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 major mannequin, the API offers you entry to many extra choices which can 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 pictures or audio.

On this article, I’ll stroll you thru all the key fashions obtainable by means of the API. You’ll study what every mannequin is finest suited to, which sort of venture it matches, and the way to work with it utilizing easy code examples. The goal is to provide you a transparent understanding of when to decide on a specific mannequin and the way 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 energy the unique and can also 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 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 price is the principle consideration.

Key Options:

• Velocity and Price: It is rather quick and really low-cost. 
• Motion After Instruction: It is usually a dependable successor of straightforward 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 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 considering, authorized analysis, and inventive writing that’s delicate. 

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 inexpensive than the normal GPT-4 mannequin. 

An openAI research revealed that GPT-4 achieved a simulated bar take a look at within the high 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 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 Answer:", response.selections[0].message.content material)

Output: 

The o-Sequence: Fashions That Assume 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 suppose and devise a method 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 very smart mannequin at a less expensive value 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 = 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 Era 

Each GPT-5 and its optimized model GPT-5.1, which was launched in mid-2025, mixed the tempo and logic. GPT-5 gives built-in considering, by 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 Considering: It takes easy queries all the way down to easy routes and easy reasoning as much as exhausting 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 high degree technique which includes common data and structured considering. 

# 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 information, OpenAI gives DALL-E 3 and the newer GPT Picture fashions. These functions will rework textual prompts into lovely in-depth pictures. Working with DALL-E 3 will allow you to attract pictures, logos, and schemes by simply describing them. 

Learn extra: Picture era utilizing GPT Picture API

Key Capabilities:

• Speedy 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 = consumer.pictures.generate( 
   mannequin="dall-e-3", 
   immediate="A futuristic metropolis with flying automobiles in a cyberpunk type", 
   n=1, 
   measurement="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 flexibility 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 essential to the builders. 

1. Embeddings (text-embedding-3-small/giant): These are used to encode textual content as numbers (vectors). This allows you to create serps 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 evaluate these vectors to seek out one of the best match 
print("Vector created with dimension:", len(resp.information[0].embedding))

Output: 

High-quality-Tuning: Customizing Your AI 

High-quality-tuning permits coaching of a mannequin utilizing its personal information. GPT-4o-mini or GPT-3.5 might be refined to choose up a specific tone, format or trade jargon. That is mighty in case of enterprise functions, 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 provides a device for almost each digital job. 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 functions with Whisper, create visible belongings with DALL-E 3, or analyze information with the most recent 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 know their strengths. Discover the fitting steadiness of price, velocity, and intelligence on your particular wants. The expertise exists to energy your subsequent utility. It’s now as much as you to use it. 

Continuously 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 (so that they don’t substitute him simply but). When not optimizing fashions, he’s in all probability optimizing his espresso consumption. 🚀☕