DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks.

Parameters

For a more detailed explanation on possible parameters, refer to OpenAI’s Create chat completion documentation.

Field	Type	Definition	Default Value
`model`	str	Required. The model key to be used. Use `deepseek-ai/deepseek-v4.1-flash`.	`deepseek-ai/deepseek-v4.1-flash`
`messages`	arr	Required. Conversation history. Each object has a `role` (`user` or `assistant`) and `content` (str).	-
`stream`	bool	Routes the request to the streaming path vs non-streaming path.	`false`
`max_completion_tokens`	int	Output token cap.	-
`temperature`	float	Randomness. Greedy sampling at `0` ; identical results are not guaranteed.	`1`
`top_p`	float	Nucleus sampling cutoff.	`1`
`top_k`	int	Limits sampling to the top K tokens.	-
`reasoning_effort`	str	Constrains effort on reasoning for reasoning models. Supported values are `none`, `low`, `high`, `max`. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response. Setting this value as `none` disables thinking.	`high`
`n`	int	How many chat completion choices to generate for each input message. Currently, we only support `1`.	`1`
`presence_penalty`	float	Number between `-2.0` and `2.0`. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics.	`0`
`frequency_penalty`	float	Number between `-2.0` and `2.0`. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.	`0`
`logprobs`	bool	Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.	`false`
`max_thinking_tokens`	int	Maximum number of thinking tokens allowed before generating the final answer.	-

Request Format

from openai import OpenAI

client = OpenAI(
    base_url="https://api-cdn.deploy.com/api/v1",
    api_key="DEPLOY_API_KEY",
)
response = client.chat.completions.create(
    model="deepseek-ai/deepseek-v4.1-flash-1017",
    messages=[{"role": "user", "content": "Explain TCP and UDP in two sentences."}],
)

print(response.choices[0].message.content)

curl https://api-cdn.deploy.com/api/v1/chat/completions \
  -H "Authorization: Bearer $DEPLOY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/deepseek-v4.1-flash-1017",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Response Format

{
  "id": "bf7eb425-c8ee-11f1-bc46-67542ee84316",
  "object": "chat.completion",
  "created": 1792106317,
  "model": "deepseek-ai/deepseek-v4.1-flash",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I help you today?",
        "reasoning_content": "The user just said \"Hello!\" That's a simple greeting. I should respond warmly and openly, inviting them to share what they need. No need for tools or complex analysis here—just a friendly, natural reply that sets a helpful tone. I'll keep it simple and engaging."
      },
      "finish_reason": "stop",
      "logprobs": null
    }
  ],
  "usage": {
    "prompt_tokens": 32,
    "completion_tokens": 68,
    "total_tokens": 100,
    "prompt_tokens_details": { "cached_tokens": 0 },
    "completion_tokens_details": { "reasoning_tokens": 58 }
  }
}

⌘I