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Brian Roemmele touts GLM-5.2 from Z.ai (Zhipu AI) as an MIT-licensed, open-weight…

2026-06-17 · 23:19 UTC ·@BrianRoemmele ·2 min read

Brief

Brian Roemmele touts GLM-5.2 from Z.ai (Zhipu AI) as an MIT-licensed, open-weight LLM — 744B params with ~40B active MoE and a 1M‑token context window — claiming it outperforms Claude Fable and GPT-5.5 (Design Arena Elo 1360; SWE-bench Pro 62.1% vs 58.6; Terminal-Bench 81.0). He reports local/quantized deployment across The Zero-Human Company and warns political limits will "stymie the US by years."

Source evidence

The “leaders” in AI have caught the ear of the political class and are trying to do their best to limit AI once again.

It will not work.

What it will do is stymie the US by years.

OPEN SOURCE GLM AI BEATS ANTHROPIC FABLE!

BEATS IT ON LOCAL GPUS.

BEATS IT.

Brian Roemmele (@BrianRoemmele)

BOOM!

OPEN SOURCE GLM BEATS THE FABLED FABLE!

GLM-5.2 from Z.ai: The Open-Weight Model That Topped Claude Fable and Powers The Zero-Human Company

Z.ai (Zhipu AI) released GLM-5.2 and our tests show it delivering a major leap in long-horizon agentic coding with a practical 1M-token context window, flexible reasoning effort levels (High/Max), and MIT open weights.

Early benchmarks and community arenas show it excelling where it matters most for developers.

We compared it to our first Anthropic Fable model tests and GLM did better!

It leads open-weight models and has claimed the top spot on Design Arena (Elo 1360), and as I said is surpassing the now-unavailable Claude Fable 5.

It also posts strong results on coding suites: 62.1% on SWE-bench Pro (beating GPT-5.5’s 58.6) and 81.0 on Terminal-Bench 2.1.106

Official blog: z.ai/blog/glm-5.2

The Zero-Human Company Goes All-In

At The Zero-Human Company, where AI agents handle nearly all operations, we’ve rolled out GLM-5.2 across all employee (agent) workflows for code generation, refactoring, debugging, and autonomous project execution. Its long-context reliability and agentic strengths make it ideal for sustained, multi-hour tasks without constant human oversight—perfect for a zero-human setup.

We’re particularly excited about its open weights and local deployment, which ensures full data privacy and resilience—no external service dependencies or potential bans.

Running GLM-5.2 Locally
Thanks to its MIT license and strong inference support, you can run GLM-5.2 (744B total params, ~40B active MoE) on your own hardware today.

Quantized versions (FP8, etc.) make it feasible on high-end setups.

Quick start options (from the official GitHub):
•vLLM: recipes.vllm.ai/zai-org/GLM-…
•SGLang: cookbook.sglang.io/…/GLM-5.2
•Hugging Face Transformers or KTransformers for more options.
•Full deployment guide: github.com/zai-org/GLM-5

Example setup with vLLM (Docker recommended for ease):

Clone repo and follow recipes for quantized inference

Supports reasoning_effort="max" (default) or "high"

This local-first approach aligns perfectly with our zero-human philosophy: agents run securely on-prem, with full customizability.
GLM-5.2 isn’t just competitive it’s a timely open alternative in a world of access restrictions.

We’re thrilled to test and build with it company-wide. Expect more updates as our AI workforce puts it through real production.

The myth of Mythos and the fable of Fable is entertaining but we are getting to work.

— https://nitter.net/BrianRoemmele/status/2067344987248087302#m