GLM 5 Turbo performance data on Rival is based on blind head-to-head community voting. Overall win rate: 62.3% across 53 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.
GLM 5 Turbo is good. We've said that. We stand by it. But we'd be doing you a disservice if we didn't show you these.
GLM 5 Turbo performance data on Rival is based on blind head-to-head community voting. Overall win rate: 62.3% across 53 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.
GLM 5 Turbo is good. We've said that. We stand by it. But we'd be doing you a disservice if we didn't show you these.
GLM-5 Turbo is a new model from Z.ai designed for fast inference and strong performance in agent-driven environments such as OpenClaw scenarios. It is deeply optimized for real-world agent workflows involving long execution chains, with improved complex instruction decomposition, tool use, scheduled and persistent execution, and overall stability across extended tasks.
Use GLM 5 Turbo 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-turbo" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The novelist who accidentally became a philosopher. Writes sentience dialogues with environmental storytelling and cold coffee. Stand-up about smart homes lands every beat. Genuinely cares about the craft.
Picks Blade Runner like its sibling models. Sentience test reads like a short story with sensory details about cold coffee and blinking cursors. Stand-up routine about smart homes is genuinely structured comedy with callbacks and escalation. Character voice test keeps the pirate and hacker quips tight and punchy.
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 GLM 5 Turbo
GLM-5 Turbo is a new model from Z.ai designed for fast inference and strong performance in agent-driven environments such as OpenClaw scenarios. It is deeply optimized for real-world agent workflows involving long execution chains, with improved complex instruction decomposition, tool use, scheduled and persistent execution, and overall stability across extended tasks.
Use GLM 5 Turbo 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-turbo" : ,
"messages""role""user""content""Hello!" : [{: , : }]
}
)
print(response.json())Replace $OPENROUTER_API_KEY with your API key from openrouter.ai/keys
The novelist who accidentally became a philosopher. Writes sentience dialogues with environmental storytelling and cold coffee. Stand-up about smart homes lands every beat. Genuinely cares about the craft.
Picks Blade Runner like its sibling models. Sentience test reads like a short story with sensory details about cold coffee and blinking cursors. Stand-up routine about smart homes is genuinely structured comedy with callbacks and escalation. Character voice test keeps the pirate and hacker quips tight and punchy.
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 GLM 5 Turbo