Z.ai: GLM 5.3 Prime

Z.ai: GLM 5.3 Prime is a text model from Z.ai, the company behind the GLM family. It is the high-speed variant of Z.ai's GLM-5.3, inheriting the full capabilities of that model while delivering 1.5 to 2 times the output throughput through inference acceleration. If you have used other GLM releases, this one sits at the top of the line as the Prime edition, aimed at workloads where generation speed matters as much as quality.

The model takes text in and returns text out, so it is not a vision or audio model. Its context window is 1,000,000 tokens, which is large enough to hold entire codebases, long document sets or extended conversation histories in a single request. The maximum output is 131,072 tokens, so long-form generations are possible without truncation in most cases. Reasoning is part of the feature set: the model exposes a reasoning parameter, a reasoning_effort control and an include_reasoning flag, which lets you decide whether reasoning content is returned alongside the final answer.

Those capabilities point to a few practical uses. Large-context analysis is the obvious one: feeding a full repository, a contract bundle or a research corpus and asking for summaries, comparisons or extraction. Agentic and tool-driven workflows fit well because the model supports tools and tool_choice, so it can call functions and act on their results. Structured output is another fit, since response_format is available for JSON-style responses. The reasoning controls suit tasks that need deliberate multi-step thinking, such as debugging, planning or mathematical work, and the throughput advantage helps when you are generating many completions or long documents in parallel.

You call it through api14's OpenAI-compatible API using the model id "z-ai/glm-5.3-prime". Because the interface follows the OpenAI schema, existing OpenAI SDKs and clients work with a change of base URL and model id. The supported request parameters are frequency_penalty, include_reasoning, logprobs, max_tokens, presence_penalty, reasoning, reasoning_effort, response_format, seed, stop, temperature, tool_choice, tools, top_k, top_logprobs and top_p. That covers sampling controls, determinism via seed, stop sequences, token limits, structured output, tool use and reasoning configuration.

There is no stated limitation beyond the text-only modality and the output cap of 131,072 tokens. If your task depends on image or audio input, this model is not the right choice, since it accepts text only. For everything else, the combination of a 1M-token context, tool support, structured output and reasoning controls makes it a reasonable default to evaluate first.