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DeepSeek V4.1 Flash
latestModel IDdeepseek-ai/DeepSeek-V4.1-Flash
DeepSeek-V4.1-Flash is the lightweight flagship of DeepSeek's new architecture family, packing 552B total MoE parameters to deliver flagship-surpassing intelligence across key benchmarks, including out-performing DeepSeek-V4-Pro. It adopts a Causal Encoder-Decoder asymmetric design with 8B active parameters for input and 16B for output, and offers native multimodal visual understanding. KV Cache usage is cut to one-quarter of the previous generation's HBM and one-eighth of its SSD storage, dramatically lowering costs for long-context and agentic workloads. With a 1M-token context window and up to 384K-token maximum output, it balances high throughput, low latency, and exceptional cost efficiency.
1,000,000 Tokens ContextFast InferenceCodingFunction CallingMultilingual
Peak
Save 20%$0.41$0.33
/ 1M input tokens
$1.63$1.3
/ 1M output tokens
Cache pricing
/ 1M tokens
Implicit cache
$0.0402$0.0322
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-V4.1-Flash",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=384000,
temperature=1,
top_p=1,
stream=False,
extra_body={
"enable_thinking": True,
}
)
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-V4.1-Flash",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=384000,
temperature=1,
top_p=1,
stream=False,
extra_body={
"enable_thinking": True,
}
)
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-V4.1-Flash",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=384000,
temperature=1,
top_p=1,
stream=False,
extra_body={
"enable_thinking": True,
}
)
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-V4.1-Flash",
messages=[
{
"role": "user",
"content": "Explain the main benefits of using a chat completion API for text generation."
}
],
max_tokens=384000,
temperature=1,
top_p=1,
stream=False,
extra_body={
"enable_thinking": True,
}
)
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