Verified75% confidenceFactExact time
Long conversations package and resend the entire context history to the model, which is counted toward the token usage of each request; the longer the conversation, the more tokens a single request consumes.
5
Sources
75%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
UnverifiedChat Memory simulates memory by injecting historical messages into the Prompt with each request, causing token consumption to grow linearly with the number of conversation turns.83% similarUnverifiedThe context window mechanism causes models in multi-turn conversations to feed the entire conversation history into the context, causing token consumption to grow rapidly as the conversation lengthens.83% similarUnverifiedIn multi-turn conversations, all previous context is resent as input each time, so redundant code generated early on continues to incur token costs in every subsequent round.77% similarUnverifiedIn agent scenarios, due to multiple rounds of tool calls, each call must carry the previous conversation history and system prompt, causing input tokens to grow cumulatively, and a code refactoring task may consume tens of thousands or even hundreds of thousands of tokens.76% similarUnverifiedThe so-called conversational memory is essentially the application layer concatenating historical messages into the prompt before submitting it to the model, with state management handled by the client or an intermediate layer.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/523886API
curl https://kongchang.com/api/v1/knowledge/claims/523886MCP
get_claim(id=523886)