How to measure cost of models for programming

Recently, DeepSeek and OpenAI released their recent model refreshes for the mini sized models (Flash and Luna) with purported scores from DeepSWE at around GPT5.4, but its hard for people to understand how much they cost. Below is some basic math. A good rule of thumb is that if you're using say a subscription to codex luna will roughly 12-25x your total max usage. Roughly, you will get about 140Billion tokens a month via Codex 20x subscription. In real $ costs at about 7 billion tokens/month, luna is going to cost about $250. Comparatively, deepseek is going to $75 for 7 billion tokens. This is about 1/3rd Luna price. But you'd need to buy directly. If you're on a small budget of like $10-20/month, you can probably just use opencode go and get fairly decently far. To measure cost of a model, calculate cost of tokens versus distribution. The average distribution of your usage for programming is going to be 93/95% cached input tokens, non cached at about 2-4% and output at about 1-2%. The output increased proportional to if you're using max or not. The main reason DeepSeek flash is much cheaper is that the cache tokens are about 1/7th luna equivalent pricing, and about 1/5th the output tokens. If your use case is significantly different wherein you are mostly generating the tokens, and not using caching the distribution will change.