--- title: "GLM-4.5: Zhipu's agent-native 355B model that ships under a permissive MIT license" date: 2026-10-21 category: Foundation Models site: NeuroAI canonical: https://neuroai.site/a/na-model-glm45-agent-native language: en --- # GLM-4.5: Zhipu's agent-native 355B model that ships under a permissive MIT license > Zhipu AI (智谱), now branded Z.ai, released GLM-4.5, a 355B open-weight MoE built to reason, code, and call tools inside agents — free for commercial use under MIT. A product manager describes a website in a paragraph and hits enter. Minutes later a working prototype is live — search bar, pages, and a deployable build — assembled by a single model that reasoned, wrote the code, and wired up the tools. The lab behind that demo calls the approach "agent-native," and the model is GLM-4.5. ## One model for reasoning, coding, and tool use GLM-4.5 is a **large model (大模型)** from Zhipu AI (智谱华章), the Beijing company spun out of Tsinghua University, now using the international brand Z.ai. It launched on 28 July 2025 and the weights were open-sourced under the MIT license. The design goal was unusual: instead of shipping separate models for math, coding, and tool use, Zhipu folded all three into one set of weights. The idea is that agentic applications — software that plans, calls functions, browses, and writes code over many turns — should not have to stitch together several specialised models. Two sizes shipped: - **GLM-4.5**: 355 billion total parameters, 32 billion active, mixture-of-experts (混合专家, MoE). - **GLM-4.5-Air**: 106 billion total, 12 billion active — a lighter sibling for cheaper deployment. Zhipu frames the efficiency directly: GLM-4.5 has about half the parameters of DeepSeek-R1 and a third of Kimi K2, yet it reports competitive results on several standard benchmarks. ## Hybrid reasoning you can toggle A distinctive feature is **dual-mode (hybrid) reasoning**. A caller can switch between: - a **thinking mode** for hard reasoning and tool use, where the model deliberates before answering; - a **non-thinking mode** for instant responses. That matters for agents, because the same model can do a quick lookup and a deep multi-step plan without swapping models. The context window is 128K tokens, with up to 96K output tokens. Native function calling is built in, and Zhipu says GLM-4.5 reached a **90.6% tool-calling success rate** on its internal agent evaluations — the kind of number that matters more for real agent loops than for static Q&A leaderboards. ## How it scored On the 12-benchmark composite Z.ai published — covering agentic, reasoning, and coding tasks — the company reported GLM-4.5 ranked **third overall and first among open-source models**, behind only closed frontier systems. On individual tests Z.ai cited: - **MMLU-Pro: 84.6%** — academic knowledge. - **SWE-bench Verified: 64.2%** — real-world software engineering. - **τ-bench: ~70%** — agent tool use (Retail 79.7, Airline 60.4). - **BrowseComp: 26.4%** — open-ended web browsing, ahead of Claude 4 Opus (18.8%) in Z.ai's run. The model was also pre-trained on roughly 23 trillion tokens — about 15T of general text followed by 7–8T of code and reasoning data — then sharpened with reinforcement learning. ## Access and price GLM-4.5 is available three ways: - **Chat**: chat.z.ai and the Z.ai web app. - **API**: through BigModel.cn (Z.ai's platform), with one-click compatibility claimed for the Claude Code framework. - **Weights**: open on Hugging Face (zai-org/GLM-4.5) and ModelScope under MIT. The published API price was about **US$0.60 per million input tokens and US$2.20 per million output tokens** for GLM-4.5, with the Air variant at roughly US$0.20 / US$1.10 — several times cheaper than comparable Western frontier tiers and free for experimentation on the Flash variants. ## Why the MIT license matters MIT is about as permissive as open model licenses get: self-host, fine-tune, and ship commercially with no royalty. For enterprises that cannot send data to a closed API, that changes the calculus. A hospital, bank, or government office can run GLM-4.5 on its own hardware and keep the data inside its walls — something a closed model cannot offer. ## Honest limitations - **Benchmarks are vendor-reported.** The third-overall and tool-use figures come from Z.ai's own runs; independent reproductions are thinner than for some Western models. - **128K context, not 1M.** Rivals like MiniMax-M1 and Gemini 2.5 Pro handle far longer single-pass context, which matters for whole-codebase or whole-document tasks. - **It has been superseded internally.** Z.ai has since shipped GLM-4.6, GLM-4.7, and the GLM-5 line; GLM-4.5 is no longer the lab's flagship, though the weights remain live and MIT-licensed. - **Big to self-host.** At 355B parameters, full-precision serving needs serious multi-GPU hardware; GLM-4.5-Air exists precisely to ease that. - ** bilingual strength is the real edge.** GLM-4.5 is genuinely strong on Chinese-English tasks, which is where it tends to beat similarly sized Western-open models most clearly. ## What readers can do now - **Use the API or the free Flash tier first.** Point a Claude-Code-compatible agent at BigModel.cn or chat.z.ai to feel the tool-calling and coding behaviour before committing to infrastructure. - **Self-host the Air variant if hardware is tight.** GLM-4.5-Air (106B/12B) keeps most capability at a fraction of the memory and is the pragmatic on-prem choice for a single rich-GPU server. - **Keep data on your own metal with MIT.** If compliance forbids sending prompts to a third-party API, pull the MIT-licensed weights and serve them behind vLLM or SGLang — no usage restrictions, no per-call royalty. --- Published by NeuroAI (https://neuroai.site/) — https://neuroai.site/a/na-model-glm45-agent-native Free to quote with attribution and a link to the original.