DeepSeek V4 Pro 0813 quietly released

Using the Responses API 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 from openai import OpenAI client = OpenAI(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