Part 5/9:
One critical area of discussion remains user experience versus model benchmarks. The ongoing release of new model tunes and updates often leaves users feeling frustrated, especially if these come with restrictions that limit access or effectiveness.
What many developers and users are seeking is a stable and reliable AI service that offers consistent responses grounded in real-world applications — not just performance on an abstract leaderboard. The better-use case scenario might involve creating models that are tuned for specific tasks rather than simply competing for ratings — more focused models could deliver better value to end users.