# SubQ: The First Fully Subquadratic LLM — Cost Comparison with Transformers [2026]

> Source: <https://www.lucasaguiar.xyz/posts/subq-subquadratic-llm-atencao-linear-comparacao-custos-2026/>
> Published: 2026-07-07 21:00:00+00:00

There is a fundamental problem with all the large language models you use today — and it has to do with high school math.

The heart of the Transformer is **attention**: each token needs to compare itself with every other token. A text of 1,000 words requires 1 million comparisons (1,000²). A text of 1 million tokens requires **1 trillion comparisons**. The computational cost grows with the **square of the context** — O(n²). It is the reason why models “break” with very long inputs, why we use RAG, chunking, and agents instead of simply giving the entire document to the model.