Guide to OpenAI API Models and How to Use Them

OpenAI fashions have developed considerably over the previous few years. This journey began with GPT-3.5 and has now reached GPT-5.1 and the brand new o-series inference mannequin. ChatGPT makes use of GPT-5.1 as its main mannequin, however the API offers you entry to many extra choices designed for several types of duties. Some fashions are optimized for velocity and value, others are constructed for deep inference, and a few are specialised for photos and audio.

This text describes all the foremost fashions out there by the API. Study what every mannequin is finest for, what sorts of tasks it suits in, and methods to work with it utilizing easy code examples. The intention is to present you a transparent understanding of when to decide on a specific mannequin and methods to use it successfully in real-world functions.

GPT-3.5 Turbo: The inspiration of contemporary AI

GPT-3.5 Turbo sparked a revolution in generative AI. ChatGPT may improve the unique and can also be a steady and cheap low-cost answer for easy duties. The mannequin is narrowed right down to following directions and having conversations. It has the power to answer questions, summarize textual content, and write easy code. Though the brand new mannequin is smarter, GPT-3.5 Turbo can nonetheless be utilized to high-volume duties the place price is the primary consideration.

Principal options:

Velocity ​​and value: Very quick and really low cost. Motion after immediate: That is additionally a dependable successor to the straightforward immediate. Context: Regulate 4K token window (roughly 3,000 phrases).

Sensible instance:

Under is an easy Python script for utilizing GPT-3.5 Turbo for textual content summarization.

import openai from google.colab import userdata # Set 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.selections)[0].message.content material)

output:

GPT-4 Household: A Multimodal Powerhouse

The GPT-4 household was a serious advance. Such sequence embrace GPT-4, GPT-4 Turbo, and the extremely environment friendly GPT-4o. These fashions are multimodal. This implies it could possibly perceive each textual content and pictures. Their essential strengths lie in advanced considering, authorized analysis, and delicate inventive writing.

GPT-4o options:

Multimodal enter: Course of textual content and pictures concurrently. Velocity: GPT-4o (o is omni) is twice as quick as GPT-4. Worth: Less expensive than conventional GPT-4 fashions.

openAI’s analysis revealed that GPT-4 handed the highest 10% of mock bar examination takers. This can be a signal of your potential to deal with superior logic.

Sensible instance (advanced logic):

GPT-4o has the power to unravel logic puzzles that contain reasoning.

message = [ 
   {“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.selections[0].message.content material)

output:

o Sequence: Assume earlier than you communicate fashions

In late 2024 and early 2025, OpenAI introduced the o sequence (o1, o1-mini, and o3-mini). These are “inference fashions”. Not like the common GPT mannequin, we do not reply straight away, however we take time to assume and strategize. This may make you higher at math, science, and tough coding.

o1 and o3-mini highlights:

Chain of Thought: This mannequin internally checks steps and minimizes errors. Coding potential: o3-mini is designed to make your code quick and correct. Effectivity: The o3-mini is a extremely smart mannequin at a cheaper price in comparison with the complete o1 mannequin.

Sensible instance (mathematical reasoning):

Use o3-mini for math issues the place step-by-step verification is necessary.

# Use o3-mini inference mannequin response = shopper.chat.completions.create(mannequin=”o3-mini”,messages=[{“role”: “user”, “content”: “Solve for x: 3x^2 – 12x + 9 = 0. Explain steps.”}]
) print(“Inference 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, launched in mid-2025, mixed tempo and logic. GPT-5 offers built-in considering the place the mannequin itself decides within the quick time period when to assume and when to reply. This model of GPT-5.1 has been improved to have higher enterprise controls and fewer hallucinations.

What units them aside:

Adaptive considering: Take easy queries to easy routes, and take easy inferences to tough inference routes. Enterprise Grade: GPT-5.1 has the choice of deep exploration utilizing Professional options. GPT Picture 1: This can be a built-in menu that replaces DALL-E 3 to offer clean picture creation in chat.

Sensible instance (enterprise technique):

GPT-5.1 is superb at top-level methods that contain normal data and structured considering.

# Instance of 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(“Draft technique:”, response.selections[0].message.content material)

output:

DALL-E 3 and GPT Photos: Visible Creativity

For visible information, OpenAI presents DALL-E 3 and newer GPT Picture fashions. These functions remodel textual content prompts into stunning, detailed photos. With DALL-E 3, you may draw photos, logos, and schemes by merely writing them.

Learn extra: Picture technology utilizing GPT Picture API

Principal options:

Fast response: Strictly comply with directions. Integration: Built-in with ChatGPT and API.

Sensible instance (picture technology):

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

image_response = shopper.photos.generate( mannequin=”dall-e-3″, immediate=”Future metropolis with cyberpunk flying vehicles”, n=1, dimension=”1024×1024″ ) print(“Picture URL:”, image_response.information[0].url)

output:

Whisper: Grasp speech-to-text conversion

The Whisper speech recognition system is probably the most superior providing from OpenAI. It has the power to transcribe audio from dozens of languages ​​into English. Resistant to ambient noise and accents. The next snippet from the Whisper API tutorial reveals how straightforward it’s to make use of.

Sensible instance (transcription):

Be sure to are within the listing with the audio file (named speech.mp3).

audio_file = open(“speech.mp3”, “rb”) Transcript = shopper.audio.transcriptions.create( mannequin=”whisper-1″, file=audio_file ) print(“Transcript:”, transcription.textual content)

output:

Embedding and moderation: utility instruments

OpenAI has a working mannequin that’s necessary to builders.

Embedding (text-embedding-3-small/giant): These are used to encode textual content as numbers (vectors). This lets you create search engines like google that may decipher meanings quite than key phrases. Moderation: This can be a free API that helps guarantee the security of your app by validating textual content content material for hate speech, violence, and self-harm.

Sensible instance (semantic search):

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

# Get embeddings resp = shopper.embeddings.create( enter=[“smartphone”, “banana”]mannequin=”text-embedding-3-small” ) # In an actual app, we’d examine these vectors to seek out the most effective one. print(“Vector created with Dimensions:”, len(resp.information)[0].embedding))

output:

Tweak: AI customization

Superb-tuning lets you practice the mannequin utilizing your individual information. GPT-4o-mini or GPT-3.5 may be tuned to select up particular tones, codecs, or jargon. That is highly effective for enterprise functions that solely require a generic response.

construction:

Put together a JSON file containing coaching examples. Add the file to OpenAI. Begin fine-tuning. Use your new customized mannequin ID within the API.

conclusion

The OpenAI mannequin panorama offers instruments for nearly any digital activity. From the velocity of GPT-3.5 Turbo to the inference energy of o3-mini and GPT-5.1, builders have an enormous alternative. Construct voice functions with Whisper, create visible property with DALL-E 3, and analyze your information with fashionable inference fashions.

Obstacles to entry stay low. All you want is an API key and an idea. We encourage you to check the scripts supplied on this information. Strive totally different fashions to grasp the strengths of every. Discover the proper stability of price, velocity, and intelligence to fit your particular wants. This expertise exists to energy the next functions: It is as much as you to use it or not.

FAQ