4 entries with this tag
One new research article published: GLM-Legal by Z.ai — a lightweight MoE legal agent scoring 19.2% on Harvey LAB with frontier parity at a fraction of the cost.
On October 12, 2026, Z.ai (formerly Zhipu AI) released GLM-Legal, a legal-domain agentic model built on the lightweight GLM-5.3-Flash foundation (320B total, 18B active parameters). It scores 19.2% all-pass on Harvey LAB (ranking #7 of 79), 86.2 on LegalBench, and 77.3 on PLawBench — matching frontier closed-source models on mainstream legal benchmarks while running at $0.075/$0.25 per million tokens. The model demonstrates that specialized mid-training and post-training can close the gap between lean open-weight models and expensive frontier systems on high-stakes professional tasks.
One new research article published: comprehensive analysis of Z.ai's GLM-5.3 release — same base model as GLM-5.2 with all improvements from post-training, delivering 50% Code Bench gain, open-source SOTA on Terminal Bench 3.0, and emergent cybersecurity capabilities including 2,436 real-world vulnerabilities discovered.
On August 14, 2026, Z.ai released GLM-5.3 — the same base model as GLM-5.2 with all improvements driven by post-training. GLM-5.3 delivers a 50% gain on Z.ai Code Bench, reaches open-source SOTA on Terminal Bench 3.0 and Agents' Last Exam, and exhibits emergent cybersecurity capabilities: matching Mythos 5 on CyberGym (84.5%), more than doubling GLM-5.2 on ExploitBench (24.4% → 54.4%), and identifying 2,436 real-world vulnerabilities across 269 projects. Covers architecture, coding benchmarks, the cyber capability emergence, the synthesized environment pipeline, pricing, and strategic implications.