In this deep-dive episode, Neural Intel goes behind the data of the Medmarks v0.1 benchmark suite, led by Sophont and the MedARC community. While previous benchmarks like MultiMedQA have "saturated," Medmarks introduces MedXpertQA, a reasoning-heavy task that currently pushes even the strongest frontier models to their limits.We examine the technical nuances of the study:• Thinking vs. Instruct: How reasoning post-training creates a "Pareto improvement" in medical accuracy.• The Efficiency Gap: Why open-weight models like Qwen3 match frontier accuracy but require 5x to 6x the token volume to get there.• Order Bias: The surprising discovery that even frontier models like Grok 4 can be "tripped up" simply by shuffling the order of multiple-choice answers.• Medical Specialization: Does a "medical-tuned" model like MedGemma actually outperform a generalist giant?.Join us as we discuss how these benchmarks are doubling as reinforcement learning environments to train the next generation of digital clinicians.