Unverified60% confidenceFactExact time
The larger the context, the more input Tokens consumed
2
Sources
60%
Confidence
Long-term
Relevance
8/2/2026
First Seen
Sources
Related Entities
Related Claims
Unverified经过 Attention 处理后得到的向量称为 Contextualized Embedding，能结合上下文让相同 Token 根据语境得到不同表示70% similarUnverifiedIn-context conditioning concatenates the text condition and image tokens into the same sequence, eliminating the cross-attention module, but it lengthens the sequence and increases the amount of attention computation.70% similarUnverifiedContext is the upper limit of information the model can "see" in each response, measured in tokens, with roughly one Chinese character corresponding to one to two tokens; when the conversation grows longer or files grow larger, earlier information is pushed out of the window, causing the model to forget.69% similarUnverified语义缓存通过将用户请求转化为向量嵌入，在向量空间中计算语义相似度，将语义相近的问题映射到同一缓存结果，从而减少模型调用次数和Token消耗68% similarUnverifiedLarge language model APIs are generally billed by the total number of input plus output tokens, and re-injecting historical context on every call in long projects causes token consumption to balloon rapidly.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/680160API
curl https://kongchang.com/api/v1/knowledge/claims/680160MCP
get_claim(id=680160)