GLM 5.2 arrives for coding, with MIT open weights planned

r/LocalLLaMAJun 13, 2026 · 20h ago

GLM 5.2 has been deployed in the GLM Coding Plan. It supports a , which means it can take in very long instructions, files, or conversation history at once. It has two thinking modes, max and high, and z.ai recommends max for coding work.

The API and are expected in about a week. Early coding tests say it is slower than GLM 5.1 at around 70

, but it appears to spend more time reasoning and produce stronger results. In a one-shot Pac-Man coding test, the game was close to working, with the main visible bug being ghosts getting stuck after leaving their starting area.

Among the tested models, it was ranked first in that test, ahead of Qwen 3.6 27b. The surrounding discussion also asks what should come first: a longer , , no price increase, or another priority.

Radar's take

For people tracking AI agents and cost control, this is worth watching. A could help handle larger codebases and longer task histories without cutting information into many smaller prompts. But a bigger window can also invite waste: long inputs still cost tokens, and slower reasoning may raise practical costs if pricing is not favorable.

The are the bigger long-term lever. If the release happens as promised, teams may be able to test local or private s instead of relying only on hosted APIs. The key question is not just whether GLM 5.2 can code well, but whether it can do useful agent work with fewer retries, fewer prompts, and acceptable speed.

Key points

GLM 5.2 is now available in the GLM Coding Plan.
It supports a for very long inputs.
z.ai recommends the max thinking mode for coding.
The API and are expected in about a week.
Early tests suggest slower speed than GLM 5.1 but better coding output.

Sources covering this story (3)

r/LocalLLaMAGLM 5.2 arrives for coding, with MIT open weights planned ↗
r/LocalLLaMAGLM 5.2 is out - open weights to be released next week. How did it do on my one-shot Pac-Man test? ↗
r/LocalLLaMAGLM-5.2 next week, open weight, MIT ↗

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