OpenAI's fastest, cheapest GPT-4.1 model for high-volume classification, extraction and autocomplete with a 1M-token context window.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Why GPT-4.1 Nano Wins on Speed and Cost, Not Reasoning
Benchmarks
GPT-4.1 Nano Benchmarks: Fast and Cheap, Below Average on Intelligence
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"stream": False,
"tool_choice": "auto",
"logprobs": False,
"model": "gpt-4.1-nano",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
headers = {
"Authorization": "Bearer AnyAPI_API_KEY",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
method: 'POST',
headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"gpt-4.1-nano","messages":[{"role":"user","content":"Hello"}]}'
};
try {
const response = await fetch(url, options);
const data = await response.json();
console.log(data);
} catch (error) {
console.error(error);
}curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-4.1-nano",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'
Comparison
GPT-4.1 Nano vs GPT-4.1 Mini: Which Small OpenAI Model?
GPT-4.1 Nano and GPT-4.1 mini are the two smaller members of the GPT-4.1 family. Both share the one-million-token context window, text and image input, tool calling and structured outputs, and both target cost-sensitive production use. The practical decision is how much capability you need per request. Nano is the smallest, fastest and cheapest, tuned for narrow, high-volume tasks. Mini is a step up in intelligence and instruction-following depth, positioned by OpenAI as a near-flagship option at lower latency and cost than full GPT-4.1, and it supports fine-tuning.
Choose GPT-4.1 Nano when throughput and cost per request dominate — classification, autocomplete, tagging and large-scale extraction where the task is well-defined and reasoning depth is not required. Choose GPT-4.1 mini when you need stronger instruction following and broader task competence, such as interactive assistants or workflows that occasionally require more nuanced responses, and can accept its higher per-token cost. Many teams route simple traffic to Nano and escalate harder requests to mini or full GPT-4.1.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
High-volume text classification
Nano is explicitly positioned by OpenAI for classification. Its low latency and low per-request cost make it ideal for tagging support tickets, moderating content, routing messages, or labeling large datasets at scale. Structured JSON output via schema keeps results parseable. Because these tasks are well-defined and require little reasoning, Nano's intelligence ceiling is rarely a constraint here.
Information extraction from large documents
The one-million-token context window lets Nano ingest long documents or datasets in a single request, and OpenAI markets it for extraction workloads. It suits pulling structured fields from contracts, invoices, logs or research papers without chunking. Keep tasks retrieval-oriented — locating and extracting facts rather than synthesizing conclusions — to stay within the model's strengths.
Autocomplete and inline suggestions
As the fastest model in the family, with a time to first token near 0.64s in independent testing, Nano fits latency-critical autocomplete, inline suggestions and typeahead. Its speed and low cost allow high request volumes without meaningful budget impact, which matters for features triggered on every keystroke or interaction.
Lightweight tool-calling flows
Nano supports function calling and instruction following, making it viable for simple, deterministic tool-invocation flows — parsing an intent and calling a well-defined function. For complex, multi-step agents that require planning or error recovery, escalate to a reasoning model; Nano is best where the tool-calling logic is shallow and predictable.