Information to OpenAI API Fashions and Easy methods to Use Them

OpenAI fashions have advanced drastically over the previous few years. The journey started with GPT-3.5 and has now reached GPT-5.1 and the newer o-series reasoning fashions. Whereas ChatGPT makes use of GPT-5.1 as its main mannequin, the API provides you entry to many extra choices which might be designed for various sorts of duties. Some fashions are optimized for velocity and price, others are constructed for deep reasoning, and a few focus on photos or audio.

On this article, I’ll stroll you thru all the foremost fashions accessible by way of the API. You’ll study what every mannequin is greatest suited to, which sort of undertaking it suits, and work with it utilizing easy code examples. The purpose is to offer you a transparent understanding of when to decide on a specific mannequin and use it successfully in an actual utility.

GPT-3.5 Turbo: The Bases of Fashionable AI 

The GPT-3.5 Turbo initiated the revolution of generative AI. The ChatGPT can even energy the unique and can be a steady and low-cost low-cost answer to easy duties. The mannequin is narrowed all the way down to obeying instructions and conducting a dialog. It has the power to answer questions, summarise textual content and write easy code. Newer fashions are smarter, however GPT-3.5 Turbo can nonetheless be utilized to excessive quantity duties the place price is the principle consideration.

Key Options:

• Velocity and Value: It is extremely quick and really low-cost. 
• Motion After Instruction: Additionally it is a dependable successor of easy prompts. 
• Context: It justifies the 4K token window (roughly 3,000 phrases). 

Palms-on Instance:

The next is a short Python script to make use of GPT-3.5 Turbo for textual content summarization. 

import openai
from google.colab import userdata 

# Set your API key 
consumer = openai.OpenAI(api_key=userdata.get('OPENAI_KEY')) 

messages = [ 
   {"role": "system", "content": "You are a helpful summarization assistant."}, 
   {"role": "user", "content": "Summarize this: OpenAI changed the tech world with GPT-3.5 in 2022."} 
] 

response = consumer.chat.completions.create( 
   mannequin="gpt-3.5-turbo", 
   messages=messages 
) 

print(response.decisions[0].message.content material)

Output:

GPT-4 Household: Multimodal Powerhouses 

The GPT-4 household was an infinite breakthrough. Such sequence are GPT-4, GPT-4 Turbo, and the very environment friendly GPT-4o. These fashions are multimodal, that’s that it is ready to comprehend each textual content and pictures. Their main energy lies in sophisticated pondering, authorized analysis, and inventive writing that’s refined. 

GPT-4o Options: 

• Multimodal Enter: It handles texts and pictures directly. 
• Velocity: GPT-4o (o is Omni) is twice as quick as GPT-4. 
• Value: It’s a lot cheaper than the standard GPT-4 mannequin. 

An openAI examine revealed that GPT-4 achieved a simulated bar take a look at within the prime 10 % of people to take the take a look at. This is a sign of its functionality to cope with refined logic. 

Palms-on Instance (Complicated Logic): 

GPT-4o has the potential of fixing a logic puzzle which includes reasoning. 

messages = [ 
   {"role": "user", "content": "I have 3 shirts. One is red, one blue, one green. " 
                               "The red is not next to the green. The blue is in the middle. " 
                               "What is the order?"} 
] 

response = consumer.chat.completions.create( 
   mannequin="gpt-4o", 
   messages=messages 
) 

print("Logic Resolution:", response.decisions[0].message.content material)

Output: 

The o-Collection: Fashions That Assume Earlier than They Converse 

Late 2024 and early 2025 OpenAI introduced the o-series (o1, o1-mini and o3-mini). These are “reasoning fashions.” They don’t reply instantly however take time to assume and devise a method in contrast to the traditional GPT fashions. This renders them math, science, and troublesome coding superior. 

o1 and o3-mini Highlights: 

• Chain of Thought: This mannequin checks its steps internally itself minimizing errors. 
• Coding Prowess: o3-mini is designed to be quick and correct in codes. 
• Effectivity: o3-mini is an extremely smart mannequin at a less expensive worth in comparison with the whole o1 mannequin. 

Palms-on Instance (Math Reasoning): 

Use o3-mini for a math drawback the place step-by-step verification is essential. 

# Utilizing the o3-mini reasoning mannequin 
response = consumer.chat.completions.create( 
   mannequin="o3-mini", 
   messages=[{"role": "user", "content": "Solve for x: 3x^2 - 12x + 9 = 0. Explain steps."}] 
) 

print("Reasoning Output:", response.decisions[0].message.content material)

Output: 

GPT-5 and GPT-5.1: The Subsequent Technology 

Each GPT-5 and its optimized model GPT-5.1, which was launched in mid-2025, mixed the tempo and logic. GPT-5 supplies built-in pondering, through which the mannequin itself determines when to assume and when to reply in a short while. The model, GPT-5.1, is refined to have superior enterprise controls and fewer hallucinations. 

What units them aside: 

• Adaptive Considering: It takes easy queries all the way down to easy routes and easy reasoning as much as arduous reasoning routs. 
• Enterprise Grade: GPT-5.1 has the choice of deep analysis with Professional options. 
• The GPT Picture 1: That is an inbuilt menu that substitutes DALL-E 3 to offer clean picture creation in chat. 

Palms-on Instance (Enterprise Technique): 

GPT-5.1 is superb on the prime stage technique which includes basic information and structured pondering. 

# Instance utilizing GPT-5.1 for strategic planning 
response = consumer.chat.completions.create( 
   mannequin="gpt-5.1", 
   messages=[{"role": "user", "content": "Draft a go-to-market strategy for a new AI coffee machine."}] 
) 

print("Technique Draft:", response.decisions[0].message.content material)

Output: 

DALL-E 3 and GPT Picture: Visible Creativity 

Within the case of visible information, OpenAI supplies DALL-E 3 and the newer GPT Picture fashions. These purposes will remodel textual prompts into lovely in-depth photos. Working with DALL-E 3 will allow you to attract photos, logos, and schemes by simply describing them. 

Learn extra: Picture era utilizing GPT Picture API

Key Capabilities:

• Instant Motion: It strictly observes elaborate directions. 
• Integration: It’s built-in into ChatGPT and the API. 

Palms-on Instance (Picture Technology): 

This script generates a picture URL based mostly in your textual content immediate. 

image_response = consumer.photos.generate( 
   mannequin="dall-e-3", 
   immediate="A futuristic metropolis with flying automobiles in a cyberpunk model", 
   n=1, 
   dimension="1024x1024" 
) 

print("Picture URL:", image_response.information[0].url)

Output: 

Whisper: Speech-to-Textual content Mastery 

Whisper The speech recognition system is the state-of-the-art supplied by OpenAI. It has the power to transcribe audio of dozens of languages putting them into English. It’s proof against background noise and accents. The next snippet of Whisper API tutorial is a sign of how easy it’s to make use of. 

Palms-on Instance (Transcription): 

Be sure to are in a listing with an audio file (named as speech.mp3). 

audio_file = open("speech.mp3", "rb") 

transcript = consumer.audio.transcriptions.create( 
   mannequin="whisper-1", 
   file=audio_file 
) 

print("Transcription:", transcript.textual content)

Output: 

Embeddings and Moderation: The Utility Instruments 

OpenAI has utility fashions that are vital to the builders. 

1. Embeddings (text-embedding-3-small/massive): These are used to encode textual content as numbers (vectors). This lets you create search engines like google and yahoo which may decipher which means versus key phrases. 
2. Moderation: It is a free API that verifies textual content content material of hate speech, violence, or self-harm to make sure apps are safe. 

Palms-on Instance (Semantic Search):

This discovers the very fact that there’s a similarity between a question and a product. 

# Get embeddings 

resp = consumer.embeddings.create(
   enter=["smartphone", "banana"], 
   mannequin="text-embedding-3-small" 
) 

# In an actual app, you examine these vectors to search out the most effective match 
print("Vector created with dimension:", len(resp.information[0].embedding))

Output: 

Nice-Tuning: Customizing Your AI 

Nice-tuning permits coaching of a mannequin utilizing its personal information. GPT-4o-mini or GPT-3.5 could be refined to select up a specific tone, format or business jargon. That is mighty in case of enterprise purposes, which require not more than basic response. 

The way it works: 

1. Put together a JSON file with coaching examples. 
2. Add the file to OpenAI. 
3. Begin a fine-tuning job. 
4. Use your new customized mannequin ID within the API. 

Conclusion 

The OpenAI mannequin panorama provides a instrument for almost each digital activity. From the velocity of GPT-3.5 Turbo to the reasoning energy of o3-mini and GPT-5.1, builders have huge choices. You may construct voice purposes with Whisper, create visible belongings with DALL-E 3, or analyze information with the newest reasoning fashions. 

The boundaries to entry stay low. You merely want an API key and an idea. We encourage you to check the scripts supplied on this information. Experiment with the completely different fashions to know their strengths. Discover the appropriate stability of price, velocity, and intelligence to your particular wants. The know-how exists to energy your subsequent utility. It’s now as much as you to use it. 

Often Requested Questions

Harsh Mishra is an AI/ML Engineer who spends extra time speaking to Giant Language Fashions than precise people. Captivated with GenAI, NLP, and making machines smarter (in order that they don’t change him simply but). When not optimizing fashions, he’s most likely optimizing his espresso consumption. 🚀☕