What Do Little Machines in Spring 2026 Know About Buddhist History?

Abstract

The availability of open-weights language models has led to a profusion of language models trained for particular tasks. These relatively small, but increasingly powerful, models run on consumer hardware and can be used freely for the price of electricity. Because the training data for most models are not made public, it is difficult to say how much knowledge about a given domain is embedded in any model; even if we knew more about the training data, it is not always clear how much of it a model can externalize.

This paper is a first step towards developing a benchmark to test the historical knowledge of language models in the domain of Buddhist Studies. We test representative models regarding their knowledge of Chinese and Japanese Buddhist history on consumer hardware. A test bank, subdivided by historical period, with multiple choice questions (MCQs) is used with different model series. Ranking the answers produces a “winner” that “knows” most about Buddhist history. We find that, in Spring 2026, the Qwen series emerges as a winner for models in the 30B range, while the larger Kimi2.5 models lead in a cloud-based setup.