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Using multiple LLMs helps you leverage different strengths and catch model-specific failures. But proceed with caution. Synthesizing answers often masks critical dissent or risks leaking data across vendors. It is not a silver bullet for reliability
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Why mix models? One size fits none. You get different strengths and unique failure modes. It’s a smart hedge, but watch out—automated synthesis can hide meaningful dissent or leak data across providers
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We are moving past the "one model to rule them all" phase. Routing to multiple models makes sense because they have different strengths and failure modes. You might use a fast model for classification and a smart one for logic. But tread lightly
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Everyone is jumping on the multi-model bandwagon, but does it solve problems or just double your API bill? Combining models lets you leverage specific strengths, cross-check outputs for hallucinations, and handle different failure modes