Z.ai: GLM 5 Turbo

Z.ai: GLM 5 Turbo is a text model from Z.ai, the provider behind the GLM family. It is positioned as a fast-inference model built for agent-driven environments, including OpenClaw style scenarios, and Z.ai describes it as deeply optimized for real-world agent workflows. It sits in the text category, so it is aimed at language tasks rather than image, audio or video work.

The model takes text in and returns text out. It has a context window of 202,752 tokens and can produce up to 131,072 tokens in a single response, which makes it suitable for long inputs and long outputs in the same request. Alongside ordinary text generation, it supports reasoning output and tool use, so it can be driven as the decision-making component of an agent loop rather than only as a chat model.

Those capabilities point to a fairly specific set of uses. Agent workflows are the clearest fit: tool calling, multi-step task execution and orchestration where the model has to decide which function to invoke and then work with the result. The large context window also suits long document analysis, summarization of extended material, and code or configuration work where the whole file or repository section needs to be in view. Because reasoning can be surfaced, it works for problems where you want the model's intermediate thinking exposed, and the response format parameter allows structured output for downstream parsing.

You call it through api14's OpenAI-compatible API using the model id "z-ai/glm-5-turbo". The supported request parameters are include_reasoning, max_tokens, reasoning, response_format, temperature, tool_choice, tools, top_k and top_p. Tool use is configured through tools and tool_choice, structured output through response_format, and sampling behaviour through temperature, top_k and top_p. Output length is bounded by max_tokens, and reasoning behaviour is controlled with reasoning and include_reasoning.

Since the model accepts only text input, tasks that need image or audio understanding will require a different model. If your workload is simple single-turn generation with no tools and no need for long context, the extra agent-oriented capabilities here may be more than you need, and a smaller text model could be the simpler choice.