Subquadratic shares third-party benchmarks for its SubQ model, claiming it breaks the quadratic attention bottleneck

A startup claims it broke through a bottleneck that’s holding back LLMs

Miami startup Subquadratic now shares independent benchmarks from Appen for its SubQ model, which it claims solves the quadratic attention bottleneck that makes LLMs slow and expensive. SubQ can process up to 12× more text at once than most models, with faster speed and lower cost, while roughly matching top models from DeepMind, OpenAI, and Anthropic on coding tasks. The CTO admits they should have released third-party results alongside the initial announcement. Appen's director of generative AI research calls the results exciting and validating. SubQ is not yet publicly available, and the post does not disclose specific latency, power, or pricing figures.

Why it matters: Subquadratic went from 'AI's Theranos' to delivering Appen independent test data: 12x context window, lower cost, coding on par with top models — a real reversal with substance. MIT Tech Review exclusive adds source authority. Not scoring higher because only one third-party te...