As AI coding agents become mainstream, managing their operational costs is a growing concern. This case study details how one developer refactored their OpenCode and DeepSeek configuration to prioritize cost and token efficiency. The core goal was to maintain development speed while minimizing context window usage. Key strategies include using a dual-model approach (Pro for complex tasks, Flash for simpler ones) and carefully tuning system prompts and context management. The post walks through the reasoning behind each decision, offering a replicable framework for teams using similar tools. While specific to OpenCode and DeepSeek, the principles of cost-aware configuration design apply broadly to any AI-assisted development setup. Teams looking to optimize their AI tooling spend will find actionable insights here.
A practical case study on optimizing OpenCode and DeepSeek configurations to reduce token usage and costs while maintaining development efficiency.