EnviroExam is a 2024 benchmark that evaluates how well large language models understand environmental science using methods inspired by real university exams. Built from forty‑two courses across undergraduate to doctoral levels, the dataset spans nine hundred thirty‑six expert‑reviewed questions covering atmospheric pollution control, environmental chemistry, soil science, waste management, and carbon‑neutrality technologies. The study compares models including DeepSeek, Qwen, Llama, Mistral, and ChatGLM under zero‑shot and five‑shot conditions. Beyond accuracy, the authors analyze consistency using the coefficient of variation, revealing not just peak performance but reliability across topics. While frontier models excel at reasoning tasks, most systems still lack comprehensive domain mastery. EnviroExam positions AI as a student in an academic ecosystem. It offers a diagnostic for progress and a challenge for designers: how do we teach models to reason with scientific nuance? The framework points to future domain‑specific evaluations in climate policy, modeling, and sustainability analytics. Produced by Cognivault — insight, intelligence, and innovation made clear.