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Swapping LLMs isn’t plug-and-play: Inside the hidden cost of model migration
Based on hands-on comparisons and real-world tests, this guide unpacks what happens when you switch from OpenAI to Anthropic or Google’s Gemini and what your team needs to watch for.
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. Based on hands-on comparisons and real-world tests, this guide unpacks what happens when you switch from OpenAI to Anthropic or Google’s Gemini and what your team needs to watch for.
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