CodeAgent

The Problem Nobody Talks About Every engineering team I’ve talked to in the past six months shares the same frustration: AI coding assistants are great, until you look at the bill. Let me give you a concrete example. We ran a React development task through a standard AI assistant setup. The task: implement a feature with proper error handling. The result? 20 minutes and 50,300 tokens consumed. For a single feature. In production, this compounds fast—multiplied across a team of ten engineers running dozens of sessions daily, you’re looking at serious API costs bleeding into your compute budget. ...

Introduction DeepSeek’s disk-based automatic context caching is famous for near 90% input token savings: cached prefix tokens cost just a tiny fraction of standard input pricing, with zero manual configuration required. Thousands of developers switch to DeepSeek chasing this aggressive discount for long system prompts, code rules, and repeated tool definitions. But a costly reality hits teams running ClaudeCode / OpenCode (code agent runtimes built for Anthropic-style cache_control) against the DeepSeek API: ...