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Z.ai · A model to run on your own server

GLM-5.2

Model files can be downloaded and run on your own server, giving organizations greater control over their data. However, this requires very powerful hardware.

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Costs
Variable · online service $1.40 per million input tokens and $4.40 per million output tokens; self-hosting requires powerful hardware
Speed
Medium to slow; generates large amounts of text
Availability
It can be downloaded, modified and also used through several online services
Input length
Very long input · up to 1 million tokens (small pieces of text)
Inputs
Primarily text; depends on how it is run

Data checked . Release date unverified.

Indicative capability profile

Where the model is strong

The five levels are our clear summary of the results below. They are not a ranking that applies to every task.

Analysis Very good at solving specialist problems among downloadable models.
Coding Good results in SciCode and terminal tasks.
Autonomous work across multiple steps Suitable for autonomous work in multiple steps under your own supervision.
Writing and content The main advantage is the ability to self-host, not text writing.
Speed Produces long outputs and is demanding to self-host.

Ideal use

When to choose it

  • Running on your own or a rented server
  • Working with sensitive data that must remain under the company's control
  • Multi-step tasks with the option to modify the model

Usage boundaries

When to choose another model

  • Running on a standard personal computer
  • Teams that do not want to manage powerful servers

Traceable supporting sources

Data and measurement sources

Distinguish between the manufacturer's documentation and the results of a specific test. Measurements also depend on the settings and the task set used.

Artificial Analysis

Intelligence Index v4.1

51 points

The highest-rated downloadable model in the measurement from 16 June 2026.

Open original source ↗
Artificial Analysis

Terminal-Bench · SciCode

78 % · 50 %

Very good result in coding and autonomous terminal work.

Open original source ↗

Next step

Compare the model using your actual task.

A benchmark narrows the selection. A short trial on your data determines the choice.

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