Unverified50% confidenceFactExact time
The LLM-as-judge evaluation approach requires consuming tokens to generate the answer to be evaluated, and then consuming additional tokens to have another model score it, resulting in double or even multiple times the token overhead
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Long-term
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8/31/2026
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Related Claims
UnverifiedEach model invocation calculates both input tokens and output tokens simultaneously, and both together determine the cost of a single call.73% similarUnverified对于大语言模型推理，吞吐通常以 token 数为单位衡量，即 tokens/s 是评估 LLM 推理框架性能的核心指标72% similarUnverifiedMainstream LLMs charge for tokens in two categories—input tokens and output tokens—and the longer the context, the higher the cost per call.67% similarUnverifiedTop-tier models employ techniques such as chain-of-thought or extended reasoning, generating a large number of intermediate tokens during inference, causing per-call token consumption to be several times that of ordinary models.67% similar
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