Unverified70% confidenceSolutionExact time
Breaking large tasks into multiple independent smaller tasks and resetting the context window with each new session is one of the most effective methods for controlling single-session token consumption.
2
Sources
70%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
Codex超高推理模式隐藏成本：一个任务耗尽70%额度的真相
redditr/OpenAI7/12/2026
Related Claims
UnverifiedMulti-agent orchestration overcomes the context window length limitation of a single large language model by introducing a master agent responsible for task planning and decomposition, then distributing the work to multiple execution agents for parallel processing78% similarVerifiedA workflow chains multi-step tasks into an automated process that can be executed repeatedly.76% similarUnverifiedThis methodology emphasizes opening a new context window for each task implementation to avoid context contamination and save tokens and time.75% similarUnverifiedWork模式采用智能体式任务执行架构，基于ReAct等框架循环思考调用工具，多步任务token消耗可达普通对话的20-50倍74% similarUnverifiedMulti-agent architectures allow each agent's prompts and fine-tuning data to be highly optimized for a single task type, avoiding the attention dilution problem that general-purpose assistants face when handling diverse tasks.74% similar
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