Nate B. Jones argues that model selectionModel selection chooses the AI model whose capability, quality, cost, speed, safety, and operating constraints best fit a task. should begin with the work, not benchmark rankings or the latest release. A daily driver must handle ambiguous tasks where judgment matters, while a cheaper model such as GLM 5.2 can be a better fit for familiar artifacts including routine code, presentations, summaries and support replies.
He recommends testing models on real inputsEvaluation is the systematic assessment of a system's behaviour or results against explicit criteria. before standardizing them. Company users must work within approved tools, while small teams should simplify around a few recurring customer outcomes instead of building an elaborate routing system. Specialist image, video and live-information models are useful when they shorten a specific path to valueCost per completed task measures the total AI, tool, infrastructure, retry, and repair expense for each verified useful outcome..
The practical rule is to separate hard, novel work from high-volume routine work, then judge both the model and its surrounding interface. Jones says the best setup is a small, tested collection of tools that reliably moves work in and out, remains affordable and does not turn model choice into a second job.
Watch on YouTube
