gpt-5.1
Model Overview
A flagship model for coding and agentic workflows, offering configurable reasoning and non-reasoning modes.
How to Make a Call
API Schema
An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API.
If set to True, the model response data will be streamed to the client as it is generated using server-sent events.
falseControls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. none is the default when no tools are present. auto is the default if tools are present.
none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools.
Whether to enable parallel function calling during tool use.
What sampling temperature to use. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
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.
An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to True if this parameter is used.
This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
Constrains effort on reasoning for reasoning models. Currently supported values are low, medium, and high. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.
An object specifying the format that the model must output.
async function main() {
const response = await fetch('https://api.aimlapi.com/v1/chat/completions', {
method: 'POST',
headers: {
'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'openai/gpt-5-1',
messages:[
{
role:'user',
content: 'Hello'
}
],
}),
});
const data = await response.json();
console.log(JSON.stringify(data, null, 2));
}
main();{
"id": "text",
"object": "text",
"created": 1,
"choices": [
{
"index": 1,
"message": {
"role": "text",
"content": "text",
"refusal": null,
"annotations": [
{
"type": "text",
"url_citation": {
"end_index": 1,
"start_index": 1,
"title": "text",
"url": "text"
}
}
],
"audio": {
"id": "text",
"data": "text",
"transcript": "text",
"expires_at": 1
},
"tool_calls": [
{
"id": "text",
"type": "text",
"function": {
"arguments": "text",
"name": "text"
}
}
]
},
"finish_reason": "stop",
"logprobs": {
"content": [
{
"bytes": [
1
],
"logprob": 1,
"token": "text",
"top_logprobs": [
{
"bytes": [
1
],
"logprob": 1,
"token": "text"
}
]
}
],
"refusal": []
}
}
],
"model": "text",
"usage": {
"prompt_tokens": 1,
"completion_tokens": 1,
"total_tokens": 1,
"completion_tokens_details": {
"accepted_prediction_tokens": 1,
"audio_tokens": 1,
"reasoning_tokens": 1,
"rejected_prediction_tokens": 1
},
"prompt_tokens_details": {
"audio_tokens": 1,
"cached_tokens": 1
}
}
}Responses Endpoint
This endpoint is currently used only with OpenAI models. Some models support both the /chat/completions and /responses endpoints, while others support only one of them. OpenAI has announced plans to expand the capabilities of the /responses endpoint in the future.
Text, image, or file inputs to the model, used to generate a response.
A text input to the model, equivalent to a text input with the user role.
An upper bound for the number of tokens that can be generated for a response, including visible output tokens and reasoning tokens.
The unique ID of the previous response to the model. Use this to create multi-turn conversations.
Whether to store the generated model response for later retrieval via API.
falseIf set to true, the model response data will be streamed to the client as it is generated using server-sent events.
falseThe truncation strategy to use for the model response.
- auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.
- disabled (default): If a model response will exceed the context window size for a model, the request will fail with a 400 error.
disabledPossible values: How the model should select which tool (or tools) to use when generating a response.
Controls which (if any) tool is called by the model.
none means the model will not call any tool and instead generates a message.
auto means the model can pick between generating a message or calling one or more tools.
required means the model must call one or more tools.
async function main() {
const response = await fetch('https://api.aimlapi.com/v1/responses', {
method: 'POST',
headers: {
'Authorization': 'Bearer <YOUR_API_KEY>',
'Content-Type': 'application/json',
},
body: JSON.stringify({
"model": "openai/gpt-5-1",
"input": "Hello"
}),
});
const data = await response.json();
console.log(JSON.stringify(data, null, 2));
}
main();{
"background": null,
"created_at": 1,
"error": {
"code": "text",
"message": "text"
},
"id": "text",
"incomplete_details": {
"reason": "text"
},
"instructions": "text",
"max_output_tokens": 1,
"metadata": {
"ANY_ADDITIONAL_PROPERTY": "anything"
},
"model": "text",
"object": "text",
"output": [
{
"role": null,
"type": null,
"content": null
}
],
"output_text": null,
"parallel_tool_calls": true,
"previous_response_id": null,
"prompt": {
"id": "text",
"variables": {
"ANY_ADDITIONAL_PROPERTY": "anything"
},
"version": null
},
"reasoning": {
"effort": "low",
"summary": "auto"
},
"service_tier": null,
"status": "completed",
"temperature": 1,
"text": {
"format": {
"type": "text"
}
},
"tool_choice": "none",
"tools": [
{
"type": "web_search_preview",
"search_context_size": "low",
"user_location": {
"type": "text",
"city": null,
"country": null,
"region": null,
"timezone": null
}
}
],
"top_p": null,
"truncation": "auto",
"usage": {
"input_tokens": 1,
"input_tokens_details": {
"cached_tokens": 1
},
"output_tokens": 1,
"output_tokens_details": {
"reasoning_tokens": 1
},
"total_tokens": 1
}
}Code Example
import requests
import json # for getting a structured output with indentation
response = requests.post(
"https://api.aimlapi.com/v1/chat/completions",
headers={
# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
"Authorization":"Bearer <YOUR_AIMLAPI_KEY>",
"Content-Type":"application/json"
},
json={
"model":"openai/gpt-5-1",
"messages":[
{
"role":"user",
"content":"Hello" # insert your prompt here, instead of Hello
}
]
}
)
data = response.json()
print(json.dumps(data, indent=2, ensure_ascii=False))async function main() {
const response = await fetch('https://api.aimlapi.com/v1/chat/completions', {
method: 'POST',
headers: {
// insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'openai/gpt-5-1',
messages:[
{
role:'user',
content: 'Hello' // insert your prompt here, instead of Hello
}
],
}),
});
const data = await response.json();
console.log(JSON.stringify(data, null, 2));
}
main();Code Example #2: Using /responses Endpoint
import requests
import json # for getting a structured output with indentation
response = requests.post(
"https://api.aimlapi.com/v1/responses",
headers={
"Content-Type":"application/json",
# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
"Authorization":"Bearer <YOUR_AIMLAPI_KEY>",
"Content-Type":"application/json"
},
json={
"model":"openai/gpt-5-1",
"input":"Hello" # Insert your question for the model here, instead of Hello
}
)
data = response.json()
print(json.dumps(data, indent=2, ensure_ascii=False))async function main() {
try {
const response = await fetch('https://api.aimlapi.com/v1/responses', {
method: 'POST',
headers: {
// Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'openai/gpt-5-1',
input: 'Hello', // Insert your question here, instead of Hello
}),
});
if (!response.ok) {
throw new Error(`HTTP error! Status ${response.status}`);
}
const data = await response.json();
console.log(JSON.stringify(data, null, 2));
} catch (error) {
console.error('Error', error);
}
}
main();Last updated
Was this helpful?