Unverified50% confidenceFactExact time
In scenarios like Devin that execute tasks autonomously, a programming task often includes multiple rounds of model calls (reading files, generating patches, running tests, modifying based on errors), and the actual token consumption may reach several times or even dozens of times that of a single-round Q&A.
1
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50%
Confidence
Long-term
Relevance
10/13/2026
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Related Claims
UnverifiedIn long-chain, multi-iteration autonomous agent tasks, models repeatedly read and write large volumes of conversation history for self-verification, error recovery, and context maintenance, causing token consumption to far exceed single-turn Q&A scenarios (token inflation).76% similarUnverifiedReasoning models and verbose system prompts can significantly change the actual number of tokens consumed by a task.76% similarUnverifiedThe recurrent architecture can dynamically determine the number of iterations based on task difficulty, achieving a decoupling of computation and parameters.73% similarUnverifiedIn multi-turn tool-calling agentic scenarios, system prompts occupying thousands of tokens compress the space available for intermediate reasoning steps and tool return results, and hitting the token limit causes task failure.73% similarUnverified'One-shot' delivery does not mean a single inference pass; the user issues only one task, but the agent internally executes thousands of loops of checking, reasoning, modification, simulation, and validation.73% similar
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