GLM-4.7 & GLM-4 Guide 2026: Zhipu China LLM for Agents

Key takeaways

GLM-4.x and GLM-4.7 lines from Zhipu (智谱) often appear in enterprise agent shortlists for stable function calling and structured output—not always for raw coding cost leadership. Match GLM when tool schemas, Chinese enterprise workflows, or IDE integrations matter. Compare with Kimi on long docs (/en/articles/kimi-vs-glm-selection-guide-2026) and DeepSeek on coding value (/en/articles/china-llm-coding-assistant-2026).

GLM-4 and GLM-4.7 on this index

Queries “glm-4” and “glm 4.7” land on the same family. On Swift Horse, GLM-4.7 is the text-and-agent entry: agentic coding, long-horizon planning, tool collaboration, code generation, natural dialogue, and immersive writing. Parameter count, context window, and knowledge cutoff are not publicly disclosed in the catalog. Treat any number you see elsewhere as unverified until the BigModel console shows it.

GLM-4V is the vision sibling in this catalog, not a drop-in replacement for GLM-4.7 text agents. GLM-5 is listed separately as the flagship tier for heavier enterprise workloads. If a blog says “GLM-4” without a version, open both the GLM-4.7 page and the GLM-5 page and match the ID your API key actually returns.

A useful GLM-4 test is twenty of your own tool-calling prompts: same JSON schema, same tools, scored on schema validity, tool success, and whether the plan stays coherent after several steps. Put DeepSeek-V4-Pro beside it when the job is mostly coding cost, and Kimi K2.6 beside it when the job is a long PDF plus agents. Keep the winner only if it beats the alternate on your logs, not on a public screenshot.

GLM-4 vs GLM-4.7 vs GLM-5 tier

Model IDs change on the live console—never copy blog names blindly. GLM-4.7 class models often target upgraded reasoning and tool stability; GLM-5 tier targets flagship enterprise workloads. Run the same 20-prompt bake-off on your stack before contracts. API setup: /en/articles/glm-api-overseas-quickstart-2026.

When GLM wins vs loses

Wins: enterprise agents, JSON-heavy pipelines, regulated CN-language products. Loses: ultra-long PDF-only workloads (test Kimi), lowest-cost coding at scale (test DeepSeek), Alibaba-native multimodal (test Qwen). Hybrid stacks are normal.

Next steps on Swift Horse

GLM model page /en/models → vs Kimi /en/articles/kimi-vs-glm-selection-guide-2026 → JSON /en/articles/china-llm-json-structured-output-2026 → best-of /en/articles/best-chinese-llm-2026.

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