GLM (Z.AI)

Sources#

What it is#

The GLM (General Language Model) family from Z.AI (Zhipu AI), a lab spun out of Tsinghua University's KEG group. In this corpus GLM appears as the large-MoE open-weight line — the axis of open weights that competes on frontier capability rather than on edge efficiency, the counterpart to Gemma 4's smaller-and-cheaper strategy. Three members appear in the SAO paper:

Model	What's known here
GLM-4.5	"Agentic, reasoning, and coding (ARC) foundation models" (Team GLM, arXiv 2508.06471, 2025). The generation the SAO authors' techniques descend from.
GLM-4.7	A frontier-competitive reasoner. In Table 1 it beats GPT-5 High and Claude-Sonnet-4.5 on AIME2025 (95.7), HMMT Nov-2025 (93.5) and IMOAnswerBench (82.0). Also used as the LLM judge for reward assignment in SAO's online-learning simulation.
GLM-5.2	A 750B-total / 40B-active open MoE — the production model SAO was built to train. The paper's framing: "successfully deployed in the agentic RL pipeline for training the open GLM-5.2 model."

GLM-5.2 priced against the frontier on real engineering work (July 2026)#

The corpus's first third-party coding placement for GLM-5.2, and it is an economic one. Databricks' internal benchmark — real engineering tasks against its multi-million-line codebase, relayed by The Register (2026-07-13, case-study, secondary reporting of Databricks' blog post) — reports it "landed in the top capability tier, statistically tied with Opus 4.8 on quality, but costing $1.28/task against Opus's $1.94", i.e. 34% cheaper per task at indistinguishable quality, and cheaper per task than Sonnet 5 ($2.09) as well.

Two reasons this is a stronger claim than a leaderboard row. It is measured on someone's actual codebase by a party that sells neither model, and it is measured in cost per task rather than accuracy — the axis Cost-per-Task Over Cost-per-Token argues is the one that decides purchases, and the axis on which a cheap model's headline price advantage is usually cancelled by burning more tokens and finishing less often (as it was for Sonnet 5) and here was not. The article gives no per-token price for GLM-5.2; the $1.28 is the only cost figure attached to it. Discount appropriately: secondary reporting, no n, no variance, and nothing published behind "statistically tied"; this figure also exists in the raw only because the ingest pass rebuilt the article body from curl'd HTML after WebFetch dropped it.

How much of this reaches US businesses: not much, and additively (July 2026)#

Every claim above is about capability or price. Ramp's July 2026 AI Index (empirical, corporate-card and bill-pay records) supplies the only demand-side bound this corpus has, and it cannot see GLM specifically — Ramp has no per-model visibility, so it counts firms paying model-serving and inference platforms as a single proxy for all open-source and Chinese model access. That proxy reaches 5.8% of AI-spending US businesses in June 2026 (up from 4.5% in January), and it is a ceiling for GLM, not a measurement of it. Two implications for this page: a Databricks-style verdict that GLM-5.2 is 34% cheaper per task at tied quality has, as of mid-2026, not translated into visible displacement of the American labs — 96.4% of the firms on those platforms still pay OpenAI or Anthropic directly, at rates above the AI-spender base rate. And the instrument is blind to the deployment mode this page's most interesting datapoint used: Hugging Face self-served nvidia/GLM-5.2-NVFP4 on its own endpoints, which generates no serving-vendor payment and no row in Ramp's data. Full treatment at The Open-Weight Frontier Gap.

Why it's in the wiki#

Two reasons, both connecting to existing threads.

It is the reason SAO exists. Asynchronous single-rollout RL is not an academic exercise here — it is the training method behind a shipped 750B-A40B open model. That production heft is what separates this paper from a methods note: the stability results (~1000 stable steps vs GRPO's collapse at ~160) had to hold at GLM-5.2 scale.

It is a data point for the open-weight frontier. The Open-Weight Frontier Gap observes that open weights at the frontier means 744B–1.6T MoEs. GLM-5.2 at 750B-A40B is exactly that class. And GLM-4.7's Table 1 numbers — ahead of two closed frontier models on three of four math-reasoning benchmarks — are a concrete instance of an open model reaching the closed frontier on a capability axis, which is the gap that page tracks. Read the two together: Gemma competes at 31B on efficiency and sits at Arena rank 43; GLM competes at 750B on capability and lands among the frontier reasoners. Same "open-weight" label, opposite strategies.

The Tsinghua / Z.AI authorship thread#

SAO's authors are Zhenyu Hou, Yujiang Li, Jie Tang, Yuxiao Dong (Tsinghua), with ZH and YL noting internships at Z.AI. Zhenyu Hou also appears on the GLM-4.5 author list, and Tang and Dong are the Tsinghua faculty behind the long-running GLM/ChatGLM line — so the paper is effectively the GLM team documenting the RL infrastructure behind their own model, published academically. Treat the GLM-4.7-beats-GPT-5 numbers with that in mind: they are empirical (measured benchmark results) but first-party to the same lab whose method the paper is selling.

Connections#

Single-Rollout Optimization — SAO, the RL method deployed to train GLM-5.2; GLM-4.7 is both its benchmark ceiling and its online-sim judge
Asynchronous RL for LLMs — the training-loop regime GLM-5.2 was trained under
The Open-Weight Frontier Gap — GLM-5.2 is the 744B–1.6T-class open MoE that page describes; GLM-4.7's numbers are the capability-side counterexample to Gemma's efficiency-side positioning, and Databricks' $1.28-vs-$1.94 per-task result is the gap closing on the axis a buyer actually pays
Cost-per-Task Over Cost-per-Token — GLM-5.2 is that page's counter-arm: the cheap end of the menu coming out cheapest per task at tied quality, which is what stops "cheaper tokens cost more overall" from being a law about price tiers rather than a claim about tokens-to-completion
Gemma 4 — the sibling open-weight family with the opposite strategy (small + efficient vs large + frontier-capable)
LLM-as-a-Judge — GLM-4.7 serves as the reward judge in the online-learning experiment
Autonomous Intrusion — GLM 5.2's first deployment appearance in this corpus rather than a benchmark one: Hugging Face reports running it locally to analyze 17,000+ attacker events during its July 2026 breach, after frontier commercial APIs' safety guardrails refused the attack payloads. The qualifying property is self-hostability plus enough capability for large-scale forensic analysis — not an Elo placement (case-study, first-party; no comparison of its analysis quality against the blocked alternative is reported). Corroborated and sharpened 2026-08-03: OpenAI's account of the same incident confirms from the other side that Hugging Face "had already begun containment and forensic reconstruction with their own open-source models," and re-attributes the attacker to OpenAI's own frontier models run with cyber refusals reduced — so the open-weight model was doing forensics on the output of a deliberately de-guardrailed closed-weight one. Detail added 2026-08-03 from HF's technical post-mortem: the build was Nvidia's NVFP4 quantization, nvidia/GLM-5.2-NVFP4, served on HF's own Inference Endpoints, and it replaced "Claude Opus and Fable" by name after both refused the work. What it did is more specific than "analysis": it recovered the agent's chunk + XOR + compress scheme and its per-campaign key from the agent's own leaked logs, which decrypted staged blobs a naive text scan had missed (~4× more secrets recovered), and it built the trace-analysis interfaces used to browse and correlate ~17,600 actions. A quantized open MoE doing cryptanalysis and tool-building on live attacker payloads is a sharper deployment claim than the first disclosure supported
Kimi (Moonshot AI) — the sibling Chinese open-MoE line; GLM-5.2 is the sixth column of Kimi K3's July 2026 benchmark table, where it trails K3 on nearly every row (vendor-claim, and mostly transcribed from the GLM-5.2 release blog rather than re-run)

Sources#

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning — GLM-5.2 as deployment target (Abstract, §1); GLM-4.7 in Table 1 and as online-sim judge (§4.5); GLM-4.5 as the referenced foundation model (Team GLM, arXiv 2508.06471). arXiv 2607.07508, 2026-07-08. empirical, first-party to the GLM lab.
The price is wrong: AI cost calculation has to consider task completion rates, not just token costs — Thomas Claburn, The Register, 2026-07-13 (case-study, secondary reporting; quotes Databricks' benchmark blog post, which is not in the corpus): GLM-5.2 in the top capability tier, statistically tied with Opus 4.8 on quality at $1.28/task vs $1.94. Figure recovered only because the ingest pass rebuilt the article body from curl'd HTML after WebFetch dropped it
Security incident disclosure — July 2026 — "Forensic analysis": GLM 5.2 run locally over 17,000+ attacker events (2026-07-16, case-study, first-party to the user, not to Z.AI).
Ramp's latest data on China vs. the American AI Labs — Ara Kharazian, Ramp AI Index (2026-07-08, empirical, third-party): the model-serving-platform proxy (5.8% of AI-spending US businesses) as an upper bound on paid Chinese/open-model access, and the 96.4% who also pay OpenAI or Anthropic. No GLM-specific figure exists — Ramp has no per-model visibility and does not name GLM. COI: Ramp's own VC-forward-skewed card customer base
Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident — "How we intercepted and analyzed the attack": nvidia/GLM-5.2-NVFP4 on HF Inference Endpoints, replacing Claude Opus and Fable after both refused; recovered the agent's chunk+XOR+compress scheme and per-campaign key; built the trace-analysis interfaces (2026-07-27, case-study, first-party to the user and to the platform hosting the model).