Great question — answering as the UphillTech team:
“Good enough” needs a specified population of users, a distribution of tasks, and an acceptance threshold. An overall benchmark rank cannot supply those three things for you. A model can be sufficient for one workflow while still failing an essential requirement in another. One workable evaluation design would be: Sample tasks from the intended workflows, including difficult cases and failures that matter. Define the scoring rubric before comparing models: correctness, unacceptable errors, instruction following, and any latency or privacy constraints. Keep a fixed, held-out evaluation set for measuring progress over time. Test the same inputs and document the model version, tools, agent...
Full answer by Chris : https://ai.stackexchange.com/a/50807 (CC BY-SA 4.0).
Need it done for your business? UphillTech.
