OpenAI's fast, vision-capable GPT-4o snapshot tuned for stronger creative writing, file handling, and low-latency multimodal chat.
Output Speed
Intelligence Index
Context Window
Input price
Output price
Performance
Where GPT-4o (2024-11-20) Is Fast and Where It Isn't
Benchmarks
GPT-4o (2024-11-20) Benchmarks: Speed Leads, Reasoning Lags
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 = {
"model": "gpt-4o-2024-11-20",
"messages": [
{
"content": [
{
"type": "text",
"text": "Hello"
},
{
"image_url": {
"detail": "auto",
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
},
"type": "image_url"
}
],
"role": "user"
}
]
}
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: '{"model":"gpt-4o-2024-11-20","messages":[{"content":[{"type":"text","text":"Hello"},{"image_url":{"detail":"auto","url":"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"},"type":"image_url"}],"role":"user"}]}'
};
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-4o-2024-11-20",
"messages": [
{
"content": [
{
"type": "text",
"text": "Hello"
},
{
"image_url": {
"detail": "auto",
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
},
"type": "image_url"
}
],
"role": "user"
}
]
}'
Comparison
GPT-4o (2024-11-20) vs GPT-4.1: What Changes for Developers
Both are OpenAI non-reasoning models aimed at general text and vision work, and both are natural defaults for chat, extraction, and multimodal tasks. The realistic decision is whether you need GPT-4o (2024-11-20)'s established, low-latency behavior or GPT-4.1's stronger coding and instruction following. Under OpenAI's own setup, GPT-4.1 completes 54.6% of SWE-bench Verified tasks versus 33.2% for GPT-4o (2024-11-20), and GPT-4.1 adds a much larger context window. GPT-4o (2024-11-20) remains competitive where raw responsiveness and predictable, well-tuned writing matter most.
Choose GPT-4o (2024-11-20) when you want a fast, stable snapshot for interactive chat, creative writing, and image analysis, and when its 128K context is sufficient. Choose GPT-4.1 when coding accuracy, stricter instruction following, or a longer context window drive the workload, or when you are building agentic pipelines that stress code editing and repository navigation. For genuinely hard multi-step reasoning, prefer a dedicated reasoning model over either.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Low-latency conversational assistants
High output speed (~155.7 tokens/sec) and low time to first token (~0.82s) make streamed replies feel immediate, which suits customer support bots, in-app assistants, and other interactive chat where perceived responsiveness matters. The October 2023 cutoff means production deployments should pair it with retrieval so answers stay current.
Image understanding and analysis
The snapshot accepts image input alongside text, enabling document interpretation, chart and diagram Q&A, screenshot analysis, and visual data extraction in a single model. Combined with structured outputs, you can return schema-valid JSON from an image, making it useful for vision pipelines that feed downstream systems. Output is text only.
High-volume text generation and rewriting
This snapshot was tuned for more natural, tailored creative writing and better file handling, and its throughput supports large-scale generation, summarization, and rewriting jobs. Automatic prompt caching lowers cost and latency when a large shared system prompt or reference block repeats across requests, which fits batch content pipelines well.
Lightweight tool-using workflows
Function calling and structured outputs let the model trigger external actions and return reliable JSON, so it works for straightforward tool-augmented tasks such as data lookups, form filling, and API orchestration. Because it is not a reasoning model, keep the decision logic shallow; route complex multi-step agentic coding to GPT-4.1 or a reasoning model.