GLM-5.2, the Zhipu AI (Z.ai) model in numbers

GLM-5.2 at a glance

GLM-5.2 is a language model from Zhipu AI (Z.ai), released on June 13, 2026. That makes it 4 months old as of October 22, 2026. Its weights are published: you can download it and run it on your own servers, or use one of the 89 API providers that list it on models.dev. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money.

We file it under open-weight models (68 models in this category on the site).

How much does GLM-5.2 cost?

GLM-5.2 costs $1.2 per million input tokens and $4.18 per million output tokens (developer's price, checked on October 22, 2026).

To put it in perspective: reading 20,000 tokens (about thirty pages) and writing 2,000 costs roughly $0.032; a thousand of those tasks, $32.27.

That is 4.2 times the median output price of open-weight models ($1).

Tokens read back from cache cost $0.30 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

Measured performance

According to results published by Epoch AI (checked on October 22, 2026), GLM-5.2 has a capability index of 70.5 out of 100, computed on 6 benchmarks from our basket.

It is not among the top 30 of the weekly ranking.

• GPQA Diamond (PhD-level science questions): 91.9% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 86.4% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 59.2% — the best tracked model, GPT-6 Astra, reaches 93.7%
• SWE-bench Verified (fixing real software bugs): 78.7% — the best tracked model, Claude Opus 4.7, reaches 83.5%

What it can do

Its context window reaches 1,000,000 tokens, roughly 1,500 pages of text at 500 words per page: enough to load a whole book or a mid-sized codebase. A single answer can run to 131,072 tokens.

It accepts as input: text.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

The weights are published on Hugging Face (zai-org/GLM-5.2). See also our list of open-source models.

Who is it for?

Given its category, GLM-5.2 is mainly aimed at projects where you want control over your data or hosting, or want to adapt the model to your field.

Its SWE-bench Verified result (78.7%, above the median of tracked models) makes it a serious candidate for coding agents.

Its very large context window makes it worth considering for analysing large sets of documents in a single request.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Comparable models

In the same category, the models closest to GLM-5.2 are:

• GLM-5.1 (Zhipu AI (Z.ai), index 70.9)
• Kimi K2 Thinking (Moonshot AI, index 69.6)
• GLM-5.3-Flash (Zhipu AI (Z.ai), index 71.9)
• DeepSeek V4 Pro 0813 (DeepSeek, index 72.2)