Open language models, whose weights are publicly available for inspection or downstream use, are contrasted with closed models accessible only through APIs or products. They are primarily bucketed into open-weight and open-source models. Open-weight models, such as Meta’s Llama, Alibaba’s Qwen, Google’s Gemma, or DeepSeek’s models, are governed by licenses and often accompanied by inference code. Since about April 2025, Chinese AI companies have been the clear leader in open-weight models. True open-source models also include training code and training data; the most prominent have been built in the United States, led recently by the Allen Institute for AI’s Olmo models, with other prominent ones from OpenAthena’s Marin models and EleutherAI’s Pythia models. Nvidia’s Nemotron models are far more open than most open-weight models, releasing large quantities of training data under permissive licenses, but are not fully open-source. GLM-5.2 and Kimi K3 have enacted a step change in the commercial viability of open models, crossing a similar threshold in agentic capabilities that Anthropic’s Claude Code crossed in December of 2025. America was the early leader in open language models, primarily through Meta’s Llama models. Chinese open-weight models surpassed American open-weight models in two key areas about 18 months ago, with China taking the lead in Hugging Face Downloads in July of 202