Z.ai: GLM 5 performance data on Rival is based on blind head-to-head community voting. Overall win rate: 53.8% across 212 duels. 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 48 challenges.
These are the models that show up when Z.ai: GLM 5 doesn't. Or when it does, but you want a second opinion. Which is healthy.
Z.ai: GLM 5 performance data on Rival is based on blind head-to-head community voting. Overall win rate: 53.8% across 212 duels. 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 48 challenges.
These are the models that show up when Z.ai: GLM 5 doesn't. Or when it does, but you want a second opinion. Which is healthy.
GLM-5 is Z.ai's flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-grade performance on large-scale programming tasks, rivaling leading closed-source models. With advanced agentic planning, deep backend reasoning, and iterative self-correction, GLM-5 moves beyond code generation to full-system construction and autonomous execution.
Use Z.ai: GLM 5 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" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The staff engineer who writes design docs before touching code. Favors decomposition, explicit constraints, and repeatable execution over flashy one-shot answers.
Handles complex prompts by establishing architecture first, then iterating with explicit checkpoints. Strong on engineering tasks that require sustained context and tool-using workflows.
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.
48 outputs from Z.ai: GLM 5
GLM-5 is Z.ai's flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-grade performance on large-scale programming tasks, rivaling leading closed-source models. With advanced agentic planning, deep backend reasoning, and iterative self-correction, GLM-5 moves beyond code generation to full-system construction and autonomous execution.
Use Z.ai: GLM 5 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" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The staff engineer who writes design docs before touching code. Favors decomposition, explicit constraints, and repeatable execution over flashy one-shot answers.
Handles complex prompts by establishing architecture first, then iterating with explicit checkpoints. Strong on engineering tasks that require sustained context and tool-using workflows.
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.
48 outputs from Z.ai: GLM 5