GLM FAQ
Frequently asked questions about GLM models: flagship versions, context window, thinking levels, benchmark results, open-source license and usage.
Common Questions about GLM Model
GLM is a family of open-weight large language models; the current flagship is GLM-5.3, joined by the first natively multimodal GLM-5.3-Flash. The line-up spans GLM-4.5-Air and GLM-4.7 up through GLM-5, GLM-5-Turbo, GLM-5.1, GLM-5.2 and now GLM-5.3, plus speech and vision variants. Every core GLM model is released under the MIT license with fully open weights.
Like GLM-5.2, GLM-5.3 raises the ceiling from 200K to a stable one million tokens, with up to 128K output tokens. "Stable" is the key word: trained on long coding-agent trajectories, quality holds across the window instead of degrading as runs grow complex.
Published GLM-5.3 results include Terminal-Bench 2.1 at 88.2 (GLM-5.2: 81.0), Terminal-Bench 3.0 at 28.3 (4.6), FrontierSWE at 78.1 (67.5), DeepSWE v1.1 at 66.9 (46.2), SWE-Marathon v1.1 at 42.5 (19.4), PostTrainBench at 39.8 (31.7), Agents’ Last Exam at 28.5 (23.8), HLE w/ Tools at 62.5 (54.7), CyberGym at 84.5% (currently leading) and ExploitBench at 54.4% (24.4%). Full comparison table above.
It depends on the task. GLM-5.3 leads on CyberGym at 84.5%, beating every rival; on Z.ai Code Bench High it beats Claude Opus 4.8 (31.4 vs 29.5) at roughly half the token usage; and it more than doubles GLM-5.2 on exploit-style benchmarks. Mythos 5 maintains its edge on ExploitBench / ExploitGym.
They control how much compute the model spends per task. GLM-5.3 offers low / high / max thinking effort and always thinks, defaulting to max. Per-tier efficiency curves are GLM-5.2 reported values, for reference: ~63% (~35K output tokens, low), 72% (~43K, high) and 74% (~83K, max).
Yes. The core GLM model weights are under the MIT license, without regional restrictions, and have been released on HuggingFace and ModelScope. It supports local inference through transformers, vLLM, SGLang, xLLM, and ktransformers, allowing you to run GLM models on your own hardware.
IndexShare allows every four sparse attention layers to share a lightweight indexer, whose top-k index is calculated only once and then reused. This eliminates the work of the indexer in three-quarters of the layers, reducing each token's FLOPs by 2.9 times under a million-token context — a key change that makes a million-token window feasible at the service level.
A free open-source LLM served via OpenRouter — not GLM-5.3 and not using GLM weights. It exists so you can test a live model while reading published GLM data. All GLM benchmark data on this site is author-reported, not measured here.