Loading model details...
Fetching the latest models and pricing from the API.
DeepSeek R1 0528
0528Model IDdeepseek-ai/DeepSeek-R1-0528
DeepSeek-R1-0528 is the May 2025 update to the original DeepSeek-R1. Built on the DeepSeek-V3 backbone with 671B total parameters and 37B active per inference pass (MoE), it achieves performance on par with OpenAI o1. Key improvements include 87.5% on AIME 2025 (up from 70%), reduced hallucinations, enhanced front-end capabilities, and newly added JSON output and function calling support.
128K Tokens ContextFast InferenceCodingFunction CallingMultilingual
Save 20%
$0.72$0.57
/ 1M input tokens
$2.87$2.29
/ 1M output tokens
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="deepseek-ai/DeepSeek-R1-0528",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=16384,
temperature=0.6,
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="deepseek-ai/DeepSeek-R1-0528",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=16384,
temperature=0.6,
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="deepseek-ai/DeepSeek-R1-0528",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=16384,
temperature=0.6,
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="deepseek-ai/DeepSeek-R1-0528",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=16384,
temperature=0.6,
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