Unverified50% confidenceFactExact time
Mainstream model APIs charge separately for input tokens and output tokens, where input includes system prompts, context history, and retrieved document fragments
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/6/2026
First Seen
Valid until: 1/4/2027
Sources
Related Entities
Related Claims
UnverifiedMainstream large model APIs charge separately for input tokens and output tokens, with input tokens typically including the entire conversation history (i.e., the context).78% similarUnverifiedThe core insight of context engineering is that model output quality is strongly correlated with the content of the input context window, including code snippets, file structure, conversation history, tool call results, and externally retrieved documents.74% similarUnverified在Cursor使用场景中，发送给模型的上下文包括编辑器自动附加的代码上下文、文件结构信息和项目索引数据，隐含Token消耗往往是用户可见输入的数倍甚至数十倍73% similarUnverifiedManually implementing simple models with tensor operations before using high-level APIs helps in understanding what high-level abstractions such as nn.Module, DataLoader, and optimizers encapsulate.72% similarUnverifiedThe context window refers to the complete set of input and output tokens visible to a large language model at any given moment, consisting of input tokens plus output tokens.71% similar
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