Z.ai: GLM 5.2 performance data on Rival is based on blind head-to-head community voting. All vote data is part of Rival's open dataset of 21,000+ human preference judgments across 200+ AI models. Model responses are curated from 53 challenges.
Z.ai: GLM 5.2's competitors exist and they've been quietly putting in work. We thought you should know.
Z.ai: GLM 5.2 performance data on Rival is based on blind head-to-head community voting. All vote data is part of Rival's open dataset of 21,000+ human preference judgments across 200+ AI models. Model responses are curated from 53 challenges.
Z.ai: GLM 5.2's competitors exist and they've been quietly putting in work. We thought you should know.
GLM-5.2 is Z.ai's flagship model built for the era of long-horizon tasks. With a genuinely usable 1M-token context window, it holds project-level engineering context, executes long-running tasks more reliably, follows engineering standards more consistently, and can carry a project from requirements all the way to multi-platform deployment in a single task.
Use Z.ai: GLM 5.2 in your applications via the OpenRouter API. Copy the code below to get started.
import requests
response = requests.post(
"https://openrouter.ai/api/v1/chat/completions" ,
headers={
"Authorization""Bearer $OPENROUTER_API_KEY" : ,
"Content-Type""application/json" :
},
json={
"model""z-ai/glm-5.2" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
Unique words vs. total words. Higher = richer vocabulary.
Average words per sentence.
"Might", "perhaps", "arguably" per 100 words.
**Bold** markers per 1,000 characters.
Bullet and numbered list items per 1,000 characters.
Markdown headings per 1,000 characters.
Emoji per 1,000 characters.
"However", "moreover", "furthermore" per 100 words.
53 outputs from Z.ai: GLM 5.2
GLM-5.2 is Z.ai's flagship model built for the era of long-horizon tasks. With a genuinely usable 1M-token context window, it holds project-level engineering context, executes long-running tasks more reliably, follows engineering standards more consistently, and can carry a project from requirements all the way to multi-platform deployment in a single task.
Use Z.ai: GLM 5.2 in your applications via the OpenRouter API. Copy the code below to get started.
import requests
response = requests.post(
"https://openrouter.ai/api/v1/chat/completions" ,
headers={
"Authorization""Bearer $OPENROUTER_API_KEY" : ,
"Content-Type""application/json" :
},
json={
"model""z-ai/glm-5.2" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
Unique words vs. total words. Higher = richer vocabulary.
Average words per sentence.
"Might", "perhaps", "arguably" per 100 words.
**Bold** markers per 1,000 characters.
Bullet and numbered list items per 1,000 characters.
Markdown headings per 1,000 characters.
Emoji per 1,000 characters.
"However", "moreover", "furthermore" per 100 words.
53 outputs from Z.ai: GLM 5.2