Is it good enough?

Capabilities & modalities

GLM is a text-focused family with a clear tilt toward coding, tool use, and agentic workloads. Per aggregated third-party evaluation and Zhipu's technical reports (ev-perf-coding, ev-glm46), GLM-4.6 is among the stronger open models for real-world coding and agentic tasks, and competitive on general capability. The family spans a wide size ladder - from a 9B dense model to the 355B/32B MoE - so you can trade capability for serving cost. Choose GLM when coding/agentic performance under a permissive MIT license matters, and account for its China-aligned behaviour (see Assess).

Context window & long-context behaviour

Context length varies by variant, so confirm the exact number on the specific model card:

• GLM-4.6 - 200K tokens (the family's longest).
• GLM-4.5 / GLM-4.5-Air - up to 128K (per checkpoint).
• GLM-4-32B-0414 / GLM-4-9B-0414 - 32K native, up to 128K extended.

Marked partial: per-variant limits differ and OneHill has not independently measured effective long-context recall this session, so treat the published maximums as the architectural ceiling rather than a verified quality guarantee at full length.

Prompt format & chat template

GLM ships a chat template (with a thinking / agentic mode) in its tokenizer configuration. Use it rather than hand-rolling role markers:

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Refactor this function..."},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

Applying the template keeps you correct across the GLM-4.5/4.6 and 0414 checkpoints and handles the thinking/agentic output format; hand-rolled prompts commonly cause garbled or over-verbose output.

Language coverage

GLM is bilingual-strong in Chinese and English, with broader multilingual coverage beyond those two. Per-language depth varies and is not exhaustively documented, so this item is partial: validate any non-Chinese/English target with your own per-language evaluation before relying on it in production.

Function / tool calling

GLM-4.6 is explicitly positioned for agentic tool use and function calling, including MCP-style workflows (ev-perf-coding) - this is one of the family's strengths. Exact tool-calling schema support is per-checkpoint, so confirm the format on the specific model card and test your tool schema before production. Marked partial because schema details vary across variants and OneHill has not independently verified them this session.