OpenAI Responses API Application and Usage
OpenAI has recently provided an interface for creating model responses. It allows for text or image input to generate text or image output. The model can call your own custom code or use built-in tools, such as web search or file search, to use your own data as input for the model response.
This document mainly describes the usage process of the OpenAI Responses API, which allows us to easily utilize the official OpenAI model response creation feature.
¶ Application Process
To use the OpenAI Responses API, first go to the 辰汐ai Console to obtain your API Token for future use.
If you are not logged in or registered, you will be automatically redirected to the login page to invite you to register and log in. After completing this, you will be automatically returned to the current page.
One API Token can call all services on the platform without needing to apply separately for each service. The first application will grant a free quota for a trial experience; when the quota is insufficient, you can recharge the general balance in the console.
📘 Complete documentation: OpenAI Responses API →
¶ Basic Usage
Next, you can fill in the corresponding content on the interface, as shown in the image:
When using this interface for the first time, we need to fill in at least three pieces of information: one is authorization, which can be selected directly from the dropdown list. The other parameter is model, which is the category of the OpenAI ChatGPT model we choose to use. Here we mainly have 20 types of models; details can be found in the models we provide. The last parameter is input, 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, which you can copy to run directly or click the "Try" button for testing.
Common optional parameters:
max_tokens: Limits the maximum number of tokens for a single response.temperature: Generates randomness, between 0-2, with larger values being more divergent.n: How many candidate responses to generate at once.response_format: Sets the return format.tools: Function/tool call definitions.background: Whether to run asynchronously in the background.
After the call, we find the returned result as follows:
{
"id": "resp_68a98322e3c88191a027de2711a02a490554cad0b36c0400",
"object": "response",
"created_at": 1755939618,
"status": "completed",
"background": false,
"content_filters": null,
"error": null,
"incomplete_details": null,
"instructions": null,
"max_output_tokens": null,
"max_tool_calls": null,
"model": "gpt-5.5",
"output": [
{
"id": "msg_68a98323422c8191a7f383eea48ba5160554cad0b36c0400",
"type": "message",
"status": "completed",
"content": [
{
"type": "output_text",
"annotations": [],
"text": "Hello! How can I assist you today?"
}
],
"role": "assistant"
}
],
"parallel_tool_calls": true,
"previous_response_id": null,
"prompt_cache_key": null,
"reasoning": {
"effort": null,
"summary": null
},
"safety_identifier": null,
"service_tier": "default",
"store": true,
"temperature": 1,
"text": {
"format": {
"type": "text"
}
},
"tool_choice": "auto",
"tools": [],
"top_p": 1,
"truncation": "disabled",
"usage": {
"input_tokens": 8,
"input_tokens_details": {
"cached_tokens": 0
},
"output_tokens": 10,
"output_tokens_details": {
"reasoning_tokens": 0
},
"total_tokens": 18
},
"user": null,
"metadata": {}
}
The returned result contains multiple fields, described as follows:
id: The ID generated for this dialogue task, used to uniquely identify this dialogue task.model: The selected OpenAI ChatGPT model.output: The response information provided by ChatGPT for the input question.usage: Token statistics for this Q&A pair.
Among them, output contains the response information from ChatGPT, and the output inside it is from ChatGPT, as shown in the image.
You can see that the content field in output contains the specific content of ChatGPT's reply.
¶ Streaming Response
This interface also supports streaming responses, which is very useful for web integration, allowing the webpage to display results word by word.
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 image, 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/openai/responses"
headers = {
"accept": "application/json",
"authorization": "Bearer {token}",
"content-type": "application/json"
}
payload = {
"model": "gpt-4.1",
"input": [{"role":"user","content":"Hello"}],
"stream": True
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)
The output effect is as follows:
data: {"type": "response.created", "sequence_number": 0, "response":
{"id": "resp_68a9837bb9bc8190b403947311db6faa0721186e8fbb89d0", "object": "response", "created_at": 1755939707, "status": "in_progress", "background": false, "content_filters": null, "error": null, "incomplete_details": null, "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "model": "gpt-4.1-data", "output": [], "parallel_tool_calls": true, "previous_response_id": null, "prompt_cache_key": null, "reasoning": {"effort": null, "summary": null}, "safety_identifier": null, "service_tier": "auto", "store": true, "temperature": 1.0, "text": {"format": {"type": "text"}}, "tool_choice": "auto", "tools": [], "top_p": 1.0, "truncation": "disabled", "usage": null, "user": null, "metadata": {}}, "model": "gpt-4.1"}
data: {"type": "response.in_progress", "sequence_number": 1, "response": {"id": "resp_68a9837bb9bc8190b403947311db6faa0721186e8fbb89d0", "object": "response", "created_at": 1755939707, "status": "in_progress", "background": false, "content_filters": null, "error": null, "incomplete_details": null, "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "model": "gpt-4.1-data", "output": [], "parallel_tool_calls": true, "previous_response_id": null, "prompt_cache_key": null, "reasoning": {"effort": null, "summary": null}, "safety_identifier": null, "service_tier": "auto", "store": true, "temperature": 1.0, "text": {"format": {"type": "text"}}, "tool_choice": "auto", "tools": [], "top_p": 1.0, "truncation": "disabled", "usage": null, "user": null, "metadata": {}}, "model": "gpt-4.1"}
data: {"type": "response.output_item.added", "sequence_number": 2, "output_index": 0, "item": {"id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "type": "message", "status": "in_progress", "content": [], "role": "assistant"}, "model": "gpt-4.1"}
data: {"type": "response.content_part.added", "sequence_number": 3, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "part": {"type": "output_text", "annotations": [], "text": ""}, "model": "gpt-4.1"}
data: {"type": "response.output_text.delta", "sequence_number": 4, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "delta": "Hello", "model": "gpt-4.1"}
data: {"type": "response.output_text.delta", "sequence_number": 5, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "delta": "!", "model": "gpt-4.1"}
data: {"type": "response.output_text.delta", "sequence_number": 6, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "delta": " How", "model": "gpt-4.1"}
data: {"type": "response.output_text.delta", "sequence_number": 7, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "delta": " can", "model": "gpt-4.1"}
data: {"type": "response.output_text.delta", "sequence_number": 8, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "delta": " I", "model": "gpt-4.1"}
data: {"type": "response.output_text.delta", "sequence_number": 9, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "delta": " help", "model": "gpt-4.1"}
data: {"type": "response.output_text.delta", "sequence_number": 10, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "delta": " you", "model": "gpt-4.1"}
data: {"type": "response.output_text.delta", "sequence_number": 11, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "delta": " today", "model": "gpt-4.1"}
data: {"type": "response.output_text.delta", "sequence_number": 12, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "delta": "?", "model": "gpt-4.1"}
data: {"type": "response.output_text.delta", "sequence_number": 13, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "delta": " 😊", "model": "gpt-4.1"}
data: {"type": "response.output_text.done", "sequence_number": 14, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "text": "Hello! How can I help you today? 😊", "model": "gpt-4.1"}
data: {"type": "response.content_part.done", "sequence_number": 15, "item_id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "output_index": 0, "content_index": 0, "part": {"type": "output_text", "annotations": [], "text": "Hello! How can I help you today? 😊"}, "model": "gpt-4.1"}
data: {"type": "response.output_item.done", "sequence_number": 16, "output_index": 0, "item": {"id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "type": "message", "status": "completed", "content": [{"type": "output_text", "annotations": [], "text": "Hello! How can I help you today? 😊"}], "role": "assistant"}, "model": "gpt-4.1"}
data: {"type": "response.completed", "sequence_number": 17, "response": {"id": "resp_68a9837bb9bc8190b403947311db6faa0721186e8fbb89d0", "object": "response", "created_at": 1755939707, "status": "completed", "background": false, "content_filters": null, "error": null, "incomplete_details": null, "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "model": "gpt-4.1-data", "output": [{"id": "msg_68a9837c49f081908f568bf9c6065c620721186e8fbb89d0", "type": "message", "status": "completed", "content": [{"type": "output_text", "annotations": [], "text": "Hello! How can I help you today? 😊"}], "role": "assistant"}], "parallel_tool_calls": true, "previous_response_id": null, "prompt_cache_key": null, "reasoning": {"effort": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "store": true, "temperature": 1.0, "text": {"format": {"type": "text"}}, "tool_choice": "auto", "tools": [], "top_p": 1.0, "truncation": "disabled", "usage": {"input_tokens": 8, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 11, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 19}, "user": null, "metadata": {}}, "model": "gpt-4.1"}
It can be seen that there are many `data` in the response, and the `delta` in `data` is the latest answer content, consistent with the content introduced above. The `delta` is the newly added answer content, and you can interface it into your system based on the results. At the same time, the end of the streaming response is determined based on the content of `data`. If the content of `type` is `response.completed`, it indicates that the streaming response has completely ended. The returned `data` result has multiple fields, which are described as follows:
- `item_id`, the ID generated for this dialogue task, used to uniquely identify this dialogue task.
- `type`, the type of the generated dialogue Responses task.
- `model`, the OpenAI ChatGPT model selected.
- `delta`, the response information provided by ChatGPT for the question.
JavaScript is also supported, for example, the streaming call code for Node.js is as follows:
```javascript
const options = {
method: "post",
headers: {
accept: "application/json",
authorization: "Bearer b82d32f570bc434d9ba9923aa0e7dce0",
"content-type": "application/json",
},
body: JSON.stringify({
model: "gpt-4.1",
input: [{ role: "user", content: "Hello" }],
stream: true,
}),
};
fetch("https://api.acedata.cloud/openai/responses", 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", "gpt-4.1");
jsonObject.put("input", [{"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/openai/responses")
.post(body)
.addHeader("accept", "application/json")
.addHeader("authorization", "Bearer b82d32f570bc434d9ba9923aa0e7dce0")
.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 interface the multi-turn dialogue function, you need to upload multiple question words in the input 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/openai/responses"
headers = {
"accept": "application/json",
"authorization": "Bearer {token}",
"content-type": "application/json"
}
payload = {
"model": "gpt-4.1",
"input": [{"role":"user","content":"Hello"},{"role":"assistant","content":"Hello! How can I help you today? 😊"},{"role":"user","content":"What did I just say?"}]
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)
By uploading multiple question words, you can easily achieve multi-turn dialogue and get the following response:
{
"id": "resp_68a989c03c508191a1dd82ce2e37e88a0932a4328c0a5d5b",
"object": "response",
"created_at": 1755941312,
"status": "completed",
"background": false,
"content_filters": null,
"error": null,
"incomplete_details": null,
"instructions": null,
"max_output_tokens": null,
"max_tool_calls": null,
"model": "gpt-4.1",
"output": [
{
"id": "msg_68a989c092e4819189821a9eb8247e1e0932a4328c0a5d5b",
"type": "message",
"status": "completed",
"content": [
{
"type": "output_text",
"annotations": [],
"text": "You just said \"Hello.\" \n\nWould you like to continue the conversation or ask a question?"
}
],
"role": "assistant"
}
],
"parallel_tool_calls": true,
"previous_response_id": null,
"prompt_cache_key": null,
"reasoning": {
"effort": null,
"summary": null
},
"safety_identifier": null,
"service_tier": "default",
"store": true,
"temperature": 1,
"text": {
"format": {
"type": "text"
}
},
"tool_choice": "auto",
"tools": [],
"top_p": 1,
"truncation": "disabled",
"usage": {
"input_tokens": 32,
"input_tokens_details": {
"cached_tokens": 0
},
"output_tokens": 20,
"output_tokens_details": {
"reasoning_tokens": 0
},
"total_tokens": 52
},
"user": null,
"metadata": {}
}
It can be seen that the information contained in output is consistent with the basic usage content, which includes the specific content of ChatGPT's responses to multiple dialogues, allowing for corresponding answers based on multiple dialogue contents.
¶ Visual Model
gpt-4o is a multimodal large language model developed by OpenAI, which adds visual understanding capabilities on the basis of GPT-4. This model can process both text and image inputs simultaneously, achieving cross-modal understanding and generation.
The text processing using the gpt-4o model is consistent with the basic usage content mentioned above. Below is a brief introduction on how to use the model's image processing capabilities.
The image processing capability of the gpt-4o model is mainly achieved by adding a type field to the original content content, which indicates whether the uploaded content is text or an image, thus utilizing the image processing capability of the gpt-4o model. The following mainly discusses how to call this function using Curl and Python.
- Curl script method
curl -X POST 'https://api.acedata.cloud/openai/responses' \
-H 'accept: application/json' \
-H 'authorization: Bearer {token}' \
-H 'content-type: application/json' \
-d '{
"model": "gpt-4.1",
"input": [
{
"role": "user",
"content": [
{"type": "input_text", "text": "what is in this image?"},
{
"type": "input_image",
"image_url": "https://cdn.acedata.cloud/e724d7f13d.png"
}
]
}
]
}'
- Python script method
import requests
url = "https://api.acedata.cloud/openai/chat/completions"
headers = {
"accept": "application/json",
"authorization": "Bearer {token}",
"content-type": "application/json"
}
payload = {
"model": "gpt-4.1",
"input": [
{
"role": "user",
"content": [
{"type": "input_text", "text": "what is in this image?"},
{
"type": "input_image",
"image_url": "https://cdn.acedata.cloud/e724d7f13d.png"
}
]
}
]
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)
Then you can get the following result, the field information in the result is consistent with the above text, specifically as follows:
{
"id": "resp_68a98c1bb784819e9b9f622007a2d37602483949012d2193",
"object": "response",
"created_at": 1755941915,
"status": "completed",
"background": false,
"content_filters": null,
"error": null,
"incomplete_details": null,
"instructions": null,
"max_output_tokens": null,
"max_tool_calls": null,
"model": "gpt-4.1",
"output": [
{
"id": "msg_68a98c1dd030819e97fb71e6ee33f5a902483949012d2193",
"type": "message",
"status": "completed",
"content": [
{
"type": "output_text",
"annotations": [],
"text": "This image shows a scenic path, possibly a boardwalk, running through a lush green field or meadow. The sky above is bright blue with some white clouds, and there are green trees and bushes in the background. It looks like a peaceful nature scene, possibly in a park, wetland, or prairie area. The image conveys a sense of tranquility and natural beauty."
}
],
"role": "assistant"
}
],
"parallel_tool_calls": true,
"previous_response_id": null,
"prompt_cache_key": null,
"reasoning": {
"effort": null,
"summary": null
},
"safety_identifier": null,
"service_tier": "default",
"store": true,
"temperature": 1,
"text": {
"format": {
"type": "text"
}
},
"tool_choice": "auto",
"tools": [],
"top_p": 1,
"truncation": "disabled",
"usage": {
"input_tokens": 1118,
"input_tokens_details": {
"cached_tokens": 0
},
"output_tokens": 75,
"output_tokens_details": {
"reasoning_tokens": 0
},
"total_tokens": 1193
},
"user": null,
"metadata": {}
}
It can be seen that the content of the answer is based on the image, so through the above two methods, the text and image processing capabilities of the gpt-4.1 model can be easily utilized.
In addition to gpt-4.1, there is a lower-cost model called gpt-4o-mini. gpt-4o-mini is the latest generation of large language models developed by OpenAI, which not only responds quickly but is also cheaper and supports multimodal capabilities. The use of vision features can refer to the content used for the gpt-4.1 model above.
¶ File processing model creation
Request example:
{
"model": "gpt-4.1",
"input": [
{
"role": "user",
"content": [
{ "type": "input_text", "text": "what is in this file?" },
{
"type": "input_file",
"file_url": "https://platform2.cdn.acedata.cloud/fish/64adc04b-c196-4a0f-9070-222ba101ce6c.wav"
}
]
}
]
}
Example result:
{
"id": "resp_68a98d7bb57c819ba25424f5f50a29a300a1af2af822e88a",
"object": "response",
"created_at": 1755942267,
"status": "completed",
"background": false,
"content_filters": null,
"error": null,
"incomplete_details": null,
"instructions": null,
"max_output_tokens": null,
"max_tool_calls": null,
"model": "gpt-4.1",
"output": [
{
"id": "msg_68a98d7d9b80819b9b0f09b7bcd00bf900a1af2af822e88a",
"type": "message",
"status": "completed",
"content": [
{
"type": "output_text",
"annotations": [],
"text": "The file you posted contains the **2024 annual letter to shareholders from Berkshire Hathaway Inc.**, written by Warren E. Buffett, Chairman of the Board. This document is a comprehensive communication that is typically included in Berkshire's annual report to shareholders.\n\n### What's Inside the File:\n\n#### 1. **Chairman's Letter to Shareholders**\n - **Introduction & Philosophy:** Warren Buffett discusses the purpose of the annual report, Berkshire Hathaway’s communication style, and his philosophy for transparency and candid discussion of both successes and failures.\n - **Discussion of Mistakes:** He talks openly about the mistakes made in capital allocation and personnel decisions, emphasizing the importance of admitting errors and acting promptly to correct them.\n - **Succession Comments:** Buffett references his eventual retirement, and that Greg Abel will succeed him as CEO and writer of these letters.\n - **Anecdotal Story:** The story of Pete Liegl, founder of Forest River (an RV manufacturer acquired by Berkshire), is told to illustrate management philosophy and business decision-making.\n\n#### 2. **2024 Business and Financial Performance**\n - **Key Results:** Summary of how Berkshire performed financially in 2024 vs. 2023, including operating earnings breakdown by business segments such as insurance, BNSF railroad, and energy.\n - **Insurance Business:** GEICO and the property-casualty insurance division had a standout year, with commentary on the industry and how Berkshire approaches insurance risk, pricing, and investment of insurance \"float.\"\n - **Investments:** Discussion on Berkshire’s strategy of owning both full businesses and partial stakes (marketable securities) in large companies (e.g., Apple, American Express, Coca-Cola), and its deployment of cash.\n - **Taxes:** Reference to Berkshire breaking records in corporate tax payments ($26.8 billion to the IRS in 2024).\n\n#### 3. **Long-term Philosophy & Capitalism Commentary**\n - **On Equities:** Buffett explains why Berkshire prioritizes ownership of businesses (equities) over cash or bonds, and why the company favors long-term investments.\n - **On Capitalism:** There’s a reflection on America’s growth, the role of capitalism, savings, and capital allocation in the nation’s success, and a nod to the importance of maintaining a stable currency.\n\n#### 4. **Japanese Investments**\n - **Update on Japanese Holdings:** Berkshire’s growing investments in five Japanese trading companies, and the positive view of their management and governance.\n\n#### 5. **Berkshire Hathaway Annual Meeting**\n - **Annual Gathering Info:** Details about the annual meeting in Omaha, including social events, book sales, and charitable initiatives related to the meeting.\n - **Personal Stories:** Personal anecdotes involving Buffett’s family, (including his sister Bertie), to add a human touch to the letter.\n\n#### 6. **Performance Tables**\n - **Berkshire vs S&P 500 (1965-2024):** Two detailed tables showing annual percentage change in Berkshire’s share price vs. total return for the S&P 500, as well as long-term compounded and overall gains.\n\n---\n\n### In Summary\n\nThis file is the **2024 Berkshire Hathaway annual letter to shareholders**, primarily written by Warren Buffett. It covers business performance, management philosophy, investment strategy, earnings and taxes, insurance operations, significant holdings, capital allocation, succession updates, and more. Tables show a remarkable outperformance of Berkshire Hathaway vs. the S&P 500 over nearly six decades – a central point of pride in the letter.\n\nIf you want specifics from any particular section, let me know!"
}
],
"role": "assistant"
}
],
"parallel_tool_calls": true,
"previous_response_id": null,
"prompt_cache_key": null,
"reasoning": {
"effort": null,
"summary": null
},
"safety_identifier": null,
"service_tier": "default",
"store": true,
"temperature": 1,
"text": {
"format": {
"type": "text"
}
},
"tool_choice": "auto",
"tools": [],
"top_p": 1,
"truncation": "disabled",
"usage": {
"input_tokens": 8438,
"input_tokens_details": {
"cached_tokens": 0
},
"output_tokens": 731,
"output_tokens_details": {
"reasoning_tokens": 0
},
"total_tokens": 9169
},
"user": null,
"metadata": {}
}
You can see that we have also processed the input file, and the result is similar to the above text.
¶ 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 official OpenAI's creation of Responses feature using the OpenAI Responses 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.