Chapter 1: Welcome to the OpenAI Ecosystem
1.3 The Evolution of OpenAI’s Models
To effectively harness OpenAI's tools, it's essential to understand their historical development and evolution. These sophisticated AI models represent the culmination of extensive research, countless iterations, and significant technological breakthroughs. Each generation has built upon the successes and lessons learned from its predecessors, incorporating new capabilities and addressing previous limitations.
Understanding the evolution of GPT and other models is crucial because it:
- Helps you select the optimal model by understanding each version's specific strengths and capabilities
- Enables you to anticipate and work around known limitations that existed in earlier versions
- Allows you to future-proof your applications by understanding the trajectory of model development
- Provides insight into how different models handle various tasks and use cases
- Helps you make informed decisions about resource allocation and API usage
Let's explore the fascinating journey of OpenAI's major models, examining how each iteration has pushed the boundaries of artificial intelligence and opened new possibilities for developers and creators.
1.3.1 🧠 GPT-1 (2018): The Prototype
OpenAI's journey into large language models began with a groundbreaking experiment in 2018: GPT-1, a 117 million parameter language model. While modest by today's standards, this model could complete simple text prompts with surprising coherence. Though GPT-1 was never released as a public API, it proved a revolutionary concept in AI development: the effectiveness of pretraining a model on vast amounts of text data, followed by fine-tuning it for specific tasks. This two-step approach would become the foundation for all future GPT models.
Key Traits:
- Very basic understanding of language, capable of simple text completion and basic pattern recognition
- Primarily served as a research project to validate the pretraining and fine-tuning approach
- Had limited context understanding and often produced inconsistent outputs
- Demonstrated the potential of transformer-based architectures in language processing
- Served as proof-of-concept for what was to come in the field of natural language processing
While you won't directly use GPT-1 in any applications today, its success catalyzed the development of increasingly sophisticated language models and launched the entire field of large language models that we know today.
1.3.2 🧠 GPT-2 (2019): The First Leap Forward
GPT-2 marked a significant milestone as the first OpenAI model to generate widespread public interest and debate. With 1.5 billion parameters - a massive leap from GPT-1's 117 million - this model demonstrated unprecedented capabilities in natural language processing. It could generate remarkably coherent text, create detailed summaries of complex content, and even continue narrative stories with surprising consistency. The model's capabilities were so advanced that OpenAI made the unprecedented decision to initially withhold the full model release, citing concerns about potential misuse in generating deceptive content or automated disinformation campaigns.
Capabilities:
- Enhanced natural language understanding with significantly improved coherence and contextual awareness compared to its predecessor
- Advanced text generation abilities, including story continuation, article writing, and creative writing tasks
- Sophisticated summarization capabilities that could distill key points from longer texts
- Basic question-answering abilities, though with notable limitations
- Still struggled with logic, math, and long context
Why It Matters:
GPT-2 represented a pivotal moment in AI development, sparking crucial discussions about AI safety and ethical considerations in AI deployment. It introduced the concept of prompt-based interfaces, revolutionizing how humans interact with AI systems. This model's release strategy also established important precedents for responsible AI development, balancing technological advancement with societal impact. The debates it sparked continue to influence AI policy and development practices today.
1.3.3 🧠 GPT-3 (2020): The API Era Begins
GPT-3 marked a revolutionary transformation in the AI landscape. This wasn't just another iteration - it represented a fundamental shift in how AI could be accessed and utilized.
With an unprecedented 175 billion parameters, GPT-3 became the first large-scale language model available through a public API. This democratization of AI technology was groundbreaking - it meant that anyone, regardless of their resources or technical expertise, could integrate sophisticated AI capabilities into their products. From independent developers working on innovative startups to Fortune 500 companies developing enterprise solutions, GPT-3's API opened doors to AI implementation that were previously locked.
What GPT-3 Introduced:
- Natural conversation-style prompting that allowed for more intuitive interactions with AI, moving away from rigid command structures to more natural language interfaces
- Remarkable performance across a wide range of language tasks, including sophisticated summarization capabilities, contextually aware question-answering systems, and high-quality content generation for various purposes
- Introduction of text-davinci-003, a significant milestone as the first "tuned" model specifically optimized for following complex instructions with greater accuracy and reliability
Example: Using GPT-3 (text-davinci-003)
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create a chat completion
response = client.chat.completions.create(
model="gpt-3.5-turbo", # More cost-effective than davinci-003
messages=[
{
"role": "user",
"content": "Write a short poem about coffee and coding."
}
],
temperature=0.7,
max_tokens=100
)
# Print the generated text
print(response.choices[0].message.content)Here's a breakdown of what each part does:
- Import and Initialization: The code imports the OpenAI library and initializes the client object.
- Creating a Chat Completion: The code calls the chat.completions.create() method with several parameters:
- model: Uses "gpt-3.5-turbo", which is more cost-effective than davinci-003
- messages: A list containing the conversation history, with a single user message requesting a poem about coffee and coding
- temperature: Set to 0.7, which controls the randomness of the output
- max_tokens: Limits the response length to 100 tokens
- Output: Finally, it prints the generated response from the model using the first choice's message content.
GPT-3 helped launch thousands of startups. It was the model behind the first waves of AI writing tools, resume builders, and coding assistants.
1.3.4 🧠 GPT-3.5 (2022): From Text to Chat
GPT-3.5 represented a significant evolution in OpenAI's language models, introducing major improvements in two critical areas. First, its instruction following capabilities were substantially enhanced, allowing it to better understand and execute complex, multi-step tasks. Second, its conversational accuracy showed remarkable improvement, with more natural and contextually appropriate responses. The most revolutionary change was the introduction of Chat Completions - a fundamental shift from the traditional single-prompt system to a more sophisticated message-based format that uses specific role labels:
system: Sets the behavior and context for the AI
• user: Contains the human input/question
• assistant: Contains the AI's responses
This new architecture enabled more natural, flowing conversations and better context management across multiple exchanges.
Major Changes:
- Chat format support via
gpt-3.5-turbo- This new model became the standard for chat-based applications, offering a more efficient and cost-effective solution for conversational AI - Better contextual awareness - The model could now maintain conversation history and understand references to previous messages, making interactions feel more natural and coherent
- Faster and cheaper than GPT-3 - Despite its improvements, GPT-3.5 was optimized for better performance, processing requests more quickly while requiring fewer computational resources
- Used in early versions of ChatGPT - This model powered the initial release of ChatGPT, demonstrating its capabilities in real-world applications and helping establish ChatGPT as a breakthrough in conversational AI
Example: GPT-3.5 Chat Completion
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create a chat completion
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What's the difference between an array and a list in Python?"}
]
)
# Extract and print the response
try:
print(response.choices[0].message.content)
except Exception as e:
print(f"Error processing response: {e}")Let's break down this code example:
1. Setup and Initialization:
- Imports the OpenAI library and creates a client instance to interact with the API
2. Creating the Chat Completion:
- Uses the chat.completions.create() method with the following parameters:
- model: Specifies "gpt-3.5-turbo", which is more cost-effective than older models
- messages: A list containing two dictionaries:
- A system message defining the AI's role
- A user message asking about Python arrays vs lists
3. Error Handling:
- Implements a try-except block to gracefully handle any potential errors during response processing
- If successful, prints the AI's response
- If an error occurs, prints an error message with details
This shift laid the foundation for modern AI chatbots—apps that remember context, clarify intent, and simulate real conversations.
1.3.5 🧠 GPT-4 (2023): Multi-Modal Intelligence
GPT-4 represents a transformative leap in OpenAI's technology, introducing unprecedented capabilities across multiple domains. The model features enhanced reasoning abilities that allow it to process complex logic chains, expanded memory capacity for handling longer contexts, and groundbreaking multi-modal capabilities that enable it to process both text and images (though API image support remains limited to specific use cases).
GPT-4's expanded capabilities include:
- Advanced code generation and debugging, with significantly reduced error rates compared to previous models
- Sophisticated instruction following that captures subtle nuances and implied context
- Enhanced document analysis that can process and synthesize information from lengthy texts
- Improved conversation management with consistent context retention across extended dialogues
- Superior prompt handling capabilities, including nested instructions and multi-step reasoning tasks
Key Advantages:
- Substantially improved accuracy in technical domains, particularly in programming and mathematical computations
- Exceptional performance across various standardized assessments, demonstrating human-expert level understanding
- Enhanced reasoning capabilities that enable more sophisticated problem-solving and analysis
Available Versions and Deployment Options:
"gpt-4"– The foundation model offering maximum accuracy and capability, though with higher latency and cost"gpt-4-turbo"– A performance-optimized variant that balances capability with efficiency, making it ideal for production environments and high-volume applications
1.3.6 🧠 The Latest Versions of OpenAI's ChatGPT: GPT-4o and Beyond
GPT-4 Turbo offers more than cost savings—it brings significant enhancements:
- Larger context windows (up to 128k tokens in some environments)
- Faster generation speeds
- More efficient API usage at scale
OpenAI has positioned GPT-4 Turbo as the default choice for production apps, especially in tools like ChatGPT Pro and custom GPTs.
OpenAI's latest ChatGPT updates mark a pivotal moment in AI chatbot evolution. These changes include retiring GPT-4, introducing GPT-4o as the default model, and planning future versions like GPT-4.1 and GPT-5. Here's what you need to know.
Retirement of GPT-4 and Introduction of GPT-4o
- GPT-4 Retirement: After April 30, 2025, GPT-4 will be removed from the ChatGPT interface but will remain available through OpenAI's API for developers and enterprise users.
- GPT-4o Overview: Launched in May 2024, GPT-4o serves as ChatGPT's new default model. This natively multimodal system handles text, images, and audio, while surpassing GPT-4 in writing, coding, STEM problem-solving, and following instructions.
Key Features of GPT-4o
- Enhanced Multimodal Capabilities:
- Improved Performance:
- Smarter Problem-Solving:
- Masters complex STEM tasks and coding workflows
- Produces cleaner code and better technical solutions
- User Experience Enhancements:
- Cost Efficiency:
- Standard version costs $2.50 per million input tokens and $10 per million output tokens, with a more affordable "mini" version available
Future Developments
- GPT-4.1: OpenAI plans to release GPT-4.1 as an upgrade to GPT-4o, featuring new "mini" and "nano" variants for different use cases.
- GPT-5: The upcoming GPT-5 aims to unify OpenAI's technology while advancing AI capabilities further.
Additional Features in ChatGPT
- Memory Updates: ChatGPT now retains conversation history for more personalized interactions—available to Pro and Plus users except in the EU and U.K.
- Image Generation: Features DALL-E-powered image creation with built-in watermarking for transparency.
- Enhanced Reasoning Tools: New features like "Structured Thoughts" and "Reasoning Recap" help explain the AI's logic step-by-step.
The shift from GPT-4 to GPT-4o marks a major advance in AI chatbot technology, bringing better multimodal capabilities, performance, and user experience. As OpenAI develops GPT-4.1 and GPT-5, it continues pushing AI innovation forward while meeting diverse user needs.
1.3.7 🖼️ DALL·E Models (2021–2023)
- DALL·E 1: Released in 2021, this pioneering model could generate abstract digital art from text descriptions. While its outputs were often surreal and less precise, it demonstrated the potential of AI image generation and laid the groundwork for future improvements.
- DALL·E 2: Launched in 2022, this version marked a significant advancement with photorealistic image generation capabilities. It introduced features like inpainting (editing specific parts of images) and outpainting (extending images beyond their original borders), while offering better control over artistic styles and composition.
- DALL·E 3: Released in 2023, this represents the current state-of-the-art in AI image generation. It excels at understanding complex prompts, maintaining consistency in details, and producing more accurate representations of human faces and hands. The model can handle nuanced artistic direction and generate images in specific art styles with remarkable precision.
DALL·E 3's integration with GPT-4 via ChatGPT has revolutionized the creative workflow. The AI can now interpret natural language descriptions more accurately, suggest improvements to prompts, and maintain artistic consistency across multiple generations. This makes it an invaluable tool for professional designers, content creators, and developers working on app-generated art, book illustrations, marketing materials, and creative prototyping. The model also includes built-in safety features and content filters to ensure responsible image generation.
1.3.8 🎙️ Whisper (2022)
Whisper, released in September 2022, represents a breakthrough in automatic speech recognition (ASR) technology. This open-source model can transcribe speech in multiple languages with remarkable accuracy, translate between languages, and generate subtitles automatically. What makes Whisper particularly impressive is its robust performance across diverse audio conditions - from clear studio recordings to noisy background environments.
The model comes in several sizes to accommodate different use cases:
- Tiny (39M parameters): Fastest but least accurate, ideal for real-time applications
- Base (74M parameters): Balanced performance for everyday use
- Small (244M parameters): Improved accuracy with reasonable speed
- Medium (769M parameters): High accuracy with moderate resource requirements
- Large (1.5B parameters): Maximum accuracy for professional applications
OpenAI has also made Whisper available through their API as whisper-1, offering developers a simple way to integrate speech recognition capabilities without managing the infrastructure. The API version is optimized for production use, providing consistent performance and reliability while handling various audio formats and languages.
1.3.9 🔎 Embeddings (2021–Present)
The text-embedding-ada-002 model represents a significant advancement in Natural Language Processing (NLP), becoming an industry standard for converting text into numerical vectors that capture semantic meaning. These vectors allow computers to understand and compare text based on its actual meaning rather than just matching keywords. The model excels at semantic comparisons, enabling developers to build sophisticated tools like:
- Custom search engines that understand context and user intent, delivering more relevant results than traditional keyword-based search
- Vector databases for RAG (Retrieval-Augmented Generation) that enhance AI responses by efficiently retrieving relevant information from large document collections
- Personalized recommendations that analyze user preferences and behavior patterns to suggest highly relevant content or products
Each embedding is a dense vector of 1,536 dimensions, providing a rich mathematical representation of text that captures nuanced relationships between words and concepts. This makes the model particularly effective for tasks requiring deep semantic understanding.
Example: Creating Embeddings
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create an embedding
response = client.embeddings.create(
model="text-embedding-ada-002",
input="How do I cancel my subscription?",
encoding_format="float" # Explicitly specify the encoding format
)
# Extract the embedding vector
embedding_vector = response.data[0].embedding
# Optional: Convert to numpy array for further processing
import numpy as np
embedding_array = np.array(embedding_vector)Here's a breakdown of what the code does:
- Setup and Initialization:
- Imports the OpenAI library
- Creates a client instance to interact with OpenAI's API
- Creating the Embedding:
- Uses the "text-embedding-ada-002" model, which is the standard model for converting text into numerical vectors
- Takes an example text input ("How do I cancel my subscription?")
- Specifies "float" as the encoding format for the output
- Handling the Result:
- Extracts the embedding vector from the response
- Optionally converts it to a numpy array for further data processing
The resulting embedding is a 1,536-dimensional vector that represents the semantic meaning of the input text, making it useful for tasks like semantic search and content recommendations
1.3.10 Looking Ahead
OpenAI continues to evolve rapidly, with new releases announced regularly. The platform's growth is particularly notable in three key areas:
- Multi-modal capabilities: Systems can now process and generate text, images, and audio simultaneously, enabling more natural and comprehensive AI interactions
- Memory features: AI models can maintain context across conversations and retain important information about user preferences and past interactions
- Tool integrations: Advanced features like code interpreters for executing and debugging code, web browsing for real-time information access, and API connections for integrating with external services have become standard offerings
This evolution represents a fundamental shift in AI application development. Developers are now creating sophisticated applications that can:
- See: Process and analyze visual information through image recognition and generation
- Hear: Convert speech to text and understand audio inputs with high accuracy
- Act: Make informed decisions and take actions based on complex reasoning and multiple data sources
All of these capabilities are underpinned by intelligent reasoning systems that can understand context, follow complex instructions, and adapt to user needs.
1.3 The Evolution of OpenAI’s Models
To effectively harness OpenAI's tools, it's essential to understand their historical development and evolution. These sophisticated AI models represent the culmination of extensive research, countless iterations, and significant technological breakthroughs. Each generation has built upon the successes and lessons learned from its predecessors, incorporating new capabilities and addressing previous limitations.
Understanding the evolution of GPT and other models is crucial because it:
- Helps you select the optimal model by understanding each version's specific strengths and capabilities
- Enables you to anticipate and work around known limitations that existed in earlier versions
- Allows you to future-proof your applications by understanding the trajectory of model development
- Provides insight into how different models handle various tasks and use cases
- Helps you make informed decisions about resource allocation and API usage
Let's explore the fascinating journey of OpenAI's major models, examining how each iteration has pushed the boundaries of artificial intelligence and opened new possibilities for developers and creators.
1.3.1 🧠 GPT-1 (2018): The Prototype
OpenAI's journey into large language models began with a groundbreaking experiment in 2018: GPT-1, a 117 million parameter language model. While modest by today's standards, this model could complete simple text prompts with surprising coherence. Though GPT-1 was never released as a public API, it proved a revolutionary concept in AI development: the effectiveness of pretraining a model on vast amounts of text data, followed by fine-tuning it for specific tasks. This two-step approach would become the foundation for all future GPT models.
Key Traits:
- Very basic understanding of language, capable of simple text completion and basic pattern recognition
- Primarily served as a research project to validate the pretraining and fine-tuning approach
- Had limited context understanding and often produced inconsistent outputs
- Demonstrated the potential of transformer-based architectures in language processing
- Served as proof-of-concept for what was to come in the field of natural language processing
While you won't directly use GPT-1 in any applications today, its success catalyzed the development of increasingly sophisticated language models and launched the entire field of large language models that we know today.
1.3.2 🧠 GPT-2 (2019): The First Leap Forward
GPT-2 marked a significant milestone as the first OpenAI model to generate widespread public interest and debate. With 1.5 billion parameters - a massive leap from GPT-1's 117 million - this model demonstrated unprecedented capabilities in natural language processing. It could generate remarkably coherent text, create detailed summaries of complex content, and even continue narrative stories with surprising consistency. The model's capabilities were so advanced that OpenAI made the unprecedented decision to initially withhold the full model release, citing concerns about potential misuse in generating deceptive content or automated disinformation campaigns.
Capabilities:
- Enhanced natural language understanding with significantly improved coherence and contextual awareness compared to its predecessor
- Advanced text generation abilities, including story continuation, article writing, and creative writing tasks
- Sophisticated summarization capabilities that could distill key points from longer texts
- Basic question-answering abilities, though with notable limitations
- Still struggled with logic, math, and long context
Why It Matters:
GPT-2 represented a pivotal moment in AI development, sparking crucial discussions about AI safety and ethical considerations in AI deployment. It introduced the concept of prompt-based interfaces, revolutionizing how humans interact with AI systems. This model's release strategy also established important precedents for responsible AI development, balancing technological advancement with societal impact. The debates it sparked continue to influence AI policy and development practices today.
1.3.3 🧠 GPT-3 (2020): The API Era Begins
GPT-3 marked a revolutionary transformation in the AI landscape. This wasn't just another iteration - it represented a fundamental shift in how AI could be accessed and utilized.
With an unprecedented 175 billion parameters, GPT-3 became the first large-scale language model available through a public API. This democratization of AI technology was groundbreaking - it meant that anyone, regardless of their resources or technical expertise, could integrate sophisticated AI capabilities into their products. From independent developers working on innovative startups to Fortune 500 companies developing enterprise solutions, GPT-3's API opened doors to AI implementation that were previously locked.
What GPT-3 Introduced:
- Natural conversation-style prompting that allowed for more intuitive interactions with AI, moving away from rigid command structures to more natural language interfaces
- Remarkable performance across a wide range of language tasks, including sophisticated summarization capabilities, contextually aware question-answering systems, and high-quality content generation for various purposes
- Introduction of text-davinci-003, a significant milestone as the first "tuned" model specifically optimized for following complex instructions with greater accuracy and reliability
Example: Using GPT-3 (text-davinci-003)
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create a chat completion
response = client.chat.completions.create(
model="gpt-3.5-turbo", # More cost-effective than davinci-003
messages=[
{
"role": "user",
"content": "Write a short poem about coffee and coding."
}
],
temperature=0.7,
max_tokens=100
)
# Print the generated text
print(response.choices[0].message.content)Here's a breakdown of what each part does:
- Import and Initialization: The code imports the OpenAI library and initializes the client object.
- Creating a Chat Completion: The code calls the chat.completions.create() method with several parameters:
- model: Uses "gpt-3.5-turbo", which is more cost-effective than davinci-003
- messages: A list containing the conversation history, with a single user message requesting a poem about coffee and coding
- temperature: Set to 0.7, which controls the randomness of the output
- max_tokens: Limits the response length to 100 tokens
- Output: Finally, it prints the generated response from the model using the first choice's message content.
GPT-3 helped launch thousands of startups. It was the model behind the first waves of AI writing tools, resume builders, and coding assistants.
1.3.4 🧠 GPT-3.5 (2022): From Text to Chat
GPT-3.5 represented a significant evolution in OpenAI's language models, introducing major improvements in two critical areas. First, its instruction following capabilities were substantially enhanced, allowing it to better understand and execute complex, multi-step tasks. Second, its conversational accuracy showed remarkable improvement, with more natural and contextually appropriate responses. The most revolutionary change was the introduction of Chat Completions - a fundamental shift from the traditional single-prompt system to a more sophisticated message-based format that uses specific role labels:
system: Sets the behavior and context for the AI
• user: Contains the human input/question
• assistant: Contains the AI's responses
This new architecture enabled more natural, flowing conversations and better context management across multiple exchanges.
Major Changes:
- Chat format support via
gpt-3.5-turbo- This new model became the standard for chat-based applications, offering a more efficient and cost-effective solution for conversational AI - Better contextual awareness - The model could now maintain conversation history and understand references to previous messages, making interactions feel more natural and coherent
- Faster and cheaper than GPT-3 - Despite its improvements, GPT-3.5 was optimized for better performance, processing requests more quickly while requiring fewer computational resources
- Used in early versions of ChatGPT - This model powered the initial release of ChatGPT, demonstrating its capabilities in real-world applications and helping establish ChatGPT as a breakthrough in conversational AI
Example: GPT-3.5 Chat Completion
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create a chat completion
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What's the difference between an array and a list in Python?"}
]
)
# Extract and print the response
try:
print(response.choices[0].message.content)
except Exception as e:
print(f"Error processing response: {e}")Let's break down this code example:
1. Setup and Initialization:
- Imports the OpenAI library and creates a client instance to interact with the API
2. Creating the Chat Completion:
- Uses the chat.completions.create() method with the following parameters:
- model: Specifies "gpt-3.5-turbo", which is more cost-effective than older models
- messages: A list containing two dictionaries:
- A system message defining the AI's role
- A user message asking about Python arrays vs lists
3. Error Handling:
- Implements a try-except block to gracefully handle any potential errors during response processing
- If successful, prints the AI's response
- If an error occurs, prints an error message with details
This shift laid the foundation for modern AI chatbots—apps that remember context, clarify intent, and simulate real conversations.
1.3.5 🧠 GPT-4 (2023): Multi-Modal Intelligence
GPT-4 represents a transformative leap in OpenAI's technology, introducing unprecedented capabilities across multiple domains. The model features enhanced reasoning abilities that allow it to process complex logic chains, expanded memory capacity for handling longer contexts, and groundbreaking multi-modal capabilities that enable it to process both text and images (though API image support remains limited to specific use cases).
GPT-4's expanded capabilities include:
- Advanced code generation and debugging, with significantly reduced error rates compared to previous models
- Sophisticated instruction following that captures subtle nuances and implied context
- Enhanced document analysis that can process and synthesize information from lengthy texts
- Improved conversation management with consistent context retention across extended dialogues
- Superior prompt handling capabilities, including nested instructions and multi-step reasoning tasks
Key Advantages:
- Substantially improved accuracy in technical domains, particularly in programming and mathematical computations
- Exceptional performance across various standardized assessments, demonstrating human-expert level understanding
- Enhanced reasoning capabilities that enable more sophisticated problem-solving and analysis
Available Versions and Deployment Options:
"gpt-4"– The foundation model offering maximum accuracy and capability, though with higher latency and cost"gpt-4-turbo"– A performance-optimized variant that balances capability with efficiency, making it ideal for production environments and high-volume applications
1.3.6 🧠 The Latest Versions of OpenAI's ChatGPT: GPT-4o and Beyond
GPT-4 Turbo offers more than cost savings—it brings significant enhancements:
- Larger context windows (up to 128k tokens in some environments)
- Faster generation speeds
- More efficient API usage at scale
OpenAI has positioned GPT-4 Turbo as the default choice for production apps, especially in tools like ChatGPT Pro and custom GPTs.
OpenAI's latest ChatGPT updates mark a pivotal moment in AI chatbot evolution. These changes include retiring GPT-4, introducing GPT-4o as the default model, and planning future versions like GPT-4.1 and GPT-5. Here's what you need to know.
Retirement of GPT-4 and Introduction of GPT-4o
- GPT-4 Retirement: After April 30, 2025, GPT-4 will be removed from the ChatGPT interface but will remain available through OpenAI's API for developers and enterprise users.
- GPT-4o Overview: Launched in May 2024, GPT-4o serves as ChatGPT's new default model. This natively multimodal system handles text, images, and audio, while surpassing GPT-4 in writing, coding, STEM problem-solving, and following instructions.
Key Features of GPT-4o
- Enhanced Multimodal Capabilities:
- Improved Performance:
- Smarter Problem-Solving:
- Masters complex STEM tasks and coding workflows
- Produces cleaner code and better technical solutions
- User Experience Enhancements:
- Cost Efficiency:
- Standard version costs $2.50 per million input tokens and $10 per million output tokens, with a more affordable "mini" version available
Future Developments
- GPT-4.1: OpenAI plans to release GPT-4.1 as an upgrade to GPT-4o, featuring new "mini" and "nano" variants for different use cases.
- GPT-5: The upcoming GPT-5 aims to unify OpenAI's technology while advancing AI capabilities further.
Additional Features in ChatGPT
- Memory Updates: ChatGPT now retains conversation history for more personalized interactions—available to Pro and Plus users except in the EU and U.K.
- Image Generation: Features DALL-E-powered image creation with built-in watermarking for transparency.
- Enhanced Reasoning Tools: New features like "Structured Thoughts" and "Reasoning Recap" help explain the AI's logic step-by-step.
The shift from GPT-4 to GPT-4o marks a major advance in AI chatbot technology, bringing better multimodal capabilities, performance, and user experience. As OpenAI develops GPT-4.1 and GPT-5, it continues pushing AI innovation forward while meeting diverse user needs.
1.3.7 🖼️ DALL·E Models (2021–2023)
- DALL·E 1: Released in 2021, this pioneering model could generate abstract digital art from text descriptions. While its outputs were often surreal and less precise, it demonstrated the potential of AI image generation and laid the groundwork for future improvements.
- DALL·E 2: Launched in 2022, this version marked a significant advancement with photorealistic image generation capabilities. It introduced features like inpainting (editing specific parts of images) and outpainting (extending images beyond their original borders), while offering better control over artistic styles and composition.
- DALL·E 3: Released in 2023, this represents the current state-of-the-art in AI image generation. It excels at understanding complex prompts, maintaining consistency in details, and producing more accurate representations of human faces and hands. The model can handle nuanced artistic direction and generate images in specific art styles with remarkable precision.
DALL·E 3's integration with GPT-4 via ChatGPT has revolutionized the creative workflow. The AI can now interpret natural language descriptions more accurately, suggest improvements to prompts, and maintain artistic consistency across multiple generations. This makes it an invaluable tool for professional designers, content creators, and developers working on app-generated art, book illustrations, marketing materials, and creative prototyping. The model also includes built-in safety features and content filters to ensure responsible image generation.
1.3.8 🎙️ Whisper (2022)
Whisper, released in September 2022, represents a breakthrough in automatic speech recognition (ASR) technology. This open-source model can transcribe speech in multiple languages with remarkable accuracy, translate between languages, and generate subtitles automatically. What makes Whisper particularly impressive is its robust performance across diverse audio conditions - from clear studio recordings to noisy background environments.
The model comes in several sizes to accommodate different use cases:
- Tiny (39M parameters): Fastest but least accurate, ideal for real-time applications
- Base (74M parameters): Balanced performance for everyday use
- Small (244M parameters): Improved accuracy with reasonable speed
- Medium (769M parameters): High accuracy with moderate resource requirements
- Large (1.5B parameters): Maximum accuracy for professional applications
OpenAI has also made Whisper available through their API as whisper-1, offering developers a simple way to integrate speech recognition capabilities without managing the infrastructure. The API version is optimized for production use, providing consistent performance and reliability while handling various audio formats and languages.
1.3.9 🔎 Embeddings (2021–Present)
The text-embedding-ada-002 model represents a significant advancement in Natural Language Processing (NLP), becoming an industry standard for converting text into numerical vectors that capture semantic meaning. These vectors allow computers to understand and compare text based on its actual meaning rather than just matching keywords. The model excels at semantic comparisons, enabling developers to build sophisticated tools like:
- Custom search engines that understand context and user intent, delivering more relevant results than traditional keyword-based search
- Vector databases for RAG (Retrieval-Augmented Generation) that enhance AI responses by efficiently retrieving relevant information from large document collections
- Personalized recommendations that analyze user preferences and behavior patterns to suggest highly relevant content or products
Each embedding is a dense vector of 1,536 dimensions, providing a rich mathematical representation of text that captures nuanced relationships between words and concepts. This makes the model particularly effective for tasks requiring deep semantic understanding.
Example: Creating Embeddings
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create an embedding
response = client.embeddings.create(
model="text-embedding-ada-002",
input="How do I cancel my subscription?",
encoding_format="float" # Explicitly specify the encoding format
)
# Extract the embedding vector
embedding_vector = response.data[0].embedding
# Optional: Convert to numpy array for further processing
import numpy as np
embedding_array = np.array(embedding_vector)Here's a breakdown of what the code does:
- Setup and Initialization:
- Imports the OpenAI library
- Creates a client instance to interact with OpenAI's API
- Creating the Embedding:
- Uses the "text-embedding-ada-002" model, which is the standard model for converting text into numerical vectors
- Takes an example text input ("How do I cancel my subscription?")
- Specifies "float" as the encoding format for the output
- Handling the Result:
- Extracts the embedding vector from the response
- Optionally converts it to a numpy array for further data processing
The resulting embedding is a 1,536-dimensional vector that represents the semantic meaning of the input text, making it useful for tasks like semantic search and content recommendations
1.3.10 Looking Ahead
OpenAI continues to evolve rapidly, with new releases announced regularly. The platform's growth is particularly notable in three key areas:
- Multi-modal capabilities: Systems can now process and generate text, images, and audio simultaneously, enabling more natural and comprehensive AI interactions
- Memory features: AI models can maintain context across conversations and retain important information about user preferences and past interactions
- Tool integrations: Advanced features like code interpreters for executing and debugging code, web browsing for real-time information access, and API connections for integrating with external services have become standard offerings
This evolution represents a fundamental shift in AI application development. Developers are now creating sophisticated applications that can:
- See: Process and analyze visual information through image recognition and generation
- Hear: Convert speech to text and understand audio inputs with high accuracy
- Act: Make informed decisions and take actions based on complex reasoning and multiple data sources
All of these capabilities are underpinned by intelligent reasoning systems that can understand context, follow complex instructions, and adapt to user needs.
1.3 The Evolution of OpenAI’s Models
To effectively harness OpenAI's tools, it's essential to understand their historical development and evolution. These sophisticated AI models represent the culmination of extensive research, countless iterations, and significant technological breakthroughs. Each generation has built upon the successes and lessons learned from its predecessors, incorporating new capabilities and addressing previous limitations.
Understanding the evolution of GPT and other models is crucial because it:
- Helps you select the optimal model by understanding each version's specific strengths and capabilities
- Enables you to anticipate and work around known limitations that existed in earlier versions
- Allows you to future-proof your applications by understanding the trajectory of model development
- Provides insight into how different models handle various tasks and use cases
- Helps you make informed decisions about resource allocation and API usage
Let's explore the fascinating journey of OpenAI's major models, examining how each iteration has pushed the boundaries of artificial intelligence and opened new possibilities for developers and creators.
1.3.1 🧠 GPT-1 (2018): The Prototype
OpenAI's journey into large language models began with a groundbreaking experiment in 2018: GPT-1, a 117 million parameter language model. While modest by today's standards, this model could complete simple text prompts with surprising coherence. Though GPT-1 was never released as a public API, it proved a revolutionary concept in AI development: the effectiveness of pretraining a model on vast amounts of text data, followed by fine-tuning it for specific tasks. This two-step approach would become the foundation for all future GPT models.
Key Traits:
- Very basic understanding of language, capable of simple text completion and basic pattern recognition
- Primarily served as a research project to validate the pretraining and fine-tuning approach
- Had limited context understanding and often produced inconsistent outputs
- Demonstrated the potential of transformer-based architectures in language processing
- Served as proof-of-concept for what was to come in the field of natural language processing
While you won't directly use GPT-1 in any applications today, its success catalyzed the development of increasingly sophisticated language models and launched the entire field of large language models that we know today.
1.3.2 🧠 GPT-2 (2019): The First Leap Forward
GPT-2 marked a significant milestone as the first OpenAI model to generate widespread public interest and debate. With 1.5 billion parameters - a massive leap from GPT-1's 117 million - this model demonstrated unprecedented capabilities in natural language processing. It could generate remarkably coherent text, create detailed summaries of complex content, and even continue narrative stories with surprising consistency. The model's capabilities were so advanced that OpenAI made the unprecedented decision to initially withhold the full model release, citing concerns about potential misuse in generating deceptive content or automated disinformation campaigns.
Capabilities:
- Enhanced natural language understanding with significantly improved coherence and contextual awareness compared to its predecessor
- Advanced text generation abilities, including story continuation, article writing, and creative writing tasks
- Sophisticated summarization capabilities that could distill key points from longer texts
- Basic question-answering abilities, though with notable limitations
- Still struggled with logic, math, and long context
Why It Matters:
GPT-2 represented a pivotal moment in AI development, sparking crucial discussions about AI safety and ethical considerations in AI deployment. It introduced the concept of prompt-based interfaces, revolutionizing how humans interact with AI systems. This model's release strategy also established important precedents for responsible AI development, balancing technological advancement with societal impact. The debates it sparked continue to influence AI policy and development practices today.
1.3.3 🧠 GPT-3 (2020): The API Era Begins
GPT-3 marked a revolutionary transformation in the AI landscape. This wasn't just another iteration - it represented a fundamental shift in how AI could be accessed and utilized.
With an unprecedented 175 billion parameters, GPT-3 became the first large-scale language model available through a public API. This democratization of AI technology was groundbreaking - it meant that anyone, regardless of their resources or technical expertise, could integrate sophisticated AI capabilities into their products. From independent developers working on innovative startups to Fortune 500 companies developing enterprise solutions, GPT-3's API opened doors to AI implementation that were previously locked.
What GPT-3 Introduced:
- Natural conversation-style prompting that allowed for more intuitive interactions with AI, moving away from rigid command structures to more natural language interfaces
- Remarkable performance across a wide range of language tasks, including sophisticated summarization capabilities, contextually aware question-answering systems, and high-quality content generation for various purposes
- Introduction of text-davinci-003, a significant milestone as the first "tuned" model specifically optimized for following complex instructions with greater accuracy and reliability
Example: Using GPT-3 (text-davinci-003)
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create a chat completion
response = client.chat.completions.create(
model="gpt-3.5-turbo", # More cost-effective than davinci-003
messages=[
{
"role": "user",
"content": "Write a short poem about coffee and coding."
}
],
temperature=0.7,
max_tokens=100
)
# Print the generated text
print(response.choices[0].message.content)Here's a breakdown of what each part does:
- Import and Initialization: The code imports the OpenAI library and initializes the client object.
- Creating a Chat Completion: The code calls the chat.completions.create() method with several parameters:
- model: Uses "gpt-3.5-turbo", which is more cost-effective than davinci-003
- messages: A list containing the conversation history, with a single user message requesting a poem about coffee and coding
- temperature: Set to 0.7, which controls the randomness of the output
- max_tokens: Limits the response length to 100 tokens
- Output: Finally, it prints the generated response from the model using the first choice's message content.
GPT-3 helped launch thousands of startups. It was the model behind the first waves of AI writing tools, resume builders, and coding assistants.
1.3.4 🧠 GPT-3.5 (2022): From Text to Chat
GPT-3.5 represented a significant evolution in OpenAI's language models, introducing major improvements in two critical areas. First, its instruction following capabilities were substantially enhanced, allowing it to better understand and execute complex, multi-step tasks. Second, its conversational accuracy showed remarkable improvement, with more natural and contextually appropriate responses. The most revolutionary change was the introduction of Chat Completions - a fundamental shift from the traditional single-prompt system to a more sophisticated message-based format that uses specific role labels:
system: Sets the behavior and context for the AI
• user: Contains the human input/question
• assistant: Contains the AI's responses
This new architecture enabled more natural, flowing conversations and better context management across multiple exchanges.
Major Changes:
- Chat format support via
gpt-3.5-turbo- This new model became the standard for chat-based applications, offering a more efficient and cost-effective solution for conversational AI - Better contextual awareness - The model could now maintain conversation history and understand references to previous messages, making interactions feel more natural and coherent
- Faster and cheaper than GPT-3 - Despite its improvements, GPT-3.5 was optimized for better performance, processing requests more quickly while requiring fewer computational resources
- Used in early versions of ChatGPT - This model powered the initial release of ChatGPT, demonstrating its capabilities in real-world applications and helping establish ChatGPT as a breakthrough in conversational AI
Example: GPT-3.5 Chat Completion
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create a chat completion
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What's the difference between an array and a list in Python?"}
]
)
# Extract and print the response
try:
print(response.choices[0].message.content)
except Exception as e:
print(f"Error processing response: {e}")Let's break down this code example:
1. Setup and Initialization:
- Imports the OpenAI library and creates a client instance to interact with the API
2. Creating the Chat Completion:
- Uses the chat.completions.create() method with the following parameters:
- model: Specifies "gpt-3.5-turbo", which is more cost-effective than older models
- messages: A list containing two dictionaries:
- A system message defining the AI's role
- A user message asking about Python arrays vs lists
3. Error Handling:
- Implements a try-except block to gracefully handle any potential errors during response processing
- If successful, prints the AI's response
- If an error occurs, prints an error message with details
This shift laid the foundation for modern AI chatbots—apps that remember context, clarify intent, and simulate real conversations.
1.3.5 🧠 GPT-4 (2023): Multi-Modal Intelligence
GPT-4 represents a transformative leap in OpenAI's technology, introducing unprecedented capabilities across multiple domains. The model features enhanced reasoning abilities that allow it to process complex logic chains, expanded memory capacity for handling longer contexts, and groundbreaking multi-modal capabilities that enable it to process both text and images (though API image support remains limited to specific use cases).
GPT-4's expanded capabilities include:
- Advanced code generation and debugging, with significantly reduced error rates compared to previous models
- Sophisticated instruction following that captures subtle nuances and implied context
- Enhanced document analysis that can process and synthesize information from lengthy texts
- Improved conversation management with consistent context retention across extended dialogues
- Superior prompt handling capabilities, including nested instructions and multi-step reasoning tasks
Key Advantages:
- Substantially improved accuracy in technical domains, particularly in programming and mathematical computations
- Exceptional performance across various standardized assessments, demonstrating human-expert level understanding
- Enhanced reasoning capabilities that enable more sophisticated problem-solving and analysis
Available Versions and Deployment Options:
"gpt-4"– The foundation model offering maximum accuracy and capability, though with higher latency and cost"gpt-4-turbo"– A performance-optimized variant that balances capability with efficiency, making it ideal for production environments and high-volume applications
1.3.6 🧠 The Latest Versions of OpenAI's ChatGPT: GPT-4o and Beyond
GPT-4 Turbo offers more than cost savings—it brings significant enhancements:
- Larger context windows (up to 128k tokens in some environments)
- Faster generation speeds
- More efficient API usage at scale
OpenAI has positioned GPT-4 Turbo as the default choice for production apps, especially in tools like ChatGPT Pro and custom GPTs.
OpenAI's latest ChatGPT updates mark a pivotal moment in AI chatbot evolution. These changes include retiring GPT-4, introducing GPT-4o as the default model, and planning future versions like GPT-4.1 and GPT-5. Here's what you need to know.
Retirement of GPT-4 and Introduction of GPT-4o
- GPT-4 Retirement: After April 30, 2025, GPT-4 will be removed from the ChatGPT interface but will remain available through OpenAI's API for developers and enterprise users.
- GPT-4o Overview: Launched in May 2024, GPT-4o serves as ChatGPT's new default model. This natively multimodal system handles text, images, and audio, while surpassing GPT-4 in writing, coding, STEM problem-solving, and following instructions.
Key Features of GPT-4o
- Enhanced Multimodal Capabilities:
- Improved Performance:
- Smarter Problem-Solving:
- Masters complex STEM tasks and coding workflows
- Produces cleaner code and better technical solutions
- User Experience Enhancements:
- Cost Efficiency:
- Standard version costs $2.50 per million input tokens and $10 per million output tokens, with a more affordable "mini" version available
Future Developments
- GPT-4.1: OpenAI plans to release GPT-4.1 as an upgrade to GPT-4o, featuring new "mini" and "nano" variants for different use cases.
- GPT-5: The upcoming GPT-5 aims to unify OpenAI's technology while advancing AI capabilities further.
Additional Features in ChatGPT
- Memory Updates: ChatGPT now retains conversation history for more personalized interactions—available to Pro and Plus users except in the EU and U.K.
- Image Generation: Features DALL-E-powered image creation with built-in watermarking for transparency.
- Enhanced Reasoning Tools: New features like "Structured Thoughts" and "Reasoning Recap" help explain the AI's logic step-by-step.
The shift from GPT-4 to GPT-4o marks a major advance in AI chatbot technology, bringing better multimodal capabilities, performance, and user experience. As OpenAI develops GPT-4.1 and GPT-5, it continues pushing AI innovation forward while meeting diverse user needs.
1.3.7 🖼️ DALL·E Models (2021–2023)
- DALL·E 1: Released in 2021, this pioneering model could generate abstract digital art from text descriptions. While its outputs were often surreal and less precise, it demonstrated the potential of AI image generation and laid the groundwork for future improvements.
- DALL·E 2: Launched in 2022, this version marked a significant advancement with photorealistic image generation capabilities. It introduced features like inpainting (editing specific parts of images) and outpainting (extending images beyond their original borders), while offering better control over artistic styles and composition.
- DALL·E 3: Released in 2023, this represents the current state-of-the-art in AI image generation. It excels at understanding complex prompts, maintaining consistency in details, and producing more accurate representations of human faces and hands. The model can handle nuanced artistic direction and generate images in specific art styles with remarkable precision.
DALL·E 3's integration with GPT-4 via ChatGPT has revolutionized the creative workflow. The AI can now interpret natural language descriptions more accurately, suggest improvements to prompts, and maintain artistic consistency across multiple generations. This makes it an invaluable tool for professional designers, content creators, and developers working on app-generated art, book illustrations, marketing materials, and creative prototyping. The model also includes built-in safety features and content filters to ensure responsible image generation.
1.3.8 🎙️ Whisper (2022)
Whisper, released in September 2022, represents a breakthrough in automatic speech recognition (ASR) technology. This open-source model can transcribe speech in multiple languages with remarkable accuracy, translate between languages, and generate subtitles automatically. What makes Whisper particularly impressive is its robust performance across diverse audio conditions - from clear studio recordings to noisy background environments.
The model comes in several sizes to accommodate different use cases:
- Tiny (39M parameters): Fastest but least accurate, ideal for real-time applications
- Base (74M parameters): Balanced performance for everyday use
- Small (244M parameters): Improved accuracy with reasonable speed
- Medium (769M parameters): High accuracy with moderate resource requirements
- Large (1.5B parameters): Maximum accuracy for professional applications
OpenAI has also made Whisper available through their API as whisper-1, offering developers a simple way to integrate speech recognition capabilities without managing the infrastructure. The API version is optimized for production use, providing consistent performance and reliability while handling various audio formats and languages.
1.3.9 🔎 Embeddings (2021–Present)
The text-embedding-ada-002 model represents a significant advancement in Natural Language Processing (NLP), becoming an industry standard for converting text into numerical vectors that capture semantic meaning. These vectors allow computers to understand and compare text based on its actual meaning rather than just matching keywords. The model excels at semantic comparisons, enabling developers to build sophisticated tools like:
- Custom search engines that understand context and user intent, delivering more relevant results than traditional keyword-based search
- Vector databases for RAG (Retrieval-Augmented Generation) that enhance AI responses by efficiently retrieving relevant information from large document collections
- Personalized recommendations that analyze user preferences and behavior patterns to suggest highly relevant content or products
Each embedding is a dense vector of 1,536 dimensions, providing a rich mathematical representation of text that captures nuanced relationships between words and concepts. This makes the model particularly effective for tasks requiring deep semantic understanding.
Example: Creating Embeddings
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create an embedding
response = client.embeddings.create(
model="text-embedding-ada-002",
input="How do I cancel my subscription?",
encoding_format="float" # Explicitly specify the encoding format
)
# Extract the embedding vector
embedding_vector = response.data[0].embedding
# Optional: Convert to numpy array for further processing
import numpy as np
embedding_array = np.array(embedding_vector)Here's a breakdown of what the code does:
- Setup and Initialization:
- Imports the OpenAI library
- Creates a client instance to interact with OpenAI's API
- Creating the Embedding:
- Uses the "text-embedding-ada-002" model, which is the standard model for converting text into numerical vectors
- Takes an example text input ("How do I cancel my subscription?")
- Specifies "float" as the encoding format for the output
- Handling the Result:
- Extracts the embedding vector from the response
- Optionally converts it to a numpy array for further data processing
The resulting embedding is a 1,536-dimensional vector that represents the semantic meaning of the input text, making it useful for tasks like semantic search and content recommendations
1.3.10 Looking Ahead
OpenAI continues to evolve rapidly, with new releases announced regularly. The platform's growth is particularly notable in three key areas:
- Multi-modal capabilities: Systems can now process and generate text, images, and audio simultaneously, enabling more natural and comprehensive AI interactions
- Memory features: AI models can maintain context across conversations and retain important information about user preferences and past interactions
- Tool integrations: Advanced features like code interpreters for executing and debugging code, web browsing for real-time information access, and API connections for integrating with external services have become standard offerings
This evolution represents a fundamental shift in AI application development. Developers are now creating sophisticated applications that can:
- See: Process and analyze visual information through image recognition and generation
- Hear: Convert speech to text and understand audio inputs with high accuracy
- Act: Make informed decisions and take actions based on complex reasoning and multiple data sources
All of these capabilities are underpinned by intelligent reasoning systems that can understand context, follow complex instructions, and adapt to user needs.
1.3 The Evolution of OpenAI’s Models
To effectively harness OpenAI's tools, it's essential to understand their historical development and evolution. These sophisticated AI models represent the culmination of extensive research, countless iterations, and significant technological breakthroughs. Each generation has built upon the successes and lessons learned from its predecessors, incorporating new capabilities and addressing previous limitations.
Understanding the evolution of GPT and other models is crucial because it:
- Helps you select the optimal model by understanding each version's specific strengths and capabilities
- Enables you to anticipate and work around known limitations that existed in earlier versions
- Allows you to future-proof your applications by understanding the trajectory of model development
- Provides insight into how different models handle various tasks and use cases
- Helps you make informed decisions about resource allocation and API usage
Let's explore the fascinating journey of OpenAI's major models, examining how each iteration has pushed the boundaries of artificial intelligence and opened new possibilities for developers and creators.
1.3.1 🧠 GPT-1 (2018): The Prototype
OpenAI's journey into large language models began with a groundbreaking experiment in 2018: GPT-1, a 117 million parameter language model. While modest by today's standards, this model could complete simple text prompts with surprising coherence. Though GPT-1 was never released as a public API, it proved a revolutionary concept in AI development: the effectiveness of pretraining a model on vast amounts of text data, followed by fine-tuning it for specific tasks. This two-step approach would become the foundation for all future GPT models.
Key Traits:
- Very basic understanding of language, capable of simple text completion and basic pattern recognition
- Primarily served as a research project to validate the pretraining and fine-tuning approach
- Had limited context understanding and often produced inconsistent outputs
- Demonstrated the potential of transformer-based architectures in language processing
- Served as proof-of-concept for what was to come in the field of natural language processing
While you won't directly use GPT-1 in any applications today, its success catalyzed the development of increasingly sophisticated language models and launched the entire field of large language models that we know today.
1.3.2 🧠 GPT-2 (2019): The First Leap Forward
GPT-2 marked a significant milestone as the first OpenAI model to generate widespread public interest and debate. With 1.5 billion parameters - a massive leap from GPT-1's 117 million - this model demonstrated unprecedented capabilities in natural language processing. It could generate remarkably coherent text, create detailed summaries of complex content, and even continue narrative stories with surprising consistency. The model's capabilities were so advanced that OpenAI made the unprecedented decision to initially withhold the full model release, citing concerns about potential misuse in generating deceptive content or automated disinformation campaigns.
Capabilities:
- Enhanced natural language understanding with significantly improved coherence and contextual awareness compared to its predecessor
- Advanced text generation abilities, including story continuation, article writing, and creative writing tasks
- Sophisticated summarization capabilities that could distill key points from longer texts
- Basic question-answering abilities, though with notable limitations
- Still struggled with logic, math, and long context
Why It Matters:
GPT-2 represented a pivotal moment in AI development, sparking crucial discussions about AI safety and ethical considerations in AI deployment. It introduced the concept of prompt-based interfaces, revolutionizing how humans interact with AI systems. This model's release strategy also established important precedents for responsible AI development, balancing technological advancement with societal impact. The debates it sparked continue to influence AI policy and development practices today.
1.3.3 🧠 GPT-3 (2020): The API Era Begins
GPT-3 marked a revolutionary transformation in the AI landscape. This wasn't just another iteration - it represented a fundamental shift in how AI could be accessed and utilized.
With an unprecedented 175 billion parameters, GPT-3 became the first large-scale language model available through a public API. This democratization of AI technology was groundbreaking - it meant that anyone, regardless of their resources or technical expertise, could integrate sophisticated AI capabilities into their products. From independent developers working on innovative startups to Fortune 500 companies developing enterprise solutions, GPT-3's API opened doors to AI implementation that were previously locked.
What GPT-3 Introduced:
- Natural conversation-style prompting that allowed for more intuitive interactions with AI, moving away from rigid command structures to more natural language interfaces
- Remarkable performance across a wide range of language tasks, including sophisticated summarization capabilities, contextually aware question-answering systems, and high-quality content generation for various purposes
- Introduction of text-davinci-003, a significant milestone as the first "tuned" model specifically optimized for following complex instructions with greater accuracy and reliability
Example: Using GPT-3 (text-davinci-003)
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create a chat completion
response = client.chat.completions.create(
model="gpt-3.5-turbo", # More cost-effective than davinci-003
messages=[
{
"role": "user",
"content": "Write a short poem about coffee and coding."
}
],
temperature=0.7,
max_tokens=100
)
# Print the generated text
print(response.choices[0].message.content)Here's a breakdown of what each part does:
- Import and Initialization: The code imports the OpenAI library and initializes the client object.
- Creating a Chat Completion: The code calls the chat.completions.create() method with several parameters:
- model: Uses "gpt-3.5-turbo", which is more cost-effective than davinci-003
- messages: A list containing the conversation history, with a single user message requesting a poem about coffee and coding
- temperature: Set to 0.7, which controls the randomness of the output
- max_tokens: Limits the response length to 100 tokens
- Output: Finally, it prints the generated response from the model using the first choice's message content.
GPT-3 helped launch thousands of startups. It was the model behind the first waves of AI writing tools, resume builders, and coding assistants.
1.3.4 🧠 GPT-3.5 (2022): From Text to Chat
GPT-3.5 represented a significant evolution in OpenAI's language models, introducing major improvements in two critical areas. First, its instruction following capabilities were substantially enhanced, allowing it to better understand and execute complex, multi-step tasks. Second, its conversational accuracy showed remarkable improvement, with more natural and contextually appropriate responses. The most revolutionary change was the introduction of Chat Completions - a fundamental shift from the traditional single-prompt system to a more sophisticated message-based format that uses specific role labels:
system: Sets the behavior and context for the AI
• user: Contains the human input/question
• assistant: Contains the AI's responses
This new architecture enabled more natural, flowing conversations and better context management across multiple exchanges.
Major Changes:
- Chat format support via
gpt-3.5-turbo- This new model became the standard for chat-based applications, offering a more efficient and cost-effective solution for conversational AI - Better contextual awareness - The model could now maintain conversation history and understand references to previous messages, making interactions feel more natural and coherent
- Faster and cheaper than GPT-3 - Despite its improvements, GPT-3.5 was optimized for better performance, processing requests more quickly while requiring fewer computational resources
- Used in early versions of ChatGPT - This model powered the initial release of ChatGPT, demonstrating its capabilities in real-world applications and helping establish ChatGPT as a breakthrough in conversational AI
Example: GPT-3.5 Chat Completion
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create a chat completion
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What's the difference between an array and a list in Python?"}
]
)
# Extract and print the response
try:
print(response.choices[0].message.content)
except Exception as e:
print(f"Error processing response: {e}")Let's break down this code example:
1. Setup and Initialization:
- Imports the OpenAI library and creates a client instance to interact with the API
2. Creating the Chat Completion:
- Uses the chat.completions.create() method with the following parameters:
- model: Specifies "gpt-3.5-turbo", which is more cost-effective than older models
- messages: A list containing two dictionaries:
- A system message defining the AI's role
- A user message asking about Python arrays vs lists
3. Error Handling:
- Implements a try-except block to gracefully handle any potential errors during response processing
- If successful, prints the AI's response
- If an error occurs, prints an error message with details
This shift laid the foundation for modern AI chatbots—apps that remember context, clarify intent, and simulate real conversations.
1.3.5 🧠 GPT-4 (2023): Multi-Modal Intelligence
GPT-4 represents a transformative leap in OpenAI's technology, introducing unprecedented capabilities across multiple domains. The model features enhanced reasoning abilities that allow it to process complex logic chains, expanded memory capacity for handling longer contexts, and groundbreaking multi-modal capabilities that enable it to process both text and images (though API image support remains limited to specific use cases).
GPT-4's expanded capabilities include:
- Advanced code generation and debugging, with significantly reduced error rates compared to previous models
- Sophisticated instruction following that captures subtle nuances and implied context
- Enhanced document analysis that can process and synthesize information from lengthy texts
- Improved conversation management with consistent context retention across extended dialogues
- Superior prompt handling capabilities, including nested instructions and multi-step reasoning tasks
Key Advantages:
- Substantially improved accuracy in technical domains, particularly in programming and mathematical computations
- Exceptional performance across various standardized assessments, demonstrating human-expert level understanding
- Enhanced reasoning capabilities that enable more sophisticated problem-solving and analysis
Available Versions and Deployment Options:
"gpt-4"– The foundation model offering maximum accuracy and capability, though with higher latency and cost"gpt-4-turbo"– A performance-optimized variant that balances capability with efficiency, making it ideal for production environments and high-volume applications
1.3.6 🧠 The Latest Versions of OpenAI's ChatGPT: GPT-4o and Beyond
GPT-4 Turbo offers more than cost savings—it brings significant enhancements:
- Larger context windows (up to 128k tokens in some environments)
- Faster generation speeds
- More efficient API usage at scale
OpenAI has positioned GPT-4 Turbo as the default choice for production apps, especially in tools like ChatGPT Pro and custom GPTs.
OpenAI's latest ChatGPT updates mark a pivotal moment in AI chatbot evolution. These changes include retiring GPT-4, introducing GPT-4o as the default model, and planning future versions like GPT-4.1 and GPT-5. Here's what you need to know.
Retirement of GPT-4 and Introduction of GPT-4o
- GPT-4 Retirement: After April 30, 2025, GPT-4 will be removed from the ChatGPT interface but will remain available through OpenAI's API for developers and enterprise users.
- GPT-4o Overview: Launched in May 2024, GPT-4o serves as ChatGPT's new default model. This natively multimodal system handles text, images, and audio, while surpassing GPT-4 in writing, coding, STEM problem-solving, and following instructions.
Key Features of GPT-4o
- Enhanced Multimodal Capabilities:
- Improved Performance:
- Smarter Problem-Solving:
- Masters complex STEM tasks and coding workflows
- Produces cleaner code and better technical solutions
- User Experience Enhancements:
- Cost Efficiency:
- Standard version costs $2.50 per million input tokens and $10 per million output tokens, with a more affordable "mini" version available
Future Developments
- GPT-4.1: OpenAI plans to release GPT-4.1 as an upgrade to GPT-4o, featuring new "mini" and "nano" variants for different use cases.
- GPT-5: The upcoming GPT-5 aims to unify OpenAI's technology while advancing AI capabilities further.
Additional Features in ChatGPT
- Memory Updates: ChatGPT now retains conversation history for more personalized interactions—available to Pro and Plus users except in the EU and U.K.
- Image Generation: Features DALL-E-powered image creation with built-in watermarking for transparency.
- Enhanced Reasoning Tools: New features like "Structured Thoughts" and "Reasoning Recap" help explain the AI's logic step-by-step.
The shift from GPT-4 to GPT-4o marks a major advance in AI chatbot technology, bringing better multimodal capabilities, performance, and user experience. As OpenAI develops GPT-4.1 and GPT-5, it continues pushing AI innovation forward while meeting diverse user needs.
1.3.7 🖼️ DALL·E Models (2021–2023)
- DALL·E 1: Released in 2021, this pioneering model could generate abstract digital art from text descriptions. While its outputs were often surreal and less precise, it demonstrated the potential of AI image generation and laid the groundwork for future improvements.
- DALL·E 2: Launched in 2022, this version marked a significant advancement with photorealistic image generation capabilities. It introduced features like inpainting (editing specific parts of images) and outpainting (extending images beyond their original borders), while offering better control over artistic styles and composition.
- DALL·E 3: Released in 2023, this represents the current state-of-the-art in AI image generation. It excels at understanding complex prompts, maintaining consistency in details, and producing more accurate representations of human faces and hands. The model can handle nuanced artistic direction and generate images in specific art styles with remarkable precision.
DALL·E 3's integration with GPT-4 via ChatGPT has revolutionized the creative workflow. The AI can now interpret natural language descriptions more accurately, suggest improvements to prompts, and maintain artistic consistency across multiple generations. This makes it an invaluable tool for professional designers, content creators, and developers working on app-generated art, book illustrations, marketing materials, and creative prototyping. The model also includes built-in safety features and content filters to ensure responsible image generation.
1.3.8 🎙️ Whisper (2022)
Whisper, released in September 2022, represents a breakthrough in automatic speech recognition (ASR) technology. This open-source model can transcribe speech in multiple languages with remarkable accuracy, translate between languages, and generate subtitles automatically. What makes Whisper particularly impressive is its robust performance across diverse audio conditions - from clear studio recordings to noisy background environments.
The model comes in several sizes to accommodate different use cases:
- Tiny (39M parameters): Fastest but least accurate, ideal for real-time applications
- Base (74M parameters): Balanced performance for everyday use
- Small (244M parameters): Improved accuracy with reasonable speed
- Medium (769M parameters): High accuracy with moderate resource requirements
- Large (1.5B parameters): Maximum accuracy for professional applications
OpenAI has also made Whisper available through their API as whisper-1, offering developers a simple way to integrate speech recognition capabilities without managing the infrastructure. The API version is optimized for production use, providing consistent performance and reliability while handling various audio formats and languages.
1.3.9 🔎 Embeddings (2021–Present)
The text-embedding-ada-002 model represents a significant advancement in Natural Language Processing (NLP), becoming an industry standard for converting text into numerical vectors that capture semantic meaning. These vectors allow computers to understand and compare text based on its actual meaning rather than just matching keywords. The model excels at semantic comparisons, enabling developers to build sophisticated tools like:
- Custom search engines that understand context and user intent, delivering more relevant results than traditional keyword-based search
- Vector databases for RAG (Retrieval-Augmented Generation) that enhance AI responses by efficiently retrieving relevant information from large document collections
- Personalized recommendations that analyze user preferences and behavior patterns to suggest highly relevant content or products
Each embedding is a dense vector of 1,536 dimensions, providing a rich mathematical representation of text that captures nuanced relationships between words and concepts. This makes the model particularly effective for tasks requiring deep semantic understanding.
Example: Creating Embeddings
from openai import OpenAI
# Initialize the client
client = OpenAI()
# Create an embedding
response = client.embeddings.create(
model="text-embedding-ada-002",
input="How do I cancel my subscription?",
encoding_format="float" # Explicitly specify the encoding format
)
# Extract the embedding vector
embedding_vector = response.data[0].embedding
# Optional: Convert to numpy array for further processing
import numpy as np
embedding_array = np.array(embedding_vector)Here's a breakdown of what the code does:
- Setup and Initialization:
- Imports the OpenAI library
- Creates a client instance to interact with OpenAI's API
- Creating the Embedding:
- Uses the "text-embedding-ada-002" model, which is the standard model for converting text into numerical vectors
- Takes an example text input ("How do I cancel my subscription?")
- Specifies "float" as the encoding format for the output
- Handling the Result:
- Extracts the embedding vector from the response
- Optionally converts it to a numpy array for further data processing
The resulting embedding is a 1,536-dimensional vector that represents the semantic meaning of the input text, making it useful for tasks like semantic search and content recommendations
1.3.10 Looking Ahead
OpenAI continues to evolve rapidly, with new releases announced regularly. The platform's growth is particularly notable in three key areas:
- Multi-modal capabilities: Systems can now process and generate text, images, and audio simultaneously, enabling more natural and comprehensive AI interactions
- Memory features: AI models can maintain context across conversations and retain important information about user preferences and past interactions
- Tool integrations: Advanced features like code interpreters for executing and debugging code, web browsing for real-time information access, and API connections for integrating with external services have become standard offerings
This evolution represents a fundamental shift in AI application development. Developers are now creating sophisticated applications that can:
- See: Process and analyze visual information through image recognition and generation
- Hear: Convert speech to text and understand audio inputs with high accuracy
- Act: Make informed decisions and take actions based on complex reasoning and multiple data sources
All of these capabilities are underpinned by intelligent reasoning systems that can understand context, follow complex instructions, and adapt to user needs.