Loading model details...
Fetching the latest models and pricing from the API.
GLM 5
latestModel IDzai-org/GLM-5
GLM-5 is Zhipu AI's February 2026 flagship — a 744B-parameter sparse MoE (40B active) with Interleaved (deep) thinking that fuses DeepSeek Sparse Attention and Multi-Token Prediction for frontier reasoning over a 200K-token window.
200K Tokens ContextFast InferenceCodingFunction CallingMultilingual
Input <=32k
Save 20%$0.72$0.57
/ 1M input tokens
$3.23$2.58
/ 1M output tokens
Cache pricing
/ 1M tokens
Implicit cache
$0.14$0.12
Request (Non streaming)
from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="zai-org/GLM-5",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=4096,
temperature=0.7,
top_p=1,
stream=False,
extra_body={
"enable_thinking": False,
}
)
print(response.choices[0].message.content)from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="zai-org/GLM-5",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=4096,
temperature=0.7,
top_p=1,
stream=False,
extra_body={
"enable_thinking": False,
}
)
print(response.choices[0].message.content)Example response
A chat completion API provides a standard way to send conversational input and receive model-generated text in a single request. Key benefits include: • Interoperability: any client can use HTTP with JSON request and response bodies. • Flexibility: system prompts, user messages, and parameters such as temperature and max tokens are easy to configure. • Observability: responses typically include token usage fields for cost and performance tracking. A typical integration sends a POST request with the model name and messages array, then reads the assistant message from the first choice in the response.
Request (Non streaming)
from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="zai-org/GLM-5",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=4096,
temperature=0.7,
top_p=1,
stream=False,
extra_body={
"enable_thinking": False,
}
)
print(response.choices[0].message.content)from openai import OpenAI
# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
base_url="https://qubrid.com/v1",
api_key="QUBRID_API_KEY",
)
response = client.chat.completions.create(
model="zai-org/GLM-5",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=4096,
temperature=0.7,
top_p=1,
stream=False,
extra_body={
"enable_thinking": False,
}
)
print(response.choices[0].message.content)Example response
A chat completion API provides a standard way to send conversational input and receive model-generated text in a single request. Key benefits include: • Interoperability: any client can use HTTP with JSON request and response bodies. • Flexibility: system prompts, user messages, and parameters such as temperature and max tokens are easy to configure. • Observability: responses typically include token usage fields for cost and performance tracking. A typical integration sends a POST request with the model name and messages array, then reads the assistant message from the first choice in the response.
Streaming supported • Function calling supported • See all examples in Playground Open in Playground for streaming, files, and all parameters.
Open in Playground