Guide to OpenAI API Models and 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 provides you entry to many extra choices which might be designed for various sorts of duties. Some fashions are optimized for velocity and value, others are constructed for deep reasoning, and a few concentrate on photos or audio.

On this article, I’ll stroll you thru all the key fashions accessible by way of the API. You’ll study what every mannequin is finest fitted to, which sort of mission it matches, and the best way to work with it utilizing easy code examples. The intention is to offer you a transparent understanding of when to decide on a specific mannequin and the best way to use it successfully in an actual software.

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 resolution to easy duties. The mannequin is narrowed right down to obeying instructions and conducting a dialog. It has the flexibility 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 rather 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 
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 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 power lies in sophisticated considering, authorized analysis, and artistic writing that’s refined. 

GPT-4o Options: 

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

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

Palms-on Instance (Complicated 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 Resolution:", response.decisions[0].message.content material)

Output: 

The o-Sequence: 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 in contrast to the conventional 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 very 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 = 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 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 considering, 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 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 supply easy picture creation in chat. 

Palms-on Instance (Enterprise Technique): 

GPT-5.1 is excellent on the high stage technique which includes basic data and structured considering. 

# 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 knowledge, OpenAI supplies DALL-E 3 and the more moderen GPT Picture fashions. These functions will rework 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:

• Quick 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 = shopper.photos.generate( 
   mannequin="dall-e-3", 
   immediate="A futuristic metropolis with flying vehicles 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 supplied by OpenAI. It has the flexibility to transcribe audio of dozens of languages putting 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): 

Be sure 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 vital 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 engines like google which might decipher that 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 = shopper.embeddings.create(
   enter=["smartphone", "banana"], 
   mannequin="text-embedding-3-small" 
) 

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

Output: 

High-quality-Tuning: Customizing Your AI 

High-quality-tuning allows coaching of a mannequin utilizing its personal knowledge. GPT-4o-mini or GPT-3.5 will be refined to select up a specific tone, format or trade jargon. That is mighty in case of enterprise functions, 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 gives a instrument for almost each digital process. From the velocity 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 functions with Whisper, create visible belongings with DALL-E 3, or analyze knowledge with the most recent reasoning fashions. 

The limitations 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 grasp their strengths. Discover the fitting steadiness of value, velocity, and intelligence on your particular wants. The expertise exists to energy your subsequent software. It’s now as much as you to use it. 

Continuously Requested Questions