GLM
The GLM series: Chinese open-weight at the top of the benchmarks.
Olya's profile
- What sets it apart
- GLM is Zhipu AI's (Z.ai) model family, Chinese-built, with one clear distinction: versions up to GLM-4.7 ship open-weight under an MIT license, downloadable from Hugging Face and usable commercially, while the flagship GLM-5.2 runs via API with a context window up to one million tokens. It is tuned mainly for coding and agentic work — function calling, structured output, MCP integration — more than for general chat.
- Strengths
- On coding benchmarks it holds its own against far pricier models: GLM-4.7 beats several rivals on LiveCodeBench and SWE-bench Verified while costing $0.60 per million input tokens and $2.20 output. Value for money is the real strength, and anyone wanting to cut costs to zero can run the open-weight versions on their own hardware.
- When to use it
- It pays off when you write or refactor code across multi-file projects and want to spend a fraction of a comparable Western model, or when you need an agentic flow with tool calls and long context. It is also a good pick if you must self-host for privacy or cost, leaning on the open releases.
- When to avoid it
- If you need multimodal capabilities (images, audio) look elsewhere: the GLM series covered here is text-only. The million-token window and the best performance sit on the flagship GLM-5.2, which is paid and consumes quota at a variable rate during peak hours; and open-weight does not mean free in practice, since the large models (hundreds of billions of parameters) demand serious hardware to run locally.