GLM-5.2
Z.ai's flagship long-horizon coding model, with a 1M-token context and deep-thinking reasoning for end-to-end engineering work.
From Z.ai, GLM-5.2 is a cloud text model. It takes a text prompt and replies with generated text. Its context window handles up to 1M input tokens and up to 128K output tokens, and it supports adjustable reasoning effort for harder problems. Generation can be tuned with system instructions. It runs through Runware using your own API key, from $0.80 per million input tokens.
- Released
- 2 months ago (Jun 2026)
- Pricing
- $0.80 / 1M in · $2.55 / 1M out
- Context window
- 1M in · 128K out
- Reasoning
- Adjustable effort
- Controls
- System prompt
Examples
Generated with GLM-5.2 via Runware. The same three prompts run against every text model in the catalog, shown verbatim — so the only thing that changes between two models’ answers is the model.
Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.
An API is a bridge that allows different software programs to communicate. When you use an app to check the weather, the app uses an API to ask a separate weather database for the current temperature. The database then sends that information back through the API.
A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.
You should absolutely use a hosted authentication provider. For a two-person team, engineering hours are your most scarce resource. A hosted service lets you implement secure login in hours instead of weeks, completely bypassing the severe security risks and maintenance burden of building from scratch. This frees you to focus entirely on your core product. The single strongest argument against this is vendor lock-in combined with escalating costs. As your user base grows, hosted providers charge per active user, turning a basic utility into a compounding operational expense that eats into profit margins. Furthermore, if you eventually outgrow them, migrating your users away requires a painful, highly risky data migration effort that could have been avoided by owning the system from day one.
Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.
Predicting our spending on artificial intelligence is challenging because the technology behaves differently than traditional software. Unlike standard IT projects, AI requires three highly variable inputs that shift constantly. First, data preparation consumes massive, hidden resources. Before any algorithm works, we must clean, organize, and store vast amounts of data. The volume and quality of this data fluctuate, making initial costs difficult to estimate. Second, AI models degrade over time. As market conditions and customer behaviors change, our algorithms lose accuracy and require expensive retraining. This ongoing maintenance creates recurring, unpredictable costs that standard software simply does not have. Third, the compute power needed to run these models scales unpredictably. Every single query or prediction requires server capacity. If user adoption spikes unexpectedly, our cloud computing bills will surge immediately. To manage this uncertainty, we need to shift our budgeting approach. Instead of fixed annual budgets, we should adopt a staged investment model. We can fund small pilot projects with strict success metrics, then scale funding only for models that prove their business value. Additionally, we must require technical teams to provide total cost of ownership estimates, including ongoing data storage and computational expenses, before approving any new AI initiative. This approach ensures financial control.
Z.ai
Z.ai develops the open GLM family of language models, built for long-horizon agentic coding and end-to-end engineering work over very large contexts.
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