Unverified50% confidenceSolutionExact time
In complex Agent tasks, multi-turn conversations and tool calls rapidly accumulate context length, directly affecting API costs and response speed; token costs can be managed through strategies such as conversation compression and summary caching.
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50%
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Long-term
Relevance
9/24/2026
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Related Claims
UnverifiedIn 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.79% similarUnverified上下文缓存机制对多轮对话、长文档处理和批量任务等场景尤为有利77% similarUnverified程序化工具调用相比传统 ReAct 框架能降低 Agentic 工作流的 Token 消耗和延迟，在串联数十次工具调用的复杂任务中效率提升显著74% similarUnverifiedThe platform measures usage by token consumption rather than conversation turns; the longer the prompt, the more context, and the larger the model, the faster tokens are consumed.74% similarVerifiedBy mapping task complexity to the corresponding model tier through a Hybrid Routing strategy, overall API expenditure can be reduced by 30% to 60%.74% similar
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