Using two AI models in a row doesn't always work — it depends on the task

Researchers tested whether having a second AI model review and fix a first model's work actually improves results, or if the second model is just re-solving the problem from scratch. They found it depends entirely on the task: on multiple-choice questions, the second model usually just solves it again (so you might as well skip the first model), but on code-writing tasks, even a bad first draft helps the second model structure its answer better.

Companies building AI products are stacking multiple models together, assuming that review and revision always helps. This paper shows that assumption breaks down fast depending on what you're using AI for. For code or creative tasks, the draft structure genuinely helps a weaker model. For closed-answer tasks, it doesn't — which means engineering teams are wasting compute cycles on pipelines that don't work the way they think they do. The practical consequence is simpler: if you're doing multiple-choice or factual lookup, spend your money on one good model instead of two mediocre ones chained together.