A new release needs to solve a difficulty before taking over as my default. The chart can look excellent while yesterday's configuration remains suitable. I want a work-related reason to switch, especially after experiencing the distance between liking a model and liking the tool around it.
OpenAI launched GPT-6.1 Sol on September 29, 2026, at $2 per million input tokens and $10 per million output tokens, with cached input at $0.10. The company positioned it near Astra on selected evaluations at lower prices. I need the tasks and conditions behind that comparison. Near Astra alone says little about the request in my queue.
I had wanted to compare models since June. On September 15, discussing my move from Claude to Codex, I considered the Codex model better while seeing Claude do things Codex still struggled with in that experience. I was demanding a practical result: the capability I saw needed to show up in finished work.
That situation defines part of the next comparison. If I change the model and the tool together, I may prefer the deliverable without knowing which change helped. The whole experience matters when choosing a workflow. Diagnosing what stalled requires separating its parts. I will record both, because a poor diagnosis can send me to change the model when the difficulty concerned access to context.
I also asked for model selection to reflect the task. I want to observe whether an agent reaches a reviewable change and explains a limitation before building on it. When an instruction changes, it needs to preserve what remains valid. Those behaviors become visible during execution; an isolated score leaves plenty out.
Cost includes the corrections required to reach a result I accept. A cheap attempt may demand several more, while a costlier option may need less intervention. I will count that entire path and keep the original request and completion criterion available. That lets me revisit the choice when the tool changes or another kind of assignment arrives.
GPT-6.1 Sol enters as a candidate on a recurring, verifiable task, with the same initial information for the models compared. My decision will stay connected to what each execution returns and its recorded conditions. I want an explainable choice for the next job. The announcement deserves investigation; changing the default will depend on an advantage I can identify in the task itself.