GLM-5.2
Zhipu AI·
LLMsopen-weightcloud + localFrontier
Overview
753B Mixture-of-Experts (~40B active) open-weight model under MIT license. Introduces the IndexShare attention mechanism (~2.9x lower per-token FLOPs at 1M-token context) and a usable 1M-token context window. Ships with two thinking-effort levels; the highest tier is marketed as 'Max'. Available as open weights on Hugging Face and via the Z.ai API. Vendor-reported benchmarks (maximum thinking effort): GPQA Diamond 91.2, SWE-bench Pro 62.1, Terminal-Bench 2.1 81.0, HLE 40.5 (no tools).
Capabilities and innovations
753B total / ~40B active MoE1M-token contextTwo thinking-effort levels (incl. 'Max')Agentic / long-horizon codingMIT licenseIndexShare attention (~2.9x FLOP reduction at 1M-token context)Usable 1M-token context window
Benchmarks
| Benchmark | Score | Source |
|---|---|---|
| GPQA Diamond | 85.61% | vendor Z.ai self-report: GPQA Diamond 91,2%. Not used as primary value (deviation >3 points from the independent runs). |
| Humanity’s Last Exam (no tools) | 40.5% | vendor HLE 40.5 no-tools (recorded for cross-model comparability). Z.ai also reports 54.7 with tools, which is not comparable to the no-tools HLE used here. |
| SWE-bench Pro | 62.1% | vendor SWE-bench Pro 62.1 — Z.ai-reported (maximum thinking effort). |
| MMLU-Pro | 86.71% | unsourced |
| Terminal-Bench 2.x | 77.9% | vendor Terminal-Bench 2.1 (Terminus-2) 81.0 — Z.ai-reported; Z.ai also lists 82.7 on its best-reported harness. Not used as primary value (deviation >3 points from the non-vendor measurement). |
| Terminal-Bench 3.0 | 4.6% | vendor Terminal-Bench 3.0 4.6 — Z.ai-reported in the GLM-5.3 launch comparison table. Figures taken from launch coverage on 2026-08-17; the primary table at docs.z.ai was not reachable from the build environment for direct verification. Do not compare against the terminal_bench column: 3.0 is a harder generation on a different scale. |
- MMMU-Pro: not reported by the vendor
Architecture and hardware
- Parameters
- 753B total, 40B active per token (MoE)
- Estimated VRAM at Q4
- ~433 GB, Frontier class
Links
More from Zhipu AI
Data curated by AI Model Timeline. See the methodology for admission criteria, benchmark eras and source priority.