OpenAI's original GPT-4 model for reproducible Chat Completions workloads where behavior must stay pinned and unchanged.
Output Speed
Intelligence Index
Context Window
Input price
Output price
Performance
Where GPT-4 Still Holds Up: Broad Reasoning and Multilingual Coverage
Benchmarks
GPT-4 Benchmarks: Original Technical Report Results in Context
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-4",
"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: '{"model":"gpt-4","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",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'
Comparison
GPT-4 vs GPT-4o: What Changes When You Migrate?
GPT-4 and GPT-4o are natural comparison points because GPT-4o is OpenAI's successor to the original GPT-4 line for general Chat Completions work. Both are high-intelligence text models reachable through the same API surface, and both support function calling. The practical decision is almost always about capability versus continuity: GPT-4o expands the context window to 128,000 tokens, adds image input and structured outputs, runs faster, and costs less, while GPT-4 offers a fixed, well-understood behavior profile that some regulated or long-lived deployments were built and validated against.
Choose GPT-4 when you need behavior pinned to the original model for reproducibility, auditability, or to avoid re-validating a system already certified against a specific snapshot, and your prompts fit within 8K tokens. Choose GPT-4o when you need a larger context window, image input, structured outputs, higher throughput, and lower cost, which covers virtually all new development. For most greenfield projects GPT-4o or GPT-4.1 is the better default; GPT-4 is a maintenance and continuity choice, especially given its deprecation trajectory.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Reproducible reasoning under audit
For regulated or long-lived systems validated against a specific model version, GPT-4's pinned snapshots (gpt-4-0613) provide stable, predictable behavior. This matters where outputs must be auditable and re-validation is costly. The constraint is the deprecation timeline: plan a migration path even when using GPT-4 for continuity, since the snapshot is scheduled for shutdown.
Multilingual knowledge tasks
GPT-4 surpassed the English-language state of the art on translated MMLU in 24 of 26 languages, making it a strong option for knowledge-intensive question answering across languages within a single model. It suits professional and academic Q&A where prompts fit inside the 8K window. For higher volume or larger context, GPT-4o offers comparable multilingual quality at lower cost.
Legacy tool-calling integrations
GPT-4 was the model that introduced reliable function calling (gpt-4-0613), so existing integrations that emit JSON tool-call arguments continue to work unchanged. This fits maintenance of established agent or plugin systems. New builds should prefer models with strict structured outputs, since GPT-4 scored under 40% on OpenAI's JSON-schema-following eval.
Multilingual assistants and Q&A
GPT-4's demonstrated strength across languages, surpassing prior state of the art on translated MMLU in 24 of 26 languages, makes it suitable for assistants and knowledge tools serving global audiences. Applications that require consistent reasoning quality across many languages within short-to-moderate prompts benefit here, provided the 8,192-token budget is respected.