Skip to main content
DeepSeek is a very powerful AI dialogue system that can generate smooth and natural replies in just a few seconds by inputting prompts. DeepSeek-V3 stands out in the industry with its excellent language understanding and generation capabilities, and today, DeepSeek-V3 has been widely applied across various industries and fields, with its influence becoming increasingly significant. Whether for daily conversations, creative writing, or professional consulting and coding, DeepSeek-V3 can provide astonishing intelligent assistance, greatly enhancing human work efficiency and creativity. This document mainly introduces the usage process of the DeepSeek Chat Completion API, allowing us to easily utilize the official DeepSeek dialogue functionality.

​
Application Process

To use the DeepSeek Chat Completion API, you can first visit the DeepSeek Chat Completion API page and click the “Acquire” button to obtain the credentials needed for the request: If you are not logged in or registered, you will be automatically redirected to the login page inviting you to register and log in. After logging in or registering, you will be automatically returned to the current page. When applying for the first time, there will be a free quota provided, allowing you to use the API for free.

​
Basic Usage

Next, you can fill in the corresponding content on the interface, as shown in the figure:

When using this interface for the first time, we need to fill in at least three pieces of content: one is authorization, which can be selected directly from the dropdown list. The other parameter is model, which is the category of the DeepSeek official model we choose to use. Here we mainly have 4 types of models; details can be found in the models we provide. The last parameter is messages, which is an array of our input questions. It is an array that allows multiple questions to be uploaded simultaneously, with each question containing role and content. The role indicates the role of the questioner, and we provide three identities: user, assistant, and system. The other content is the specific content of our question. You can also notice that there is corresponding code generation on the right side; you can copy the code to run directly or click the “Try” button for testing. Common optional parameters:
  • max_tokens: Limits the maximum number of tokens for a single reply.
  • temperature: Generates randomness, between 0-2, with larger values being more divergent.
  • n: How many candidate replies to generate at once.
  • response_format: Sets the return format.

After the call, we find the return result as follows: 
{
  "id": "chatcmpl-050bf20a-ebcd-498a-bf6e-63ee0738013b",
  "object": "chat.completion",
  "created": 1764846609,
  "model": "deepseek-v3.2-exp",
  "usage": {
    "prompt_tokens": 8,
    "completion_tokens": 11,
    "total_tokens": 19
  },
  "choices": [
    {
      "index": 0,
      "message": {
        "content": "Hello! 😊 How can I help you today?",
        "role": "assistant"
      },
      "refs": null,
      "logprobs": null,
      "finish_reason": "stop",
      "service_tier": null
    }
  ]
}
The return result contains multiple fields, described as follows:
  • id: The ID generated for this dialogue task, used to uniquely identify this dialogue task.
  • created: The creation time information of this dialogue task.
  • model: The selected DeepSeek official model.
  • choices: The response information provided by DeepSeek for the question.
  • usage: The token statistics for this Q&A pair.
Among them, choices contains the response information from DeepSeek, and the choices inside it is the response information from DeepSeek, as shown in the figure.

As can be seen, the content field inside choices contains the specific content of DeepSeek’s reply.

​
Streaming Response

This interface also supports streaming responses, which is very useful for web integration, allowing the webpage to achieve a character-by-character display effect. If you want to return responses in a streaming manner, you can change the stream parameter in the request header to true. Modify as shown in the figure, but the calling code needs to have corresponding changes to support streaming responses.

After changing stream to true, the API will return the corresponding JSON data line by line, and we need to make corresponding modifications at the code level to obtain the line-by-line results. Python sample calling code: 
import requests

url = "https://api.acedata.cloud/deepseek/chat/completions"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "deepseek-v3",
    "messages": [{"role":"user","content":"hello"}],
    "stream": True
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)
The output effect is as follows: 
data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": "Hello", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": "!", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": "", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": " 😊", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": " How", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": " can", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": " I", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": " assist", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": " you", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": " today", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": "?", "role": "assistant"}, "logprobs": null, "finish_reason": null, "index": 0}], "usage": null}

data: {"id": "o7X27b1-2kFHot-97098d957dd1d39a-PDX", "object": "chat.completion.chunk", "created": 1755437709, "model": "deepseek-v3", "system_fingerprint": null, "choices": [{"delta": {"content": "", "role": "assistant"}, "logprobs": null, "finish_reason": "stop", "index": 0}], "usage": {"prompt_tokens": 4, "completion_tokens": 12, "total_tokens": 16, "prompt_tokens_details": {"cached_tokens": 0, "text_tokens": 0, "audio_tokens": 0, "image_tokens": 0}, "completion_tokens_details": {"text_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0}, "input_tokens": 0, "output_tokens": 0, "input_tokens_details": null}}

data: [DONE]
You can see that there are many data in the response, and the choices in data are the latest response content, consistent with the content introduced above. The choices are the newly added response content, and you can integrate it into your system based on the results. At the same time, the end of the streaming response is determined by the content of data. If the content is [DONE], it indicates that the streaming response has completely ended. The returned data result has multiple fields, which are described as follows:
  • id, the ID generated for this dialogue task, used to uniquely identify this dialogue task.
  • model, the DeepSeek model selected from the official website.
  • choices, the response information provided by DeepSeek for the question words.
JavaScript is also supported, for example, the streaming call code for Node.js is as follows: 
const options = {
  method: "post",
  headers: {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
  },
  body: JSON.stringify({
    "model": "deepseek-v3",
    "messages": [{"role":"user","content":"hello"}],
    "stream": true
  })
};

fetch("https://api.acedata.cloud/deepseek/chat/completions", options)
  .then(response => response.json())
  .then(response => console.log(response))
  .catch(err => console.error(err));
Java sample code: 
JSONObject jsonObject = new JSONObject();
jsonObject.put("model", "deepseek-v3");
jsonObject.put("messages", [{"role":"user","content":"hello"}]);
jsonObject.put("stream", true);
MediaType mediaType = "application/json; charset=utf-8".toMediaType();
RequestBody body = jsonObject.toString().toRequestBody(mediaType);
Request request = new Request.Builder()
  .url("https://api.acedata.cloud/deepseek/chat/completions")
  .post(body)
  .addHeader("accept", "application/json")
  .addHeader("authorization", "Bearer {token}")
  .addHeader("content-type", "application/json")
  .build();

OkHttpClient client = new OkHttpClient();
Response response = client.newCall(request).execute();
System.out.print(response.body!!.string())
Other languages can be rewritten accordingly; the principle is the same.

​
Multi-turn Dialogue

If you want to integrate multi-turn dialogue functionality, you need to upload multiple question words in the messages field. The specific examples of multiple question words are shown in the image below:

Python sample call code: 
import requests

url = "https://api.acedata.cloud/deepseek/chat/completions"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "model": "deepseek-v3",
    "messages": [{"role":"user","content":"Hello"},{"role":"assistant","content":"Hi! How can I assist you today?"},{"role":"user","content":"What I say just now?"}]
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)
By uploading multiple questions, you can easily achieve multi-turn conversations and receive the following response: 
{
  "id": "as-8g3qzbsw2b",
  "object": "chat.completion",
  "created": 1755437895,
  "model": "deepseek-v3",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "You just said:  \n\n**\"Hello\"**  \n\nAnd I responded with:  \n\n**\"Hi! How can I assist you today?\"**  \n\nThen you followed up with:  \n\n**\"What I say just now?\"**  \n\nLet me know how I can help! 😊"
      },
      "finish_reason": "stop",
      "flag": 0
    }
  ],
  "usage": {
    "prompt_tokens": 22,
    "completion_tokens": 57,
    "total_tokens": 79
  }
}
As you can see, the information contained in choices is consistent with the basic usage content, which includes the specific responses from DeepSeek for multiple dialogues, allowing you to answer corresponding questions based on multiple dialogue contents.

​
Error Handling

When calling the API, if an error occurs, the API will return the corresponding error code and message. For example:
  • 400 token_mismatched: Bad request, possibly due to missing or invalid parameters.
  • 400 api_not_implemented: Bad request, possibly due to missing or invalid parameters.
  • 401 invalid_token: Unauthorized, invalid or missing authorization token.
  • 429 too_many_requests: Too many requests, you have exceeded the rate limit.
  • 500 api_error: Internal server error, something went wrong on the server.

​
Error Response Example

{
  "success": false,
  "error": {
    "code": "api_error",
    "message": "fetch failed"
  },
  "trace_id": "2cf86e86-22a4-46e1-ac2f-032c0f2a4e89"
}

​
Conclusion

Through this document, you have learned how to easily implement the conversation function of the official DeepSeek using the DeepSeek Chat Completion API. We hope this document helps you better integrate and use the API. If you have any questions, please feel free to contact our technical support team.