Zhipu AI just dropped GLM-4.5, a Mixture-of-Experts LLM built to juggle three hard modes at once: agentic tasks, deep reasoning, and real-world coding. The headline specs: 355B total parameters with 32B active per token, a 23-trillion-token training run, and a hybrid reasoning switch that flips between “think-out-loud” and terse answers based on task demands. There’s also a slimmer GLM-4.5-Air (106B/12B active) for teams who can’t babysit a mega-model.
Why it stands out
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ARC trifecta focus. Across 12 benchmarks, GLM-4.5 places #3 overall and #2 on agentic suites—with marquee scores like 91.0 on AIME’24, 64.2 on SWE-bench Verified, and 70.1 on TAU-Bench. It also reports 26.4 on BrowseComp for web agents, near OpenAI’s o4-mini-high in the authors’ runs.
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Parameter-efficient MoE. Compared to some giant peers, GLM-4.5 keeps active params modest while stacking deeper layers, 96 attention heads, partial RoPE, QK-Norm, and a built-in MTP layer for speculative decoding.
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Hybrid reasoning as a product feature. Both GLM-4.5 and Air support thinking (for complex tool use) and non-thinking (instant replies) modes from the same checkpoint.
The training recipe (quick hits)
A two-stage pretraining + mid-training stack mixes high-quality web, multilingual, code, math/science, then adds repo-level code, synthetic reasoning, 128K-token long-context, and agent trajectories to push real software-engineering and planning skills. Post-training distills expert Reasoning, Agent, and General models into one hybrid generalist, followed by targeted RL (including a “pathology RL” cleanup pass).
What you can actually download
Zhipu has published code, evals, and model cards on GitHub; weights are also listed on Hugging Face. The team pitches GLM-4.5 as agent-first and ships a simple eval harness to reproduce scores.
Bottom line
Open-source has plenty of great single-skill models. GLM-4.5 is aiming for a different bullseye: one backbone that can browse, reason, and patch code without feeling second-tier. If the reported ARC numbers hold up in the wild, this could become the go-to open checkpoint for production-grade agents.
Paper link: arXiv 2508.06471 (PDF)
