CommandCodeAI / CommandCodeAI/command-code

something seems not correct with ds v4 flash caching

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描述

Summary

Hi Command team,

Could you please help check whether there is an issue with DeepSeek V4 Flash caching or upstream routing on the Command servers?

My GOAT usage has increased unusually fast over the last couple of hours, and I hit the $35 weekly limit much faster than before, even though I have only been using deepseek/deepseek-v4-flash for past few hours and my workflow has not changed significantly.

One recent request from my usage dashboard shows:

  • Input tokens: 357,723
  • Output tokens: 120
  • Charged: $0.0783
  • Model: deepseek/deepseek-v4-flash

At the current off-peak DeepSeek V4 Flash pricing, 357,723 fresh input tokens × $0.22/M is approximately $0.0787, which is almost exactly what I was charged.

This seems to suggest that nearly the entire input was billed as fresh/cache-miss input rather than cache-read input.

This is unusual for my workload. When I previously used the DeepSeek API directly for the same type of long-running agent workflow, my prompt cache hit rate was typically around 95–98%. For example, on one day I had about 758M cached input tokens versus only 13.4M uncached input tokens.

Could you please check whether there has recently been any issue with:

  • DeepSeek V4 Flash prompt caching
  • upstream provider/server routing or cache affinity
  • cache keys being invalidated between consecutive agent requests
  • any changes related to the recent DeepSeek V4 Flash / pricing update

I’ve attached screenshots of both my Command usage and my previous DeepSeek API usage for comparison.

If possible, could you also check the trace IDs from my recent requests and confirm whether those requests were actually receiving prompt-cache hits?

Thanks!

Image
Expected Behavior

n.a.

Actual Behavior

n.a.

Steps to reproduce the issue

n.a.

Command Code Version

claude code

Operating System

macOS

Terminal/IDE

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Shell

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Session file (optional)

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Fix prompt (optional)

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Additional context

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研究方向

首先檢查附加的使用情況儀表板證據,以及報告中提到的近期 request trace IDs。將計費的輸入與預期的 cache-read 行為進行比較,並驗證 DeepSeek V4 Flash requests 是否獲得了 prompt-cache hits;完成的標準是確認 routing 或 caching 原因,或用證據排除該原因。

由索引模型根據 Issue 內容生成。

評估

領域
ai, backend
Issue 類型
缺陷
難度
4/5
預估耗時
3-5 天
活躍度
活躍
描述清晰度
需要釐清
新手友好度
28/100

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