GLM-5 is Zhipu's fifth-generation frontier foundation model released February 2026, with 744 billion total parameters and 40 billion active in a Mixture-of-Experts configuration. Zhipu reports it was trained entirely on Huawei Ascend chips using the MindSpore framework, without NVIDIA hardware, a notable step toward large-model training independent of US semiconductor supply chains. The model reports open-source state-of-the-art results in coding and agentic tasks, with real-world programming capability that Zhipu benchmarks directly against Claude Opus 4.5. Trained on 28.5 trillion tokens, it integrates DeepSeek Sparse Attention to reduce deployment cost while preserving long-context capacity, with a 200K context window. Its reasoning-first design emphasizes thought quality and multi-step problem decomposition, making it strong for software engineering, mathematics, and frontier reasoning. Released under an MIT license, GLM-5 targets developers building state-of-the-art coding agents, research systems, and reasoning-intensive applications. Its roughly two-fold scale increase over GLM-4.5 combined with sparse activation aims to deliver capability gains while keeping inference practical.
GLM 5 runs on 4 different routes through the Opper gateway. Compare residency, ZDR, and training posture at a glance β full data-handling detail per route below.
Training posture across routes: No training on prompts by default.
Each route hosting GLM 5 has its own privacy posture, residency, and GDPR terms. Postures are maintained by Opper with a last-verification timestamp.
Zero data retention is available via Opper Enterprise contract. No training on customer data. US; unknown.
Zero data retention is on by default on Pay-as-you-go β no action required. No training on customer data. US; SCCs.
Zero data retention is available via Opper Enterprise contract. No training on customer data. US; SCCs; DPA available.
Zero data retention is available via Opper Enterprise contract. No training on customer data. US; SCCs; DPA available.
Independent benchmark scores β composite indices for reasoning, coding, and math, plus individual eval scores where available.
Call GLM 5 through the Opper gateway with one API key. Let your coding agent set it up, or call it directly β Opper is drop-in compatible with the OpenAI, Anthropic, and Google AI SDKs.
Copy this and paste it into your coding agent β Claude Code, Cursor, Codex, and more β and it'll wire up Opper for you.
import OpenAI from "openai";const client = new OpenAI({apiKey: process.env.OPPER_API_KEY,baseURL: "https://api.opper.ai/v3/compat",});const completion = await client.chat.completions.create({model: "deepinfra/zai-org/GLM-5",messages: [{ role: "user", content: "Hello" }],});console.log(completion.choices[0].message.content);
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