GPT-5.6 Luna vs. DeepSeek V4 Flash: Cache Hits and Real-World Task Costs

Summary

The discussion centers on whether “Luna outperforms DeepSeek V4 on performance and cost.” The original poster emphasizes a 99.9% cache hit rate on long tasks; respondents say Luna may use fewer tokens and run faster, but consume roughly 2–3 times as many dollars as DeepSeek. Another user believes DeepSeek remains cheaper after the price change, while Luna is stronger on coding benchmarks. The conclusion depends heavily on cache rate, subscription quotas, peak and off-peak pricing, and the harness.

Original article

GPT 5.6 Luna has been the most discussed model for developers that want to switch away from deepseek after the API price… This is a necessary excerpt; read the original source for full context.

Assessment

This is a useful community discussion for reminding readers not to compare only public list prices. For long-context workflows such as Tabbit’s, cache hit rate, first-pass success rate, total tokens, wall-clock time, and the cost per acceptable result should be measured in practice.