Deep|LLM: Open-Weight Models' Low-Cost Catch-Up Is Unlikely to Last

A succession of strong open-weight models were released this summer. Zhipu launched GLM-5.2 on June 22, followed by GLM-5.3 on August 14, a release focused solely on scaling post-training. On Zhipu's own evaluations, GLM-5.3 matched or outperformed Fable 5 and GPT-5.6 Sol on several agentic coding tasks. Moonshot AI launched Kimi K3 on July 17. It scored 57 on the Artificial Analysis Intelligence Index, behind only Fable 5 and GPT-5.6 Sol. DeepSeek's August 13 update to V4 Pro scored 87.9 on Terminal-Bench 2.1, nearly matching Fable 5's 88 at roughly one-fiftieth of its cost per task. DeepSeek followed with V4.1 Flash in September. Xiaomi released MiMo-V2.6 with open weights on September 22; its Pro variant now leads the open-weight rankings on the Artificial Analysis Intelligence Index.

Many in the industry now put the gap between open-weight and top proprietary models at just a few months. Some argue that distillation is not the main reason open-weight models have kept pace. We agree it is not the only reason, but it has been one of the key ones. What worries investors is whether open-weight developers can keep matching top proprietary models at a fraction of the R&D cost, and if so, how long the proprietary labs' lead can last. That worry is feeding into AI and compute stocks, as it did after DeepSeek R1 in January 2025.