DeepSeek User Feedback: Significant Token Consumption Even Under Light Loads

Overview

Recently, some users have reported that when using DeepSeek and related AI Agent tools, the model consumes a large amount of tokens even for very simple tasks. This phenomenon has drawn attention to the model's resource scheduling mechanisms. Some users noted that under frameworks like Hermes Agent, single calls in low-load scenarios are often accompanied by disproportionate computational overhead.

Phenomenon Description: Heavy Consumption for Light Tasks

According to user test reports, both GPT and DeepSeek exhibited significant overconsumption of tokens when performing routine instruction configurations or simple Q&A using Hermes Agent. Specifically, after executing only a lightweight operation without submitting complex code or lengthy text, the processing volume recorded by the backend far exceeded expectations.

"Each time, even though nothing substantial was done, tokens were consumed heavily." — User feedback

This comparative experiment also revealed stability issues potentially occurring with GPT services during peak periods, such as request termination mid-process (Busy/Error). While DeepSeek did not experience interruptions, its resource utilization efficiency came under scrutiny as well. Users are concerned that if models cannot effectively streamline context for simple tasks, it will directly impact usage costs and response speeds.

Cause Analysis: Agent Architecture Overhead

Industry analysis suggests this phenomenon may be related to the operating mechanisms of Agent frameworks. Modern AI Agents do not merely invoke a base model once when executing a single instruction; instead, they may trigger multiple rounds of internal reasoning, tool-call planning, and context maintenance.

• Context Accumulation: Even for a single simple task, the system may retain historical dialogues or search results, leading to a large baseline of input tokens.
• Toolchain Invocation: Frameworks like Hermes tend to complete tasks through multi-step collaboration, where the "thinking" and "verification" processes in intermediate steps also consume additional computing power.
• Load Scheduling Strategies: In low-load scenarios, the model may not enter the optimal power-saving or streamlined mode, resulting in low resource utilization efficiency.

User Suggestions and Outlook

In response to the above feedback, the community has suggested that developers optimize Agent configuration logic, such as setting stricter maximum token limits or enabling lightweight reasoning modes. Meanwhile, some users have called on DeepSeek's official team to implement technical improvements regarding long-context management to reduce operational costs for high-frequency, light tasks.

Currently, this issue has sparked discussions across multiple technical forums, becoming an important case study for observing the performance of domestic large language models in practical application scenarios. As AI applications delve deeper into more niche Agent scenarios, balancing intelligence with resource efficiency will become a critical challenge the industry urgently needs to address.