Z.ai: GLM 5.3 FlashX

GLM 5.3 FlashX is a text generation model from Z.ai, listed on api14 under the model id z-ai/glm-5.3-flashx. It is the high-speed variant of Z.ai's GLM-5.3-Flash, and Z.ai describes it as a native multimodal model built on a hybrid sparse and linear attention architecture. Z.ai positions it for inference speeds of up to 200 tokens per second, which makes it the faster option within the GLM-5.3-Flash line rather than a differently sized model.

The model accepts text, images and video as input, and it returns text only. Its context window is 1,048,576 tokens, and the maximum output length is 131,072 tokens. Because the input side covers three modalities, you can send prompts that mix written instructions with image or video content in the same request, and the model will produce a text response such as an answer, a summary or generated content. Image and video understanding is native to the model rather than bolted on through a separate pipeline.

Those capabilities point to a fairly specific set of practical uses. Long documents, large codebases and lengthy transcripts fit comfortably inside the context window, so tasks like summarizing long reports, answering questions across a whole repository, or extracting structured data from accumulated records are reasonable fits. The multimodal input makes it useful for describing or reasoning about images, checking video content against a set of rules, or turning visual material into text form. The high output ceiling supports long-form generation and detailed reasoning traces, and the reported throughput suits interactive applications where response latency matters, such as chat interfaces and agent loops.

You call the model through api14's OpenAI-compatible API using the model id z-ai/glm-5.3-flashx. The supported request parameters are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, temperature, tool_choice, tools, top_k and top_p. In practice this means you can control sampling with temperature, top_p and top_k, cap the response with max_tokens, request structured output through response_format, give the model callable functions with tools and tool_choice, and manage its reasoning behaviour with the reasoning, reasoning_effort and include_reasoning parameters.

Choose another model when your task does not need multimodal input or such a large context. Text-only work with a smaller input size may be served just as well by a lighter text model, and if you do not need images or video, the extra input support offers no benefit. Also note that the model outputs text only, so it is not a fit if you need generated images, audio or video. Within its stated scope, though, it handles text, image and video input at a context length that covers most long-context and multimodal jobs.