Zhipu officially announced the launch and open-sourcing of GLM-5.3-Flash, which is the first native multimodal model in the GLM-5 series.

It is reported that the overall performance of GLM-5.3-Flash exceeds that of GLM-5.2, with programming and Agent evaluations approaching Claude Opus 4.8, but at only one-tenth the price of GLM-5.2. It also features a new foundational model, introducing a mixed architecture of sparse attention and linear attention for the first time in the main GLM series, and is pre-trained with 30T Token multimodal data.

Zhipu stated that to gather extensive and professional feedback from a wide range of users, large-scale testing was conducted with the anonymous model Ox-Alpha (referred to as “Niu Lai” in the Chinese community) on OpenCode and OpenRouter before the official release. Ox-Alpha quickly became the most popular model of the week, setting a new high for call volume on both platforms, with all request traffic supported by domestic chip computing power.

Previously, the community’s DeepSWE small sample test for Ox Alpha once achieved 80%, but that was based on only 10 questions. After expanding the sample, the score fell back to about 63%, and testers also actively corrected the initial claim. This score still belongs to the top tier, but is not as exaggerated as the initial 80%. After the weights are open-sourced, developers can directly deploy using frameworks like vLLM, SGLang, KTransformers, without needing to go through the anonymous model’s API.