Why do people rate the same AI models so differently?
ratings can vary sharply depending on the task and how the model is used. In the same community, some people consider Fable the best option while others find 4.6 better.
One possible reason is that people use a model for work it does not handle especially well, receive average results, and come away disappointed. It remains unclear whether the differences mainly come from task choice, usage habits, expectations, or other factors.
Key points
- Opinions differ sharply between Fable and 4.6.
- The best model may depend on the task.
- Using a model outside its strengths may produce disappointing results.
- Compare models with the same real tasks and conditions.