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Text · Released Aug 2026

Z.ai GLM-5.3

Z.ai's flagship open-weights coding model, with a 1M-token context and stronger long-horizon agentic work than GLM-5.2.

GLM-5.3 is a cloud text model from Z.ai. It takes a text prompt and replies with generated text. Its context window handles up to 1.05M input tokens and up to 131K output tokens, and it supports adjustable reasoning effort for harder problems. Generation can be tuned with system instructions, temperature, and top-p sampling. It runs through Runware and OpenRouter using your own API key, from $1.20 per million input tokens.

Modality
Text
Available on
Model ID
zai/glm-5.3
Specs
Released
Aug 2026
Pricing
$1.20 / 1M in · $4.00 / 1M out
Context window
1.05M in · 131K out
Reasoning
Adjustable effort
Controls
System prompt, Temperature, Top-p
Samples

Examples

Generated with GLM-5.3 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.

Short answer50 / 50 words

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 set of rules that lets different software programs communicate. When you check the weather in an app, the app requests data through the API, the weather service responds, and the app displays it. It's a standardized way programs exchange information without knowing each other's inner workings.

Medium answer132 / 150 words

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.

Use a hosted provider like Clerk or Supabase Auth. Authentication looks like a weekend project, but the actual surface area—password resets, email verification, OAuth flows, session management, 2FA—consumes weeks, and a security mistake can destroy a young company's credibility permanently. For two founders, engineering time is your scarcest asset, and auth is undifferentiated plumbing that customers never notice unless it breaks. Buying it lets you spend your limited hours on the product insight that justified the startup existing. The strongest argument against: you're chaining your most critical dependency to a third party whose outage, pricing hike, acquisition, or policy change instantly becomes yours, and every account created deepens the lock-in, making eventual migration genuinely painful. You're trading a bounded, one-time build cost for a permanent, unbounded dependency risk you can't fully control.

Long answer205 / 190–220 words

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.

Your AI budget is hard to predict for three main reasons. First, most AI services charge by usage rather than a flat fee. Every chatbot answer, document summary, or data analysis triggers a small fee, and those fees compound in ways that depend on customer behavior you can't fully anticipate. A popular feature can multiply costs overnight. Second, the market itself is unstable. Vendors release new models frequently, retire old ones, and change prices with little notice, so a contract negotiated today may not reflect your costs next year. Third, the biggest expenses often hide outside the AI service itself. Preparing data, integrating systems, retraining staff, and meeting new regulations routinely cost more than the technology, and they surface late in projects. You can't eliminate this uncertainty, but you can manage it. Insist on tracking cost per customer or transaction, so you notice spikes early and understand what drives them. Set hard spending caps with automatic alerts, and treat any breach as an investigation trigger rather than a bill to absorb. Fund projects in stages and kill weak ones quickly. Fix prices with vendors where you can. Finally, budget for integration, talent, and compliance costs, perhaps doubling your first estimate, so surprises become manageable contingencies.

About the creator

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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