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dsh-session-cost

Client Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install KIDLi1412/dsh-session-cost

Paste the following prompt into your AI chat to install this plugin:

To install this plugin in DeepSeek Harness, run the command `dsh plugin install KIDLi1412/dsh-session-cost` with the full source code at https://github.com/KIDLi1412/dsh-session-cost.

About this plugin

When using the DeepSeek API for conversations, users often struggle to track token consumption, costs, and API balance in real time, which hampers effective budget management. The dsh-session-cost plugin solves this by seamlessly integrating key cost information into the chat interface, making expenses transparent and accessible.

Its core capability lies in real-time estimation of session costs and display of API balance. It calculates costs based on token usage from session logs, applying model-specific pricing that accounts for peak and off-peak rates to ensure accuracy even in multi-model sessions. Balance information is fetched via the official API with caching to minimize requests, and all data is prominently shown in the statistics bar below the input box for easy monitoring at a glance.

The plugin also offers practical features like low balance alerts, manual refresh, and detailed per-model cost breakdowns, helping users manage their API budget effectively. Whether you're a developer, researcher, or any frequent user of the DeepSeek API, this tool enhances efficiency by keeping expenses visible and controllable. In summary, dsh-session-cost demystifies API costs, allowing you to focus on your conversations without worrying about overspending.

Screenshots

Use Cases

  • View real-time token cost estimates during conversations to track per-turn expenses.
  • Monitor DeepSeek API balance and set low balance alerts to prevent service interruptions.
  • Optimize API usage based on model pricing and peak/off-peak periods to save costs.

Best For

  • Programmers and developers using the DeepSeek API for development.
  • Researchers and data scientists needing to finely manage AI service costs.
  • DSH Web users who frequently engage in multi-turn conversations and are budget-conscious.