This article was drafted together with AI and reviewed by an editor. It is not a recommendation to buy or invest in any particular product.
As we said in "Weekly AI News #1," models pour out every week and prices keep falling. Large companies have dedicated teams to evaluate them, but for a small team, the moment you switch, that week's schedule disappears. Here are the five standards we set.
1. Divide by "task," not by model
Instead of "which model is best," we first ask, "how accurate does this task need to be?" For things that cannot be undone if the result is wrong (public posts, external messages, reported figures), we use the strongest model plus human review. For things we can fix right away if wrong (drafts, classification, summaries), a cheap model is enough.
2. Before switching, run it once on "our inputs"
Benchmarks are someone else's problems. Take about ten inputs you actually handled last month, run them through the new model, and compare the results side by side. If it is not noticeably better, we do not switch. We keep this exercise under an hour.
3. Keep the way of working outside the model
Instructions, style rules and banned phrases are not hidden inside one model's settings; we keep them as documents. That way you can expect the same results when you change models, and it is easy to check whether a new model follows the same rules.
4. Look at cost "per unit"
Token prices change over time. Instead, we record "what it costs to produce one article, one image, one report." If the per-unit cost does not fall even when prices do, it means the way we work is inefficient.
5. For irreversible actions, a human presses the button to the end
No matter how good models get, deleting, paying, sending externally and publishing go through human confirmation. This is not a question of model performance but of responsibility.
Summary
We cannot control the model race, but we can decide which work to hand over and at what level. If you keep these five standards, news of a new model becomes not anxiety but "something worth comparing once."