Dynamic Trust
I often find myself stuck on one question:
Which model should I use this time?
At first, the reason was very practical: token and usage limits.
I could not run every task in the deepest mode, so I had to start making choices.
Should I use ChatGPT, Codex, or Claude for this?
Does this need deeper reasoning?
Or is the normal mode already enough?
Then I noticed that my answers kept changing.
I do not have a “best model”
For a while, I thought Claude was the strongest at coding.
After using them more, I found that Codex worked very well for some design and implementation tasks.
And when a problem is still unclear, I often prefer to talk it through with ChatGPT first.
So if someone asks me:
Which one is the best?
I have a hard time answering.
My judgment is closer to:
Who do I trust more for this particular task?
Even with the same model, I may choose a different mode or a different depth of reasoning depending on the task.
I do not have a rigorous scoring system for these decisions.
The main input is simply:
the experience accumulated from using them again and again.
Later, I came across a phrase
Later, I came across the phrase “dynamic trust,” and it immediately helped me understand why I keep going back and forth.
I had never really treated trust as a fixed value.
Not:
Claude: trusted.
Codex: not trusted.
But:
How much do I trust it for this task?
How far am I willing to let it work on its own this time?
And that judgment naturally changes over time.
Models get updated.
New models appear.
My own experience with them changes too.
A judgment I form today should not automatically become a permanent rule.
Why I always keep a little doubt
I think this also has something to do with generative models themselves.
With traditional software, I usually expect:
the same conditions to produce relatively deterministic and repeatable results.
But generative-model outputs are probabilistic.
Ask the same question again, and the result may be different.
And from my perspective, model capability still has a certain black-box quality.
One task going extremely well does not prove that the next similar task will be equally reliable.
So I have become more comfortable thinking about trust as:
a temporary judgment about this task, based on past experience.
The goal is not to find the one model that deserves permanent trust.
It is to keep updating the question:
How much reason do I have to trust it this time?