Reasoning vs. Non-Reasoning AI Models: What's the Difference?
1 min read
Summary
AI reasoning models analyze multiple paths for responding to a query before settling on one, compared with non-reasoning models that respond based on pattern recognition.
While non-reasoning models dominate in terms of speed and creativity, reasoning models excel in complex math problem-solving and code debugging.
For some data analysis tasks, the reasoning model’s additional insights do not justify the wait time.
Furthermore, the technical constraints mean these models are 2-5 times more computationally expensive, and so are typically more expensive to use, and have a larger carbon footprint.
The author suggests that users become more selective, saving reasoning capabilities for tasks that warrant deeper analysis, rather than everyday queries.
The future may see AI systems that can switch between reasoning and non-reasoning models depending on the task.
