Researchers engineer GLM-4.1V-Flash to match Jev decision model accuracy

Researchers have developed a prompting technique that gives standard large language models like GLM-4.1V-Flash decision-making properties similar to the specialized Jev model. The core method involves crafting input prompts so that the model's very first output token directly answers a given question, enabling a decision in a single forward pass. Benchmarks show this approach matches Jev in accuracy and speed while significantly outperforming another model called Laya. However, Jev remains several times more cost-efficient per decision than the new setup. A notable advantage of the proposed method is its support for vision inputs, which Jev lacks.