Impressive benchmark results should make it easy for engineers to select the right open model for the outcome they are tasked with developing. However, that is not the case. Benchmarks indicate an open model’s optimal performance, but in production, a model must work repeatedly. The challenge is that in production, other factors matter, such as the information provided to the model and its accuracy. So benchmarks can’t tell an engineer whether the AI they are developing will complete the task accurately, or even at all. This is especially important for long-horizon AI tasks. Ryan Hanrui Wang...

Source: The New Stack