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

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.