Lab: cost versus accuracy for reasoning strategies
Run a simulator that compares direct answers, chain of thought, voting, verifier best-of-n and adaptive sampling on 3,000 problems of varying difficulty, plot accuracy against tokens, then extend it with your own methods and apply the same method to a real model.
Goal: make the accuracy-versus-cost trade-off of reasoning methods concrete, then repeat the measurement on a real model and benchmark.
You need: Python 3.9+ for the simulator; for the real-model extension, an inference engine or API with a small open model and a verifiable benchmark (for example grade-school maths with numeric answers).
Honest limits. The simulator runs as shown; its problems and error patterns are assumed (success probabilities from a Beta distribution, one tempting wrong answer). Use it to learn the shape of the trade-offs, not to predict a real model.
Pro content
Keep reading with Inference Academy Pro
Unlock this full lab, plus every advanced lesson, hands-on lab, the larger playground models and completion certificates.