Choose AI Models by Task and Cost
Normie Mode turns scattered model comparisons into practical recommendations for familiar jobs, balancing capability against the cost of getting the work done.
Developed from a conversation between Pete Winn and Andy David
Technical benchmarks can show which AI models perform well on standard tests, but they don’t always answer the question most people have. Someone choosing a model wants to know whether it can analyse financial data in a spreadsheet, work with AutoCAD, help with homework or support language learning. The right choice depends on the job, not simply which model leads an overall ranking.
We built Normie Mode around those recognisable use cases. It uses Google’s autocomplete suggestions for searches beginning with the best AI model for to create an initial set of roughly fifty tasks. The tool combines results from four or five model-comparison sources, then weighs capability against cost. A model with slightly less horsepower may be the more useful recommendation when it can complete the task for far less money.
The recommendations are intended as a gut check, not a scientifically definitive ranking. Comparison sites aren’t always updated at the same pace, which can skew results towards models with broader coverage. Andy found that many tasks initially favoured one well-represented model while newer releases such as Opus 5.5 had little data available. Normie Mode therefore narrows the choice using the evidence on hand, while its independent tests and task catalogue continue to grow.