Since January 2026, the most capable open-weight models have lagged frontier closed models by an average of four months, or 8 ECI points.

Can Chinese and open model companies compete with the frontier through e.g. distillation and talent?

Models that fit on consumer GPUs match the performance of frontier AI within a year or less.

The ratio of training data to active parameters in open-weight LLMs has grown 3.1x per year since 2022. Recent models LLMs have been trained with 20 times more data per parameter than the optimal ratio suggested by the 2022 Chinchilla scaling laws.

This Gradient Updates issue explores DeepSeek-R1's architecture, training cost, and pricing, showing how it rivals OpenAI's o1 at 30x lower cost.

Analysis of open vs. closed AI models reveals the best open model today matches closed models in performance and training compute, but with a one-year lag.