A Chinese lab quietly did something the big American closed models usually avoid: it handed the weights of a frontier-scale model to the public, for free, with almost no strings attached. You can download GLM-4.5, run it, fork it, or build a product on it — and the license will not come after you.
For developers outside China, that is the part worth understanding. The model is not a trimmed "community" edition; it is the real thing.
What actually shipped
Zhipu AI (智谱AI), the Beijing company behind the GLM (General Language Model / 大模型) family, released GLM-4.5 on 28 July 2025. Two variants:
- GLM-4.5 — 355 billion total parameters, 32 billion active, a mixture-of-experts (MoE) design.
- GLM-4.5-Air — 106 billion total, 12 billion active, a lighter sibling that still lands near far larger proprietary models on reasoning benchmarks.
Both are open-weights under the MIT License and published on Hugging Face and ModelScope. MIT is about as permissive as open-source licenses get — commercial use included, attribution aside.
The "agent" framing is the point
Zhipu did not market this as a chatbot. It positioned GLM-4.5 as a foundation model for intelligent agents (智能体) — software that plans, calls tools, and acts over many steps rather than answering one prompt. The design choices follow that goal:
- Hybrid reasoning. A single set of weights switches between a "thinking" mode for complex reasoning and tool use, and a "non-thinking" mode for instant responses. You do not need two models; you flip a switch.
- 128k context window and native function calling, the plumbing agents need to hold long state and invoke external tools.
- A reported 100 tokens/second on the high-speed API tier.
Zhipu says the model was trained on roughly 15 trillion tokens of general pre-training plus 8 trillion tokens of targeted training in code, reasoning, and agentic tasks, then sharpened with reinforcement learning.
Where it sits on the leaderboard
On a 12-benchmark composite covering reasoning, coding, and agentic tasks, Zhipu reported GLM-4.5 at 3rd place globally — behind only the very top proprietary models — and first among open-source models at the time of release. On agentic benchmarks (τ-bench, Berkeley Function Calling Leaderboard v3) it claimed parity with Claude 4 Sonnet, and on the web-browsing BrowseComp test it reported 26.4%, ahead of Claude-4-Opus (18.8%).
Those are the company's own benchmark numbers, and they should be read as marketing-adjacent until independently reproduced. But the architecture claim is concrete and verifiable: a 355B MoE that activates only 32B per token is a genuine efficiency story, letting a capable model run on far less hardware than its parameter count suggests.
The price, converted
Zhipu priced the API aggressively: input at 0.8 RMB per million tokens, output at 2 RMB per million tokens — roughly US$0.11 / HK$0.87 to send a million input tokens and US$0.28 / HK$2.18 for a million output tokens. The company also made it compatible with the Claude Code agent framework on its BigModel.cn platform, a deliberate move to let developers port agent workflows with minimal rewiring.
Why an open-weight Chinese model matters
Most of the world's most capable models are behind APIs you cannot inspect. Zhipu's choice to release GLM-4.5 with MIT terms puts a large-model (大模型) capability in the hands of anyone who can host it — researchers in countries without API access, companies wary of depending on a single foreign provider, and regulators who want to audit what they deploy.
It also reflects a broader Chinese strategy: open-weight releases as a way to build developer ecosystems and standards, not just products. Zhipu, spun out of Tsinghua University, is one of the labs pursuing that path most aggressively.
What readers can do now
- If you build agents, pull GLM-4.5 from Hugging Face or ModelScope and test the thinking/non-thinking switch on a real multi-step task; the 32B-active MoE is light enough to self-host on modest clusters.
- If you are locked to a US API, treat GLM-4.5 as a drop-in option for cost-sensitive or compliance-sensitive workloads, especially given Claude Code compatibility.
- If you follow the open-model race, watch Zhipu's benchmark claims against independent reruns — the "3rd globally / #1 open" line is the vendor's, and the real test is community reproduction.
Honest limitations
Verified facts: Zhipu AI (智谱AI) released GLM-4.5 on 28 July 2025, open-weights on Hugging Face and ModelScope under the MIT License, GLM-4.5 at 355B total / 32B active and GLM-4.5-Air at 106B total / 12B active, hybrid thinking/non-thinking modes, 128k context, native function calling, and API pricing of 0.8 RMB / 2 RMB per million tokens — confirmed by Zhipu's official z.ai blog and corroborated by Shanghai Securities News (上证报), Beijing News (新京报), and the Beijing Mentougou government portal (30 July 2025). The training-data scale (15T general + 8T targeted) is from the Beijing Mentougou and Southern Metropolis Daily coverage. The "3rd globally / #1 open-source / parity with Claude 4 Sonnet / 26.4% BrowseComp" performance claims are Zhipu's own benchmark results reported by those outlets and are not independently re-verified in this article; treat them as vendor claims pending community reproduction. Currency converted at ~7.1 RMB/USD and ~1.09 HKD/RMB: input ≈ US$0.11 / HK$0.87 per million tokens, output ≈ US$0.28 / HK$2.18. No political figures or stock-touting asserted. Analysis is current to 28 September 2026 and is not investment advice.