Is GLM / Z.ai (Zhipu AI) Down?

NAMED PRODUCTION DEPLOYMENT -- PERPLEXITY COMPUTER RUNS ON GLM 5.2, POST-TRAINED BY PERPLEXITY (disclosed 2026-09-21, third-party vendor-primary; recorded here 2026-09-28): Perplexity's research post 'Learning from real-world experience' describes post-training 'a Perplexity Computer model' with rejection-sampling fine-tuning plus hint-guided self-distillation on real sessions, and discloses that Computer sessions are served by **GLM 5.2** with the trained checkpoint offered as a model option in Computer; in live use the later checkpoint reduced tool-call failures by 21.2% relative (2.24% to 1.77%, statistically significant) with no significant change in user dissatisfaction. Why it matters for this page: it is the first time a major Western consumer AI product has publicly named a Z.ai model as the engine behind a flagship agent, and it validates the MIT-licensed 5.2 base (not the custom-licensed 5.3) as the version enterprises can post-train and ship. Perplexity's Portable Computer, by contrast, runs Qwen 3.8 27B or Perplexity's own PPLX 27B locally (9/24), not GLM.

STALENESS CATCH-UP -- GLM-5.3 AND GLM-5.3-FLASH SHIPPED 2026-08-25, AND THE FLAGSHIP'S LICENCE CHANGED FROM MIT TO A CUSTOM ONE (vendor-primary via Z.ai's Hugging Face model cards and API pricing page; this page was last reviewed 2026-07-09 and still describes GLM-5.2 as current): **(1) GLM-5.3** -- 'uses the same base model as GLM-5.2 -- every gain comes from post-training.' Z.ai's claims: 'the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench', open-source SOTA on Terminal Bench 3.0 and Agents' Last Exam, and -- stated unusually plainly -- '**Emergent Cyber Capability**: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks.' **Vendor benchmark table (GLM-5.3 / GLM-5.2 / Kimi K3 / DeepSeek-V4-Pro-0813 / Qwen3.8-Max / Opus 4.8 / Fable 5 / GPT-5.6 Sol):** Terminal Bench 2.1 **88.2** / 81.0 / 88.3 / 87.9 / 86.6 / 85.0 / 88.0 / 88.8; Terminal Bench 3.0 **28.3** / 4.6 / 17.4 / - / - / 21.1 / 33.7 / 34.6; DeepSWE v1.1 **66.9** / 46.2 / 67.5 / 62.7 / 56.6 / 58.0 / 69.7 / 72.7; CyberGym **84.5** (highest in the table) / 77.2 / 80.0 / 83.3 / 78.5 / 78.1 / 83.8 / 83.6; ExploitBench 54.4 / 24.4 / 32.2 / - / 28.8 / 40.0 / 78.0 / 76.5; Toolathlon Verified 73.0; AutomationBench **48.2** (highest); HLE w/ tools 62.5; GDPval-AA v2 **1769** (highest, evaluated by Artificial Analysis). So on Z.ai's own numbers 5.3 is at parity with Kimi K3 and DeepSeek V4-Pro among open weights and still 3-6 points behind Fable 5 / Sol on the hardest agentic suites, while leading everyone on CyberGym and GDPval. **LICENCE: GLM-5.3 ships under a custom 'glm-5.3' licence, not MIT** -- a change from GLM-5.2's MIT and the reason the 'MIT licensed' claim in this page's tagline no longer applies to the flagship. **API price $1.40 input / $4.40 output per 1M, cache read $0.26**, same as 5.2 (limited-time free cache write). **(2) GLM-5.3-Flash** -- 'the first natively multimodal model in the GLM-5 series', **320B total / 18B active**, a newly trained base with a **hybrid sparse + linear attention** architecture and Manifold-Constrained Hyper-Connections, 30T-token multimodal corpus; Z.ai says it 'outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.' **MIT licensed. API $0.15 / $0.50 per 1M, cache read $0.03.** `reasoning_effort` low/high/max (defaults to max). **THE CYBER DISCLOSURE DESERVES A SECOND LOOK:** Z.ai is openly publishing exploitation-chain benchmark gains on a downloadable model, three weeks after OpenAI's 'Defender's Window' post warned of an open-weight cyber model 'a few months behind the frontier' -- GLM-5.3's ExploitBench 54.4 is well below Fable 5's 78.0 and Sol's 76.5, so it is not frontier-level, but it is the strongest open-weight cyber result any vendor has published. **Also 8/25:** BF16 builds of both. **Why this was missed:** z.ai/blog now 404s to plain fetches; the Hugging Face zai-org index by createdAt is the reliable enumeration path.

ZCODE HARNESS LAUNCHED (2026-07-02, press-verified): Zhipu released **ZCode**, an agentic-coding harness for GLM-5.2 that lets developers build autonomous coding assistants on the model -- an explicit shot at Anthropic's Claude Code, leaning on developer frustration with Anthropic's recent access restrictions and Zhipu's open-weights positioning. Launch incentives: **5 million free tokens for new ZCode users** and a 50% data-quota boost for existing subscribers. Open-source status implied but not explicitly confirmed -- verify the repo/license before building on it. Strategically this completes the GLM-5.2 story: MIT-licensed frontier weights + a first-party harness = a full Claude Code substitute stack from one Chinese lab

INDEPENDENT BENCHMARK PUBLISHED (verified 2026-07-04): Artificial Analysis now has a citable independent eval for GLM-5.2 -- **Intelligence Index = 51, ranked #1 of 93 open-weight models** (artificialanalysis.ai/models/glm-5-2), corroborating the vendor's 'tops the open-weights index' claim with a third-party number. Keep the Zhipu-reported figures (SWE-Bench Pro 62.1%, Terminal-Bench 2.1 81.0%) labeled as vendor benchmarks -- the AA-51 is the independently-measured one to lead with.

MODEL LAUNCH (2026-06-13): **GLM-5.2** -- Z.ai's new open-weights flagship 'for long-horizon tasks', a substantial leap over GLM-5.1. ~753B-parameter MoE, MIT licensed, with a 1M-token context that sustains long-horizon work and the new **IndexShare** sparse-attention architecture that cuts per-token FLOPs ~2.9x at 1M-token context. Vendor-published benchmarks (third-party verification still settling): SWE-Bench Pro 62.1, GPQA-Diamond ~91.2, AIME 2026 ~99.2, HLE reasoning ~40.5. It tops the Artificial Analysis open-weights Intelligence Index, and VentureBeat reports it beats GPT-5.5 on several long-horizon coding benchmarks at roughly 1/6 the cost. MIT weights shipped to Hugging Face (zai-org/GLM-5.2) and ModelScope; works as a drop-in coding backend for Claude Code, Cline, and OpenCode. Note: exact active-parameter count for 5.2 was not published in the model card -- treat the MoE-active figure as unconfirmed pending a vendor spec sheet.

GLM-4.6 requires specific tokenizer and chat template -- several community llama.cpp quants initially had broken tool-use until fixes landed