Z.ai GLM-5.3 Prime
The high-speed variant of Z.ai's GLM-5.3, with the same capabilities at 1.5 to 2 times the output throughput and a higher price.
GLM-5.3 Prime is a cloud text model built by Z.ai. It takes a text prompt and replies with generated text. Its context window handles up to 1M 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 OpenRouter using your own API key, from $2.80 per million input tokens.
- Released
- Sep 2026
- Pricing
- $2.80 / 1M in · $8.80 / 1M out
- Context window
- 1M in · 131K out
- Reasoning
- Adjustable effort
- Controls
- System prompt, Temperature, Top-p
Examples
Generated with GLM-5.3 Prime via OpenRouter. 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 set of rules that lets one piece of software talk to another. When an app shows you the weather or lets you log in with a different account, it's asking another service for information through its API, which decides what can be shared.
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 Auth0, Clerk, or Firebase Auth. Two founders have almost no slack for the work auth entails: password storage, session management, OAuth flows, MFA, rate limiting, and staying ahead of breaches. Getting any of it wrong is catastrophic and none of it differentiates your product. A hosted provider gives you battle-tested security in an afternoon, and the monthly cost is trivial next to the weeks of engineering time you'd otherwise burn. The strongest argument against this: auth is core infrastructure, and outsourcing it means you never escape the dependence. As you grow, provider pricing scales painfully, outages and API changes are outside your control, and migrating away later—after thousands of users depend on it—is harder than building it right in the first place. You're not avoiding the hard problem, just deferring it to a moment when it's most expensive to solve.
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.
Forecasting what your company will spend on artificial intelligence is difficult, and the causes are structural, not a failure of your finance team. First, most AI costs are usage-based. Unlike traditional software licenses priced per seat, AI is billed by volume: every query, document, or conversation triggers a charge. Costs therefore track employee adoption, which you cannot know in advance. Second, the market changes faster than procurement cycles. Vendors release new models, cut prices, retire older versions, and bundle AI features into tools you already pay for, sometimes with surcharges. A plan built on today's pricing can be obsolete within months. Third, experimentation distorts budgets. AI initiatives rarely deliver predictable returns; some pilots scale rapidly, others quietly die after consuming significant spend on data preparation, integration, and talent that never appears as a line item labeled AI. What you can do: insist that every AI expense, including cloud compute and embedded vendor fees, is tagged and reported centrally so nothing hides inside departmental budgets. Set usage thresholds that trigger alerts before overruns occur. Give each pilot a fixed budget ceiling with explicit criteria for scaling or shutting down. Finally, measure cost per business outcome, such as resolved tickets, so you judge value rather than spend.
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.
z.ai ↗Z.ai's natively multimodal GLM-5.3 tier for efficient coding and long-horizon agent tasks, with image input and tool calling.
Z.ai's flagship open-weights coding model, with a 1M-token context and stronger long-horizon agentic work than 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.