Cost control pain points
In long-term development, the difference in Token cost of different AI models can be up to 5-10 times.Plandex's flexible model switching mechanism can target to solve this problem.
Implementation steps
- benchmarking: start with
set-model gpt-4-turboCompleting the core logic design, logging task elapsed time and token usage - hierarchical use: Switch to
deepseek-v3Handles template code generation (cost reduction 80%), retains higher order models to handle complex algorithms - Localized Deployment: Run through Docker Compose
./start_local.shAfterwards, a mix of open source models (e.g. Llama3) can be used to further control costs - usage monitoringCloud hosted version offers $20 credit alerts per month, comes with API key users can monitor in real time via OpenRouter dashboard
caveat
- start using
--light-contextThe mode loads key files (e.g.plandex load core/ --light) Reduce non-essential token consumption - Prioritize tree-sitter native parsing over AI models for low cognitive load tasks such as grammar checking
- run
plandex optimizeAutomatically clean up stale contexts
This answer comes from the articlePlandex: an open source AI coding assistant with support for ultra-long contextsThe
May not be reproduced without permission:AI productivity tools " How to Improve Cost Control for Collaborative Multi-Model Development with Plandex?
