Z.ai: GLM 4.7 Flash

GLM 4.7 Flash is a text generation model from Z.ai, released by the provider on 19 January 2026. Z.ai describes it as a 30B-class SOTA model that offers a new option balancing performance and efficiency, and notes that it is further optimized for agentic coding use cases, with strengthened coding capabilities and long-horizon task planning. It sits in the text category, so it is aimed at language tasks rather than image, audio or video work.

The model accepts text input and returns text output. It has a context window of 200,000 tokens, which is large enough to hold substantial source files, documentation sets or long conversation histories in a single request. The maximum output is 117,964 tokens, so responses can run well beyond a short answer when the task calls for it. Because the provider highlights agentic coding and long-horizon planning, the model is positioned for work where it must reason across many steps rather than produce a single isolated reply.

Those characteristics suggest a few practical uses. Agentic coding is the clearest fit: reading a codebase, planning a sequence of edits and carrying that plan across a long session. Long-horizon task planning follows from the same strengths, for example breaking a multi-stage job into ordered steps and tracking progress. The large context window also suits document analysis, summarisation of long inputs, and tool-driven workflows where the model calls functions and incorporates their results. Structured outputs and reasoning controls make it usable in pipelines that need predictable response shapes rather than free-form prose.

You call the model through api14's OpenAI-compatible API using the model id "z-ai/glm-4.7-flash". Because the interface follows the OpenAI format, existing OpenAI client libraries and request shapes work with a change of base URL and model id. The model supports a broad set of request parameters: temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty, repetition_penalty, max_tokens, stop, seed, logit_bias, logprobs, top_logprobs, response_format, structured_outputs, tools, tool_choice, reasoning and include_reasoning. That combination covers sampling control, deterministic runs via seed, token-level inspection through logprobs, and tool or structured output workflows.

One limit worth noting is modality: input and output are text only, so tasks involving images or audio need a different model. The 200,000 token context and 117,964 token output ceiling are generous but finite, so extremely large inputs still need chunking or retrieval. If your workload does not involve coding, planning or tool use, a simpler text model may be sufficient, but within its stated scope GLM 4.7 Flash is built for exactly these agentic and long-context jobs.