GLM-5.3-Flash: 320B MoE, 18B Active, MIT
Summary: GLM-5.3-Flash in 30 Seconds GLM-5.3-Flash is Z.ai’s (formerly Zhipu AI) new model, released 26 August 2026. It is the first natively multimodal member of the GLM-5 series: text and images go through the same model. It is 320 billion parameters, but only 18 billion run per token. Layer count is roughly half of GLM-4.5’s: 45 against 92. The licence is MIT. Most strong Chinese models this summer shipped under bespoke community licences; here there is no fine print to read before shipping a product. It beats GLM-5.2 by a wide margin on coding and agentic tests (63.4 against 46.2 on DeepSWE) and approaches Claude Opus 4.8 overall, at roughly one tenth of GLM-5.2’s price. API pricing is $0.15 in / $0.50 out per million tokens, or $0.075 and $0.25 with the 50% discount running until 9 September 2026. It is the first model in the series to use hybrid attention: linear attention carries local dependencies, sparse attention retrieves distant context. Against GLM-5.3 that is 3x less attention compute and a 4.4x smaller KV cache. Before launch it was tested anonymously as ox-alpha on OpenCode and OpenRouter, where it became the most used model of the week. All of that traffic was served on Chinese AI chips. There is one race in open-weight models this summer: producing the same intelligence with less compute. Alibaba’s Qwen3.8-Flash-Next beat its own 397B sibling with 6 billion active parameters. Z.ai’s answer is GLM-5.3-Flash: 320 billion total parameters with only 18 billion running per token, leaving GLM-5.2 behind at a tenth of the cost. ...
