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GLM-5.2

GLM-5.2 model image

GLM-5.2

credit: Zhipu AI

thinking mode · OpenCode single agent · 1 xhigh subagents split across the two game prompts.

model dossier

Model sheet

Playable artifacts and benchmark context for this model page.

Road Hopper
2 builds
Robot-Filled Maze Shooter
2 builds
Ribbit Rush
0 builds
Builds
4 recorded outputs
Method
1 xhigh subagents
Runtime
OpenCode single agent
Quant
thinking mode
Bench
No local throughput sweep recorded

Generated game outputs

Versions are listed first for selection. Embedded outputs remain below for direct review.

Road Hopper 2 versions

road-hopper_glm-5-2_00 · Road Hopper · v1fullscreen ↗ · compare
road_hopper_glm_5_2_opencode · Road Hopper · GLM-5.2 OpenCode evalfullscreen ↗ · compare
Road Hopper v1 GLM-5.2 road-hopper_glm-5-2_00fullscreen ↗
Road Hopper GLM-5.2 OpenCode eval GLM-5.2 road_hopper_glm_5_2_opencodefullscreen ↗

Robot-Filled Maze Shooter 2 versions

maze_glm-5-2_00 · Robot-Filled Maze Shooter · v1fullscreen ↗ · compare
maze_glm_5_2_opencode · Robot-Filled Maze Shooter · GLM-5.2 OpenCode evalfullscreen ↗ · compare
Robot-Filled Maze Shooter v1 GLM-5.2 maze_glm-5-2_00fullscreen ↗
Robot-Filled Maze Shooter GLM-5.2 OpenCode eval GLM-5.2 maze_glm_5_2_opencodefullscreen ↗
UNDER RAPID CONSTRUCTION - WILL CHANGE
why

Local agents need public test loops

Working on local agents matters because these tests are the first step toward an R&D base-model trust score: a practical signal for choosing which architectures and models deserve deeper research and development.

Agentic Arcade is a playable hello world for that work. Anyone can open the games, review the artifacts, and compare model behavior. The mission is maximal distribution of intelligence to all people.