Loading model details...
Fetching the latest models and pricing from the API.
Kimi K2 Thinking
latestModel IDmoonshotai/Kimi-K2-Thinking
Kimi K2 Thinking is the first open-weights model to achieve SOTA performance against leading closed-source models (GPT-5, Claude 4.5 Sonnet) across major benchmarks including HLE (44.9%), BrowseComp (60.2%), and SWE-Bench Verified (71.3%). Built on a 1T parameter MoE architecture with 32B active per token and native INT4 quantization via QAT, it maintains stable tool-use across 200–300 sequential calls within a 256K context window.
256K Tokens ContextFast InferenceCodingFunction CallingMultilingual
Save 20%
$0.72$0.57
/ 1M input tokens
$2.87$2.29
/ 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="moonshotai/Kimi-K2-Thinking",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=16384,
temperature=1,
top_p=0.95,
stream=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="moonshotai/Kimi-K2-Thinking",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=16384,
temperature=1,
top_p=0.95,
stream=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="moonshotai/Kimi-K2-Thinking",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=16384,
temperature=1,
top_p=0.95,
stream=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="moonshotai/Kimi-K2-Thinking",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=16384,
temperature=1,
top_p=0.95,
stream=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