Unverified60% confidenceFactExact time
Agent-type tasks use the ReAct think-act-observe loop, and a single user task may trigger dozens or even hundreds of model calls, causing computational overhead to grow non-linearly.
2
Sources
60%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
GPT-5.6发布即预警：算力瓶颈才是大模型真正的考验
Related Claims
VerifiedIn agentic scenarios, an Agent often initiates multiple rounds of model calls within a single user request, with each round carrying an increasingly longer context, causing costs to grow nonlinearly with conversation depth.79% similarUnverifiedIn Agent scenarios, each tool call requires the full context to be re-input into the model, causing token consumption to grow exponentially.77% similarUnverifiedAutonomous Agent（如ReAct架构）依赖大模型在运行时动态决定下一步调用哪个工具，灵活性强但不确定性高，Token消耗更大76% similarVerifiedA complex Agent task may require dozens or even hundreds of model calls76% similarUnverifiedIn the agent scenario, the cost accounting metric shifts from "how much per thousand tokens" to "how much per successful task", because an agent task may consume tens of thousands of tokens and trigger multiple API calls.76% similar
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