Verified65% confidenceFactExact time
Under a linear conversation structure, token consumption is cumulative—every request requires retransmitting the entire history, increasing inference latency and driving up API call costs.
3
Sources
65%
Confidence
Long-term
Relevance
8/24/2026
First Seen
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Related Claims
Unverified随着对话轮次增加，每轮需携带完整历史上下文，输入token数量持续膨胀，导致推理时间呈现非线性增长80% similarUnverifiedBecause every conversation turn re-reads all prior content, the longer a session runs, the more expensive each new message becomes—costs accumulate with session length rather than instruction length.78% similarVerifiedRepeatedly stuffing redundant conversation history into the context window leads to increased token costs and higher inference latency.78% similarUnverifiedEach inference by a large model consumes compute, long conversations accumulate context, and cost rises with the number of turns and text length; additional features such as web search and file parsing also incur extra overhead.77% similarUnverifiedLarge models are stateless; in multi-turn conversations the client must resend the complete history of messages with each request, causing the input length of the Nth turn to be roughly the accumulation of all messages from the previous N-1 turns, so input cost grows quadratically with the number of turns.76% similar
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