z.ai’s open source GLM-5 achieves record low hallucination rate and leverages new RL ‘slime’ technique
Publish Date: 2026-02-11 19:12:00
Source Domain: venturebeat.com
Chinese AI startup Zhupai aka z.ai is back this week with an eye-popping new frontier large language model: GLM-5.
The latest in z.ai’s ongoing and continually impressive GLM series, it retains an open source MIT License — perfect for enterprise deployment – and, in one of several notable achievements, achieves a record-low hallucination rate on the independent Artificial Analysis Intelligence Index v4.0.
With a score of -1 on the AA-Omniscience Index—representing a massive 35-point improvement over its predecessor—GLM-5 now leads the entire AI industry, including U.S. competitors like Google, OpenAI and Anthropic, in knowledge reliability by knowing when to abstain rather than fabricate information.
Beyond its reasoning prowess, GLM-5 is built for high-utility knowledge work. It features native “Agent Mode” capabilities that allow it to turn raw prompts or source materials directly into professional office documents, including ready-to-use .docx, .pdf, and .xlsx files.
Whether generating detailed financial reports, high school sponsorship proposals, or complex spreadsheets, GLM-5 delivers results in real-world formats that integrate directly into enterprise workflows.
It is also disruptively priced at roughly $0.80 per million input tokens and $2.56 per million output tokens, approximately 6x cheaper than proprietary competitors like Claude Opus 4.6, making state-of-the-art agentic engineering more cost-effective than ever before. Here’s what else enterprise decision makers should know about the model and its training.
Technology: scaling for agentic efficiency
At the heart of GLM-5 is a massive leap in raw parameters. The model scales from the 355B parameters of GLM-4.5 to a staggering 744B parameters, with 40B active per token in its Mixture-of-Experts (MoE) architecture. This growth is supported by an increase in pre-training data to 28.5T tokens.
To address training inefficiencies at this magnitude, Zai developed “slime,” a novel asynchronous…
