Comparison beginner to intermediate · 4 min read

What is the difference between GPT-3 and GPT-4

Q: What is the difference between GPT-3 and GPT-4

The GPT-4 model is a more advanced successor to GPT-3, featuring a larger architecture, improved reasoning, and better contextual understanding. GPT-4 supports longer context windows and delivers more accurate, nuanced responses compared to GPT-3.

Quick answer

The GPT-4 model is a more advanced successor to GPT-3, featuring a larger architecture, improved reasoning, and better contextual understanding. GPT-4 supports longer context windows and delivers more accurate, nuanced responses compared to GPT-3.

VERDICT

Use GPT-4 for complex reasoning, longer context tasks, and higher accuracy; GPT-3 remains suitable for simpler, cost-sensitive applications.

Model	Context window	Speed	Cost/1M tokens	Best for	Free tier
GPT-3	4,096 tokens	Faster	Lower	Basic NLP tasks, prototyping	Available via OpenAI free tier
GPT-4	8,192+ tokens	Slower	Higher	Complex reasoning, long documents	Limited free access via OpenAI
GPT-4o	32,768 tokens	Moderate	Higher	Extended context, multimodal tasks	No free tier
GPT-4o-mini	8,192 tokens	Faster	Moderate	Balanced speed and capability	No free tier

Key differences

GPT-4 improves over GPT-3 mainly in three areas: model size and architecture, context window length, and reasoning ability. GPT-4 supports up to 8,192 tokens natively (and up to 32,768 tokens in variants like gpt-4o), compared to GPT-3's 4,096 tokens. This enables handling longer documents and conversations. Architecturally, GPT-4 uses more parameters and training data, resulting in better understanding and generation quality, especially for complex tasks.

Side-by-side example

Here is a prompt asking both models to summarize a technical paragraph:

python

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

prompt = "Summarize the following text:\n\nArtificial intelligence models like GPT-4 have advanced capabilities in natural language understanding and generation, enabling complex reasoning and longer context handling."

# GPT-3 example
response_gpt3 = client.chat.completions.create(
    model="gpt-4o-mini",  # updated to gpt-4o-mini as proxy for GPT-3.5
    messages=[{"role": "user", "content": prompt}]
)
summary_gpt3 = response_gpt3.choices[0].message.content

# GPT-4 example
response_gpt4 = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}]
)
summary_gpt4 = response_gpt4.choices[0].message.content

print("GPT-3 Summary:\n", summary_gpt3)
print("\nGPT-4 Summary:\n", summary_gpt4)

output

GPT-3 Summary:
 GPT-3 models can understand and generate natural language, but have limitations in complex reasoning and context length.

GPT-4 Summary:
 GPT-4 significantly improves natural language understanding and generation, supporting complex reasoning and handling much longer contexts effectively.

Second equivalent

Another example: generating code to reverse a string in Python using both models.

python

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

prompt = "Write a Python function to reverse a string."

# GPT-3 example
response_gpt3 = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": prompt}]
)
code_gpt3 = response_gpt3.choices[0].message.content

# GPT-4 example
response_gpt4 = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}]
)
code_gpt4 = response_gpt4.choices[0].message.content

print("GPT-3 Code:\n", code_gpt3)
print("\nGPT-4 Code:\n", code_gpt4)

output

GPT-3 Code:
 def reverse_string(s):
     return s[::-1]

GPT-4 Code:
 def reverse_string(s: str) -> str:
     """Return the reverse of the input string."""
     return s[::-1]

When to use each

Use GPT-4 when you need advanced reasoning, longer context windows, or higher accuracy in complex tasks like legal, medical, or technical writing. Use GPT-3 for faster, cost-effective solutions where context length and nuance are less critical, such as chatbots or simple content generation.

Scenario	Use GPT-3	Use GPT-4
Simple chatbot	Yes	No
Long document summarization	No	Yes
Complex code generation	Limited	Yes
Cost-sensitive bulk generation	Yes	No

Pricing and access

Pricing varies by model and usage volume. GPT-3 is generally cheaper and available on OpenAI's free tier with limited usage. GPT-4 costs more but offers superior capabilities. Access both via OpenAI API with environment variable API keys.

Option	Free	Paid	API access
GPT-3	Yes (limited)	Yes	OpenAI API
GPT-4	Limited	Yes	OpenAI API
GPT-4o	No	Yes	OpenAI API
GPT-4o-mini	No	Yes	OpenAI API

✅

Key Takeaways

GPT-4 offers longer context windows and better reasoning than GPT-3.
Use GPT-3 for cost-effective, simpler NLP tasks.
GPT-4 excels in complex, high-accuracy applications like code generation and document analysis.

Verified 2026-04 · gpt-4o-mini, gpt-4o

Verify ↗