Google says Gemini 3.8 Flash keeps the same introductory input and output prices as 3.7 Flash, but 3.8 may take extra reasoning steps and call tools more often on difficult work. A flat token price can still produce a different bill per completed task. Google suggests lowering the effort setting or staying on 3.7 Flash when efficiency comes first. Planning and difficult code changes may benefit from extra work, while a classifier or routine extraction step may just spend more tokens. I ran both tests through the same ZenMux API setup, using the exact 3.7 model slug for one run and the 3.8 slug for the other. Both got the same coding tasks and timeouts, and I tracked retries and accepted patches in one set of request logs. They both did the work well. I could not see a meaningful difference between them, which probably says more about my tasks than the models. They were too easy. What I actually care about is whether 3.8 completes enough extra tasks to cover the extra reasoning it sometimes uses. That requires accepted results and total tokens from the same run, not a model price copied from a launch page. Source https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/ submitted by /u/JuggernautCritical92
Originally posted by u/JuggernautCritical92 on r/ArtificialInteligence