[D] How to get credits to run experiments on closed source models as a student researcher.

A student researcher has sparked a viral discussion by detailing the immense computational cost of evaluating cutting-edge AI models. Their project involves running a reasoning-intensive benchmark across approximately 900 questions on models like Google's Gemini 3.1 Pro and OpenAI's GPT-5.2. A single query to Gemini 3.1 Pro can generate an average of 30,000 output tokens, while a GPT-5.2 run can take up to 15 minutes to complete. This scenario underscores a growing crisis in AI academia: the frontier models with the most advanced capabilities are locked behind expensive, pay-per-use APIs, putting large-scale evaluation out of reach for most students and university labs without substantial funding.

The technical demands are staggering. Running the full 900-question benchmark at this scale would likely cost thousands of dollars in API credits, a sum far beyond typical student grants. This creates a significant access gap, where only well-funded corporate or institutional researchers can afford to rigorously test and compare the latest models. The community response suggests solutions like applying for academic grants from the AI companies themselves (e.g., OpenAI's Researcher Access Program), seeking university departmental funds, or using cloud credits from providers like Google Cloud or AWS. This dilemma highlights a critical tension in AI progress: as models become more powerful and expensive to run, the ability to independently verify their performance becomes concentrated in the hands of a few.