DeepSeek V4 Pro 0813 quietly released

To meet the demand for Codex, our API now supports the Responses API format, with the base_url being https://api.deepseek.com.

With a simple configuration, you can use DeepSeek models in Codex.

Integrating DeepSeek Models into Codex​

Please refer to Integrate with Codex.

Calling DeepSeek Models via the Responses API​

# Please install OpenAI SDK first: `pip3 install openai`
from openai import OpenAI

client = OpenAI(api_key="<your DeepSeek API Key>", base_url="https://api.deepseek.com")

response = client.responses.create(
    model="deepseek-v4-flash",
    instructions="You are a helpful assistant.",
    input="Hi, how are you?",
)

print(response.output_text)

Streaming​

Set stream: true to receive the response as a sequence of semantic server-sent events (SSE). Each event carries an event field indicating the event type, and a monotonically increasing sequence_number. The stream ends with a response.completed / response.incomplete / response.failed event — there is no data: [DONE] message.

stream = client.responses.create(
    model="deepseek-v4-flash",
    instructions="You are a helpful assistant.",
    input="Hi, how are you?",
    stream=True,
)

for event in stream:
    if event.type == "response.output_text.delta":
        print(event.delta, end="")

The full list of events:

Compatibility Details​

This section lists the compatibility details of the DeepSeek API with the Responses API. For the full Responses API format definition, please refer to the official OpenAI API reference.

Top-level Request Parameters​

Unsupported parameters are silently ignored and do not cause errors, so existing Responses API clients can connect without modification.

Input Items​

Tools​

Response Fields​

The response object is compatible with the OpenAI Responses API response structure. Fields that depend on unsupported capabilities always take fixed values (e.g. store: false, previous_response_id: null, parallel_tool_calls: true).

Token usage is returned in usage:

• input_tokens: number of input tokens, where input_tokens_details.cached_tokens is the number of tokens hitting the context cache
• output_tokens: number of output tokens, where output_tokens_details.reasoning_tokens is the number of chain-of-thought tokens