Information to OpenAI API Fashions and The way 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 offers you entry to many extra choices which might be designed for various sorts of duties. Some fashions are optimized for pace and value, others are constructed for deep reasoning, and a few specialise in photos or audio.

On this article, I’ll stroll you thru all the key fashions out there by the API. You’ll study what every mannequin is greatest fitted to, which kind of challenge it suits, and tips on how to work with it utilizing easy code examples. The goal is to present you a transparent understanding of when to decide on a selected mannequin and tips on how to 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 may also energy the unique and can also be a secure and low cost low-cost resolution to easy duties. The mannequin is narrowed right 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 value is the primary consideration.

Key Options:

• Velocity and Price: 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 
shopper = 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 = shopper.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 unlimited 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 power 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 inexpensive than the standard GPT-4 mannequin. 

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

Palms-on Instance (Advanced Logic): 

GPT-4o has the aptitude 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 = shopper.chat.completions.create( 
   mannequin="gpt-4o", 
   messages=messages 
) 

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

Output: 

The o-Sequence: Fashions That Suppose Earlier than They Communicate 

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 technique not like the conventional 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 entire 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 = shopper.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 Era 

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, during 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 Pondering: It takes easy queries right down to easy routes and easy reasoning as much as laborious 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 easy picture creation in chat. 

Palms-on Instance (Enterprise Technique): 

GPT-5.1 is excellent on the high stage technique which includes common information and structured pondering. 

# Instance utilizing GPT-5.1 for strategic planning 
response = shopper.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 more moderen 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:

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

Palms-on Instance (Picture Era): 

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

image_response = shopper.photos.generate( 
   mannequin="dall-e-3", 
   immediate="A futuristic metropolis with flying automobiles in a cyberpunk fashion", 
   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 offered by OpenAI. It has the power to transcribe audio of dozens of languages placing them into English. It’s immune to 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): 

Ensure you are in a listing with an audio file (named as speech.mp3). 

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

transcript = shopper.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 crucial to the builders. 

1. Embeddings (text-embedding-3-small/massive): These are used to encode textual content as numbers (vectors). This allows you to create search engines like google and yahoo which may 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 very fact that there’s a similarity between a question and a product. 

# Get embeddings 

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

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

Output: 

Positive-Tuning: Customizing Your AI 

Positive-tuning permits coaching of a mannequin utilizing its personal information. GPT-4o-mini or GPT-3.5 may 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 presents a instrument for practically each digital activity. From the pace of GPT-3.5 Turbo to the reasoning energy of o3-mini and GPT-5.1, builders have huge choices. You possibly can construct voice purposes with Whisper, create visible belongings with DALL-E 3, or analyze information with the most recent reasoning fashions. 

The boundaries 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 know their strengths. Discover the proper stability of value, pace, 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. Obsessed with GenAI, NLP, and making machines smarter (in order that they don’t exchange him simply but). When not optimizing fashions, he’s most likely optimizing his espresso consumption. 🚀☕