GPT-5.6 is interesting, but the part I care about is not another benchmark saying the newest model is the cleverest. It is that OpenAI has made the tradeoff explicit. Sol, Terra, and Luna are three different points on the capability and cost curve, and that is much closer to how real workloads need to be treated.
In the GPT-5.6 announcement, OpenAI positions Sol as the flagship, Terra as the balanced option, and Luna as the fastest and cheapest. API pricing follows that split: Sol is $5 per million input tokens and $30 per million output tokens, Terra is $2.50 and $15, while Luna is $1 and $6. OpenAI also says Terra is competitive with GPT-5.5 and Luna can get close to it for substantially less money.
That should change the default question from “which model is best?” to “what does this task actually need?” A difficult migration, security investigation, or long-running Codex job may justify Sol. Routine classification, extraction, summaries, and predictable tool calls probably do not. The sensible setup is to test all three on your own small evaluation set and route work by evidence rather than by model prestige.
The annoying part is that this creates another decision teams have to own. The useful part is that the decision can now save real money. I would rather have clear tiers and measure them than pay flagship prices for every background job because nobody wanted to choose.