Z.AI: GLM 4.5 performance data on Rival is based on blind head-to-head community voting. Overall win rate: 67.8% across 363 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 54 challenges.
These are the models that show up when Z.AI: GLM 4.5 doesn't. Or when it does, but you want a second opinion. Which is healthy.
Z.AI: GLM 4.5 performance data on Rival is based on blind head-to-head community voting. Overall win rate: 67.8% across 363 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 54 challenges.
These are the models that show up when Z.AI: GLM 4.5 doesn't. Or when it does, but you want a second opinion. Which is healthy.
GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of up to 128k tokens. GLM-4.5 delivers significantly enhanced capabilities in reasoning, code generation, and agent alignment. It supports a hybrid inference mode with two options, a "thinking mode" designed for complex reasoning and tool use, and a "non-thinking mode" optimized for instant responses.
Use Z.AI: GLM 4.5 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-4.5" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The earnest explainer who treats every prompt as a chance to give a TED talk. Names characters "NEXUS-7" and sets everything in minimalist university offices.
Writes "Key Satirical Elements Used" after a satire prompt because it cannot help explaining its own work. Provides 6 options when you asked for 1. Solid but predictable.
Taste is judged on an uncapped scale where 100 is the reference, originality first. The space past 100 is the craft today's models rarely reach.
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.
54 outputs from Z.AI: GLM 4.5
GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of up to 128k tokens. GLM-4.5 delivers significantly enhanced capabilities in reasoning, code generation, and agent alignment. It supports a hybrid inference mode with two options, a "thinking mode" designed for complex reasoning and tool use, and a "non-thinking mode" optimized for instant responses.
Use Z.AI: GLM 4.5 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-4.5" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The earnest explainer who treats every prompt as a chance to give a TED talk. Names characters "NEXUS-7" and sets everything in minimalist university offices.
Writes "Key Satirical Elements Used" after a satire prompt because it cannot help explaining its own work. Provides 6 options when you asked for 1. Solid but predictable.
Taste is judged on an uncapped scale where 100 is the reference, originality first. The space past 100 is the craft today's models rarely reach.
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.
54 outputs from Z.AI: GLM 4.5