GLM 5.2

Extracted Attributes

• License

  MIT License

• Developer

  Z.AI (formerly Zhipu AI)

• Model Type

  Open-weight Large Language Model

• Context Length

  1,000,000 tokens

• Training Hardware

  Huawei

• Training Methodology

  Distillation (AI)

• Maximum Output Tokens

  128,000 tokens

Timeline

• Z.AI officially introduces GLM-5.2, highlighting its 1M-token context and long-horizon task capabilities. (Source: Web Search)

  2026-06-16

Z.ai

Knowledge Atlas Technology Joint Stock Co., Ltd., branded internationally as Z.ai, is a Chinese technology company specializing in artificial intelligence (AI). The company was formerly known as Zhipu AI outside China until its rebranding in 2025. Z.ai's flagship product is the GLM (General Language Model) family of large language models, which the company has released under the free and open-source MIT License since July 2025. As of 2024, it is one of China's "AI tiger" companies by investors and considered to be the third-largest LLM market player in China's AI industry according to the International Data Corporation. In January 2025, the United States Commerce Department blacklisted the company in its Entity List due to national security concerns.

Web Search Results

• What Is GLM 5.2? The Open-Weight Model Beating GPT 5.5 on ...

  GLM 5.2 is an open-weight model from Z.AI that can be self-hosted, fine-tuned, or accessed via API — giving teams flexibility that fully proprietary models don’t offer. Its design arena benchmark results place it above GPT 5.5 in human preference evaluations for design-related tasks, making it a strong choice for creative and design workflows. Multi-token prediction improves inference speed and reduces compute costs, making GLM 5.2 efficient for high-volume production use. Pricing is a genuine advantage — API costs sit below comparable proprietary models, and self-hosting eliminates API costs entirely. [...] ## A New Challenger in Open-Weight AI The open-weight model space has gotten a lot more interesting. GLM 5.2 — released by Z.AI, the company formerly known as Zhipu AI — has climbed to the top of design-focused AI evaluation leaderboards, outscoring proprietary models including GPT 5.5 in head-to-head comparisons. That’s a notable result for a model whose weights are publicly available and whose API pricing sits well below most Western alternatives. If you haven’t been following the GLM series closely, you’re not alone. Z.AI operates largely out of China and doesn’t get the same Western media coverage as OpenAI or Anthropic. But the benchmarks don’t care about press cycles — and GLM 5.2 is producing results worth paying attention to. [...] GLM 5.2 is the latest generation of that series — a large language model released as open-weight, meaning the model weights are publicly available for download, fine-tuning, and self-hosted deployment. It supports text and multimodal inputs, long-context reasoning, and coding tasks alongside its standout strength in design and creative visual understanding. ## One coffee.One working app. You bring the idea. Remy manages the project. WHILE YOU WERE AWAY ✓Designed the data model ✓Picked an auth scheme — sessions + RBAC ✓Wired up Stripe checkout ✓Deployed to production Live at yourapp.msagent.ai Image 7: RemyThe world's most powerful product manager agentTry Remy today

• GLM-5.2 - Overview - Z.AI DEVELOPER DOCUMENT

  Language Models # GLM-5.2 Copy page Copy page ## ​ Overview GLM-5.2 is a flagship model built for the era of long-horizon tasks. With truly usable 1M-token context, it has been tested to handle project-scale engineering context, delivering more stable long-task execution, more reliable adherence to engineering standards, and higher success rates in development scenarios. A single task can complete the full development workflow—from requirements to deployable products across multiple platforms. ## Positioning Flagship Foundation Model ## Input Modalities Text ## Output Modalitie Text ## Context Length 1M ## Maximum Output Tokens 128K ## ​ Capability ## Thinking Mode Offering multiple thinking modes for different scenarios [...] GLM-5.2 is well suited for testing rule understanding, state machine design, level structure, scoring logic, resource loading, interaction feedback, and settlement flows in mini game development. Compared with static pages, this type of task better demonstrates the model’s understanding of complex states, user paths, and product completeness.Recommended way to try it: Provide a complete but not overly detailed gameplay goal, and let the model first design the rules, then implement a runnable version: [...] GLM-5.2 is more stable in cross-file, multi-step, long-chain tasks. It first breaks down the goal, identifies dependencies and risks, then implements, verifies, and closes the task in stages. This makes it suitable for tasks that require continuous progress, such as module decoupling, API migration, directory restructuring, SDK adaptation, and cross-language refactoring.Recommended way to try it: Choose a medium-sized refactoring task, define clear boundaries, and enable `/goal` mode:

• GLM-5.2: Built for Long-Horizon Tasks - Z.ai

  GLM-5.2 also introduces effort level control, enabling users to explicitly balance model capability against task execution speed and computational cost. As shown in the figure, GLM-5.2 delivers substantially stronger agentic coding performance than GLM-5.1 at comparable token budgets, with its capability roughly positioned between Claude Opus 4.7 and Claude Opus 4.8 under similar token consumption. Moreover, the Max effort level allows users to allocate additional computation when higher performance is required in challenging tasks, further extending the model’s coding capability. This design gives users greater flexibility when using GLM-5.2 for coding tasks, allowing them to select the most suitable reasoning mode for different scenarios. [...] kernels, and developing production-grade services, GLM-5.2 still has room to grow, trailing Opus 4.8 by 13% while remaining second only to the Opus series. Across all three benchmarks, GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability. [...] Image 1 2026-06-16 · Research # GLM-5.2: Built for Long-Horizon Tasks Image 2Try it at Z.ai Image 3Call it at Z.ai Image 4Z.ai Coding Plan Image 5GitHub Image 6HuggingFace We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include:

• zai-org/GLM-5.2 · Hugging Face

  [Paper] [GitHub] ## Introduction We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: bench_52 bench_52 ## Benchmark

• GLM 5.2 in 9 Minutes - YouTube

  The video reviews GLM 5.2, ZAI’s latest flagship open-weight model, highlighting its competitiveness with closed frontier models like Opus 4.8 and GPT 5.5 across benchmarks, including cases where it outperforms GPT 5.5 and Fable 5. It covers key features such as a 1M-token context for long-horizon work, availability on Hugging Face under an MIT license, and deployment across multiple inference providers that can drive speed and lower prices. Using Artificial Analysis, the host discusses its intelligence index (~51), weighted cost per task (about $0.42 vs $0.83 for GPT 5.5 X-High), token usage patterns when “thinking” is increased, and typical per-token pricing. Benchmarks like Vending Bench and DeepSuite are discussed, followed by an OpenCode demo generating a SaaS landing page and [...] In this video, I'm going to be going over GLM 5.2, which is the latest flagship model from the team over at ZAI. Now, this model is incredible when we compare it to some of the Frontier closed models that are out there. Across a ton of different benchmarks, this model packs punches with models like Opus 4.8 as well as GPT 5.5. And there are some benchmarks that I'll show you a little bit later in the video where it does even outperform on some tasks like GPT 5.5 as well as in one case where it actually outperformed Fable 5. Now, in terms of some of the flagship features of the model, so it has a million tokens of context. You're going to be able to perform long horizon tasks. If you're using this in the context of something similar or within something like Claude code, Codex, open code,