At an Aug. 31 H1 2026 results briefing, Zhipu AI founder Tang Jie rejected criticism that the firm only performs fine‑tuning and said the next‑generation base model will expand in size while explicitly controlling activated parameters to avoid slower inference and higher costs. Zhipu said it has deployed scaled inference on roughly 100,000 Chinese chips, cutting per‑token inference cost about 80% since the start of the year. GLM‑5.3‑Flash is the company’s first model to run entirely on domestic

At an Aug. 31 H1 2026 results briefing, Zhipu AI founder Tang Jie rejected criticism that the firm only performs fine‑tuning and said the next‑generation base model will expand in size while explicitly controlling activated parameters to avoid slower inference and higher costs. Zhipu said it has deployed scaled inference on roughly 100,000 Chinese chips, cutting per‑token inference cost about 80% since the start of the year. GLM‑5.3‑Flash is the company’s first model to run entirely on domestic chip clusters under large real‑world traffic; end‑to‑end service performance is about 3x the initial hardware baseline. Tang said GLM‑6.0 will target “self‑evolution,” with the primary research challenge being the model’s ability to judge when to stop and when to self‑correct rather than simply increasing parameter count.