Z.ai: GLM 5.3 (batch)

GLM 5.3 (batch) is a text model from Z.ai, listed on api14 under the model id z-ai/glm-5.3:batch. Z.ai describes it as a large-scale reasoning model built for complex software engineering and long-horizon agent tasks. The batch variant is the version you call through this endpoint, and it keeps the same text-only input and output profile as the base model.

The model accepts text and returns text, so it fits text generation, analysis and transformation work rather than image or audio tasks. Its context window is 1,048,576 tokens, which is large enough to hold very long documents, extended conversation histories or sizeable codebases in a single request. The maximum output is 131,072 tokens, so long answers, multi-file code changes and extended reasoning traces are possible within one call. Reasoning behaviour is exposed through parameters such as reasoning, reasoning_effort and include_reasoning, which lets you control whether reasoning content is returned alongside the final answer.

Those capabilities point to a few practical uses. Software engineering tasks are a natural fit: reading across many files, tracing dependencies and producing patches or refactors that span a large codebase. Long-horizon agent work is another, where the model plans over many steps and calls tools as it goes. Because the context window is large, you can also use it for document-heavy work such as summarising or querying long reports, contracts or transcripts without chunking them first. Structured extraction and classification are supported too, since the model accepts response_format and structured_outputs.

Calling it through api14 works the same way as any OpenAI-compatible chat completion: send your request to the chat completions endpoint with the model field set to z-ai/glm-5.3:batch. The supported parameters are frequency_penalty, include_reasoning, logit_bias, max_tokens, min_p, presence_penalty, reasoning, reasoning_effort, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k and top_p. Tool use is available through tools and tool_choice, and sampling can be tuned with temperature, top_p, top_k, min_p and the penalty parameters. Use max_tokens to cap output and seed when you need repeatable sampling.

Two limits are worth noting before you commit. Input and output are text only, so if your task depends on images, audio or other modalities, a different model will be needed. The batch designation also means this is the batch-oriented entry rather than an interactive one, so check that its latency profile suits your application. For workloads that fit text in and text out, though, the combination of a 1M-token context and a 131k-token output ceiling covers a wide range of long-context and agentic jobs.