Zhipu

Models & products

Models

GLM

Trends

• Chinese labs take the lead in open-weight models
• Open-weight models diversify in architecture, modality, and size
• AI labs move inference onto custom silicon
• Frontier labs slow down and publicly report their own risks
• Open weights consolidate around a flash tier and an Nvidia-owned hub

Items

1. Aug 26

   Zhipu's GLM-5.3-Flash

   The GLM-5 series' first natively multimodal model (320B total, 18B active, 1M-token context, hybrid sparse-plus-linear attention) shipped with MIT-licensed weights and runs entirely on domestic Chinese accelerators, after twelve days atop OpenRouter as the anonymous "Ox Alpha."

2. Aug 15

   Z.ai's GLM-5.3 tops open models, weights delayed

   Artificial Analysis scored the post-training-only GLM-5.3 at 60 on its Intelligence Index, tying Kimi K3 as the top open model, but Z.ai delayed the open-weight release about two weeks because its vulnerability-discovery capability needs more safety hardening.

3. Jul 8

   DeepSeek and Zhipu design in-house inference chips

   Reuters and The Information report both Chinese labs are quietly designing custom inference silicon to cut dependence on Nvidia and Huawei, with DeepSeek's move coinciding with its first outside fundraise of ~$7B.

4. Jun 13

   Z.ai's GLM-5.2

   A 753B-parameter MoE (~40B active) with a stable 1M-token context and MIT-licensed weights, scoring 62.1 on SWE-bench Pro versus GPT-5.5's 58.6 and 74.4% on FrontierSWE, with IndexShare attention cutting per-token compute ~2.9x at full context.

5. Apr 7

   Z.ai's GLM-5.1

   A 744B-parameter MIT-licensed MoE (40B active) that tops SWE-Bench Pro at 58.4%, ahead of GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro, sustains eight-hour autonomous coding loops, and was trained entirely on Huawei Ascend hardware.