In data-intensive industries, making the context window larger risks being counterproductive. Although models may be able to process more information, more data drives up costs faster than token prices can fall. But a reasoning model should not have to read every data point in order to reason about the data, or reprocess the same underlying dataset every time a new question is asked.
Larger context is not the same as better architecture
Picture a trader analyzing 10 years of market data, or a scientist working through millions of genomic observations. One approach is to place the underlying data inside the model’s context and let it work through everything directly. Technically, this may work, but architecturally, it means an expensive reasoning system has to process enormous quantities of information before it can begin understanding the question, determining the appropriate analysis, and interpreting the result.
But a quantitative analyst doesn’t manually read hundreds of thousands of observations and do the arithmetic by hand. They decide what needs calculating, hand it to a computational tool, and reason with what comes back.
