Z.ai: GLM 4.6V

GLM 4.6V is a large multimodal model from Z.ai, listed on api14 under the model id z-ai/glm-4.6v. It sits in the text category, and Z.ai describes it as being built for high fidelity visual understanding and long context reasoning across images, documents and mixed media. It was released by the provider on 8 December 2025.

The model accepts image, text and video as input and returns text. Its context window is 131072 tokens, and the maximum output is 32768 tokens. Z.ai notes that it handles complex page layouts and works over long contexts spanning up to 128K tokens, which makes it suitable for material where the visual structure matters as much as the words. Because output is text only, it is a reading and reasoning model rather than a generator of images or video.

Practical uses follow directly from that mix. You can send scanned documents or photographed pages and ask for the contents to be extracted, summarised or restructured while the layout is taken into account. Screenshots and interface captures can be turned into written descriptions or checked against expectations. Video input supports tasks such as summarisation, question answering about what happens in a clip, or pulling structured notes from recorded material. Long mixed media inputs suit research over collected documents, multi page reports and archives where you want a single pass of reasoning rather than chunk by chunk stitching.

You call it through api14's OpenAI compatible API using the model id z-ai/glm-4.6v. The supported request parameters are frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, temperature, tool_choice, tools, top_k and top_p. Tool calling and structured output are therefore available through tools, tool_choice and response_format, and the reasoning and include_reasoning parameters let you control how reasoning is surfaced. Sampling can be tuned with temperature, top_p, top_k, seed, the penalty parameters and stop sequences, while max_tokens caps the response length.

If your task does not involve images, documents or video, a text only model may be simpler and cheaper to run, since the multimodal input path exists for visual and mixed media work. The video and image inputs also depend on what you actually need to extract; for plain text generation over long inputs, the visual capabilities add nothing. Otherwise, the combination of a 131072 token context, 32768 token maximum output and tool support covers most document, screenshot and video analysis workloads.