Information to OpenAI API Fashions and Learn how to Use Them

OpenAI fashions have developed 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 major mannequin, the API offers you entry to many extra choices which are designed for various sorts of duties. Some fashions are optimized for velocity and price, others are constructed for deep reasoning, and a few concentrate on photographs or audio.

On this article, I’ll stroll you thru all the main fashions accessible by way of the API. You’ll be taught what every mannequin is greatest fitted to, which kind of undertaking it suits, and the best way to work with it utilizing easy code examples. The goal is to provide you a transparent understanding of when to decide on a selected mannequin and the best way to use it successfully in an actual utility.

GPT-3.5 Turbo: The Bases of Trendy AI 

The GPT-3.5 Turbo initiated the revolution of generative AI. The ChatGPT can even energy the unique and can also be a secure 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 flexibility to reply to 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 value is the primary consideration.

Key Options:

• Pace and Price: It is extremely quick and really low cost. 
• Motion After Instruction: It’s also 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.selections[0].message.content material)

Output:

GPT-4 Household: Multimodal Powerhouses 

The GPT-4 household was an unlimited breakthrough. Such collection 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 power lies in sophisticated pondering, authorized analysis, and artistic writing that’s delicate. 

GPT-4o Options: 

• Multimodal Enter: It handles texts and pictures without delay. 
• Pace: GPT-4o (o is Omni) is twice as quick as GPT-4. 
• Value: It’s a lot inexpensive than the standard GPT-4 mannequin. 

An openAI research revealed that GPT-4 achieved a simulated bar take a look at within the prime 10 p.c of people to take the take a look at. This is a sign of its functionality to take care of subtle logic. 

Palms-on Instance (Complicated Logic): 

GPT-4o has the aptitude of fixing a logic puzzle which entails 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 Answer:", response.selections[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 suppose and devise a method not like the traditional GPT fashions. This renders them math, science, and tough 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 value in comparison with the whole o1 mannequin. 

Palms-on Instance (Math Reasoning): 

Use o3-mini for a math downside 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.selections[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 suppose 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 Pondering: 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 supply clean picture creation in chat. 

Palms-on Instance (Enterprise Technique): 

GPT-5.1 is superb on the prime degree technique which entails common data 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.selections[0].message.content material)

Output: 

DALL-E 3 and GPT Picture: Visible Creativity 

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

Learn extra: Picture era utilizing GPT Picture API

Key Capabilities:

• Fast 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 primarily based in your textual content immediate. 

image_response = consumer.photographs.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.knowledge[0].url)

Output: 

Whisper: Speech-to-Textual content Mastery 

Whisper The speech recognition system is the state-of-the-art offered by OpenAI. It has the flexibility to transcribe audio of dozens of languages placing 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 you 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/giant): These are used to encode textual content as numbers (vectors). This lets you create search engines like google and yahoo which might decipher that means versus key phrases. 
2. Moderation: This can be 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 actual 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 very best match 
print("Vector created with dimension:", len(resp.knowledge[0].embedding))

Output: 

Superb-Tuning: Customizing Your AI 

Superb-tuning permits coaching of a mannequin utilizing its personal knowledge. GPT-4o-mini or GPT-3.5 might be refined to select up a selected tone, format or business jargon. That is mighty in case of enterprise purposes, which require not more than common 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 affords a instrument for practically 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’ll be able to construct voice purposes with Whisper, create visible belongings with DALL-E 3, or analyze knowledge with the newest reasoning fashions. 

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

Ceaselessly 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 exchange him simply but). When not optimizing fashions, he’s in all probability optimizing his espresso consumption. 🚀☕