Reasoning Models

For all the hype about model accuracy, inference cost, and latency, most organizations are still squinting at scatter plots to decide which large language model (LLM) to use. But what if we could cut through the tradeoff fog with a single number that tells you exactly which model is worth deploying—for your use case, under your constraints? That’s the bold proposal in a recent paper by Zellinger and Thomson from Caltech: treat LLM selection as an economic decision. Rather than searching for models on the accuracy-cost “Pareto frontier,” they suggest an approach grounded in price-tagging errors, delays, and abstentions in dollar terms. Think of it as a model selection framework that answers: How much is a mistake worth to you? ...

Cut the Fluff: Leaner AI Thinking When it comes to large language models (LLMs), brains aren’t the only thing growing—so are their waistlines. As AI systems become increasingly powerful in their ability to reason, a hidden cost emerges: token bloat, high latency, and ballooning energy consumption. One of the most well-known methods for boosting LLM intelligence is Chain-of-Thought (CoT) reasoning. CoT enables models to break down complex problems into a step-by-step sequence—much like how humans tackle math problems by writing out intermediate steps. This structured thinking approach, famously adopted by models like OpenAI’s o1 and DeepSeek-R1 (source), has proven to dramatically increase both performance and transparency. ...