Z.ai: GLM 5V Turbo

GLM 5V Turbo is a model from Z.ai, released by the provider on 2026-04-01 under the model id z-ai/glm-5v-turbo. Z.ai describes it as its first native multimodal agent foundation model, built for vision-based coding and agent-driven tasks. It sits in the text category on api14, which reflects the fact that it returns text, while its inputs go well beyond text.

The model accepts image, video, and text as input and produces text as output. It handles those modalities natively rather than through separate preprocessing stages, which matters when a single request mixes a screenshot, a clip, and written instructions. The context window is 202752 tokens, and the maximum output is 131072 tokens. That combination supports long inputs and long generations in the same call, so you are not forced to compress a large body of material before asking for a detailed answer.

Z.ai positions GLM 5V Turbo for vision-based coding and agent-driven work. In practice that means tasks where the model has to look at something and then act on it: reading a user interface screenshot or a video of a session and writing or fixing the code behind it, working through a repository alongside visual references, or carrying out multi-step agent workflows that require long-horizon planning. Text-only tasks such as summarization, extraction, and structured generation also fit, since text is one of the accepted inputs. The reasoning controls and tool support listed below make it usable as the decision-making component in an agent loop rather than only as a single-turn responder.

You call it through api14's OpenAI-compatible API using the model id "z-ai/glm-5v-turbo". Because the endpoint follows the OpenAI schema, existing clients and SDKs work with a change of base URL and model name. The supported request parameters are include_reasoning, max_tokens, reasoning, response_format, temperature, tool_choice, tools, top_k, and top_p. In short, you can cap output length, tune sampling with temperature, top_p, and top_k, request structured output via response_format, expose functions through tools and tool_choice, and control reasoning with the reasoning and include_reasoning flags.

The main thing to check before committing is output modality. GLM 5V Turbo returns text only, so if you need generated images, audio, or other non-text output, a different model is the better fit. Likewise, if your workload is purely text and does not benefit from visual input or agent-style tool use, the multimodal capability here may be more than you need. For mixed image, video, and text inputs that end in text, whether code, plans, or structured data, this model is designed for exactly that shape of task.