explores the hidden complexities of cross-model migration, from tokenizer quirks and formatting preferences to response structures and context window performance. However, from a machine learning (ML) practitioner’s viewpoint, making model choices and decisions based on purported per-token costs can often be misleading.
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