OpenAI's omni flagship for fast, low-latency text and image workloads that need strong general intelligence without reasoning overhead.
Output Speed
Intelligence Index
Context Window
Input price
Output price
Performance
Where GPT-4o Earns Its Place: Speed and Responsiveness
Benchmarks
How GPT-4o Scores on Independent Intelligence and Coding Tests
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-4o",
"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: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"gpt-4o","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 '{
"stream": false,
"tool_choice": "auto",
"logprobs": false,
"model": "gpt-4o",
"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 vs GPT-4.1: What Actually Changes for Developers?
GPT-4o and GPT-4.1 are both OpenAI non-reasoning models suited to general text and vision work, so teams frequently choose between them for the same production slots. The key differences are concrete: GPT-4.1 offers a far larger context window (over 1 million tokens), a higher 32,768-token output ceiling, a fresher June 2024 knowledge cutoff, and independently reported gains in coding and instruction following. GPT-4o remains a mature, fast, well-understood default. The practical decision is whether your workload needs GPT-4.1's larger context and stronger coding, or whether GPT-4o's proven speed and general balance already suffice.
Choose GPT-4o when you want a stable, fast generalist for chat, vision, and high-throughput text where a 128K context is sufficient and low latency matters. Choose GPT-4.1 when you process very long documents or codebases, need output longer than 16,384 tokens, want a more recent knowledge cutoff, or depend on stronger coding and instruction-following accuracy. For frontier reasoning or complex math, neither is ideal—reach for OpenAI's o-series reasoning models instead.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Real-time chat assistants
GPT-4o's low time to first token (near 0.76s on OpenAI's API in independent tests) and roughly 117 tokens per second output make streamed conversations feel immediate. Because it skips hidden reasoning tokens, response latency stays predictable under load. This suits customer-facing assistants and interactive copilots where perceived speed drives satisfaction more than deep reasoning. Where answers require complex multi-step logic, route those specific turns to a reasoning model.
Vision and document understanding
GPT-4o accepts image input alongside text, enabling visual question answering, document reading, screenshot interpretation, and image-based classification in a single request. Combined with Structured Outputs, it can return parsed fields directly for downstream automation. This fits document-intake pipelines and multimodal support tools. Note that GPT-4o outputs only text—it does not generate images—so pair it with a generation model if visual output is required.
High-throughput text generation
For summarization, rewriting, tagging, and bulk extraction at volume, GPT-4o's speed and mature tooling make it a dependable workhorse. Its output pace keeps batch pipelines moving, and Structured Outputs enforce machine-readable results for parsing and validation. It occupies a balanced cost tier suitable for cost-sensitive, high-volume jobs. For tasks requiring outputs beyond 16,384 tokens per call, plan continuation logic or select a higher-output model.
Tool-using automation
GPT-4o supports function calling with tools and tool_choice, reliably emitting JSON that matches your function signatures. This enables it to orchestrate lookups, bookings, and API-triggered workflows within an assistant. Paired with its low latency, it works well for lightweight agentic flows where each step is simple. For agents that require heavy planning or long chains of reasoning, a reasoning model will make more robust decisions.