One of the surprising things about Model Evaluation Studio is that we thought it would primarily be about cost reduction. We see people all over LinkedIn and Twitter complaining about AI model costs, so, we expected that most of our users would use Neurometric to test for cheaper models. Some do. But it’s not the main use case. The main use case is improved latency.
When you build an agent, particularly one that takes multiple steps to complete a task, it can be slow. Using smaller faster models to complete part of that workflow can improve latency significantly (we see an average of 4-5x speed improvements in our customer base). As you see above, when you analyze lots of models on your specific workflows, we can give you a radar graph showing how the models stack up on cost and speed and token efficiency.
If you want to know which models work fastest for your specific AI workloads, sign up and try it out for free.