Zhipu open-sources GLM-5.3-Flash: 320B native multimodal model matching Claude Opus 4.8 at 1/40 the price

GLM-5.3-Flash：前沿智能进入普惠时代

Zhipu released and open-sourced GLM-5.3-Flash, a 320B-parameter native multimodal model with 18B active parameters. It scores 57 on the Artificial Analysis Intelligence Index, matching Anthropic Claude Opus 4.8, and delivers comparable coding performance at 1/40 the API price. The model uses a hybrid sparse-and-linear attention architecture, cutting attention compute by over 3x versus GLM-5.3 on long contexts. It can use visual feedback in coding loops to self-correct—it once ran autonomously for 16 hours to build a 400 m² kitchen scene in Blender. All public test traffic last week ran on a domestic chip cluster; the team used EPD disaggregated serving and aggressive memory optimizations to achieve 3x end-to-end speedup, bringing per-token cost on par with mainstream NVIDIA GPU setups. Weights are open on HuggingFace, with API access via ZCode and the BigModel platform.

Why it matters: Zhipu open-sourced GLM-5.3-Flash, a 320B-total / 18B-active model scoring 57 on the AA Intelligence Index — matching Claude Opus 4.8 — at 1/40 the API price. The hybrid attention architecture cuts long-context compute by over 3x, backed by a standalone tech blog. Running the a...