Study Smart with OpenAI Tools ChatGPT, DALL.E, SORA and many more: A Student Guide

Comparison of OpenAI Tools and Why They Matter for Students:

This comparative table provides a concise overview of OpenAI’s major products, their purpose, and their significance. It is designed as a quick reference for students who need structured information at a glance.

Tool / Model	Type / Category	Release Year	Key Feature / Purpose	Why Students Care
GPT-1	Language Model	2018	Learned from 7,000+ books; 117M parameters	First step toward AI writing like humans; historical foundation for research.
GPT-2	Language Model	2019	1.5B parameters; powerful text generation	Sparked debates on AI misuse; shows early AI writing potential.
GPT-3	Language Model	2020	175B parameters; multitasking (poems, code, logic)	Useful for writing, coding, and logical reasoning exercises.
GPT-4	Multimodal Model	2023	Handles text, images, and audio with system prompts	Great for assignments, reports, and mixed media projects.
GPT-5 (expected)	Advanced Model	2025 (rumored)	Long-term memory, workflow integration	Could help manage research projects and long-term study notes.
ChatGPT	Chatbot (Text AI)	2022	Conversational AI with context understanding	Helps answer academic questions, brainstorm, and solve problems.
ChatGPT Deep Research	Research Chatbot	2024–2025	Layered explanations, web search + citations	Ideal for academic research, essays, and sourcing references.
GPTBot	Data Collector Bot	2023	Crawls websites for training GPT models	Indirect importance—students learn about AI ethics & data sourcing.
DALL·E (1–3)	Image Generator	2021	Text-to-image using CLIP	Students can make diagrams, posters, or creative presentations.
OpenAI Codex	Code Generator	2021 (public in 2025)	Converts natural language into code; 12+ languages	Assists in learning programming, debugging, and coding homework.
OpenAI Five	Game AI (DOTA 2)	2019	Learned team strategy; beat world champions	Shows AI’s power in decision-making—case study in teamwork and logic.
Sora	Text-to-Video Generator	2024 (tests)	Creates realistic videos from text prompts	Students in media/film can visualize concepts without cameras.
Whisper	Speech-to-Text	2022	Accurate multilingual transcription	Converts lectures/interviews into notes, even with noise.
OpenAI Gym	Reinforcement Learning Platform	2016	Train AI through trial-and-error games	Useful for computer science students studying machine learning.

These comparisons make it clear that while each OpenAI product has a unique role, together they form a complete ecosystem—covering text, images, audio, video, coding, and even gaming. For students, this means a single platform to explore almost every frontier of artificial intelligence.

Related: Strange OpenAI Phenomenon

Now that we’ve seen how OpenAI’s products stand side by side, let’s begin with the most well-known one — ChatGPT.

 

ChatGPT: OpenAI’s Flagship AI Chatbot

It is the most notable and widely used product of OpenAI. Officially, it is Chat Generative Pre-Trained Transformer (ChatGPT) which means it generates answers with the data it has been trained and in a mood that suits your question.

Let’s Elaborate It:

1. Generative → It generates answers to the questions.

2. Pre-Trained → It has a lot of data fed into its system.

3. Transformer → It transforms its answers into every language and tone as per your questions.

What ChatGPT Does

It is an AI chatbot designed for text-based conversations, generating ideas and understanding daily-life problems. It is trained and fed with tons of data for the following reasons:

1. Grasping human questions.

2. Understanding the context.

3. Replying in the most efficient way possible.

This is the basic introduction to how ChatGPT works. Let’s move on to an interesting topic.

Has ChatGPT Passed the Turing Test?

The Turing Test is performed on machines to check whether their answers can confuse humans into thinking they are interacting with another human rather than a machine.

What We Know

1. According to stats online and some unofficial lab experiments, at least 50% of the people were confused by ChatGPT’s answers.

2. Some even wrote things like: “Are you even human? The amount of empathy you possess is insane.”

This confirms that if people are confused, the theory of Alan Turing has been successful somehow.

Official Position

OpenAI has not confirmed that ChatGPT passed the test. This is intentional to:

1. Maintain transparency and trust in AI.

2. Avoid marketing their AI tools as human equivalents.

3. Keep the focus on being confident in usefulness, not replacing humans.

ChatGPT Deep Research: Academic-Level Insights

If you are a student, researcher, writer, journalist, or blogger, you may need layered data on various topics. Layered data means explanations that go deeper within an explanation. That’s what ChatGPT Deep Research is built for.

Unique Features

1. Provides layered data (explanation within an explanation).

2. Cites references directly from trusted web sources.

3. Can search the web and fetch direct results, not just rely on pre-fed data.

Why ChatGPT Deep Research?

How to Use ChatGPT Deep Research:

1. Upgrade to ChatGPT Pro or Plus plan.

2. Use the GPT-4o model to access tools like Web and Python.

3. From the model selector, choose “Deep Research” and start exploring.

Related: GPT 5 Secrets No One has Talked About 

Other GPT Models

GPT-1: The Beginning of Generative Transformers

1. Released: 2018

2. Goal: Test if AI could write like humans by only reading text.

3. Key Features: Trained on 7,000+ books; 117M parameters.

4. Why It Mattered: Served as the foundation for later GPT models.

GPT-2: The Model Too Powerful to Release

1. Released: 2019

2. Goal: Large-scale text generation with minimal errors.

3. Key Features: 1.5B parameters; strong writing ability.

4. Why It Mattered: Its release was initially delayed due to concerns of misuse, which highlighted the ethical risks of AI.

GPT-3: The Multi-Tasking Performer

1. Released: 2020

2. Goal: Extend beyond simple text into poems, coding, and logic.

3. Key Features: 175B parameters; trained on diverse data including GitHub and Stack Overflow.

4. Why It Mattered: Proved GPT models could perform multiple tasks, making it a turning point in AI research.

GPT-4: The Multi-Modal Breakthrough

1. Released: 2023

2. Goal: Enable AI to process text, images, and audio together.

3. Key Features: Uses “system prompts” to guide behavior. Exact parameter count has not been disclosed by OpenAI, but industry estimates suggest 500B–1T parameters.

4. Why It Mattered: Marked a leap in multi-modal learning, useful for assignments, reports, and creative media.

GPT-5: The Next Leap in AI Evolution

1. Expected Release: 2025 (based on livestream announcements and industry reporting; no official confirmation from OpenAI at the time of writing).

2. Goal: Improve long-term memory and integrate tasks into seamless workflows.

3. Key Features: Official details are not released; rumors suggest enhanced memory and project management capabilities.

4. Why It Matters: If realized, it could serve as a comprehensive academic and research assistant.

GPTBot: The Data Collector Behind GPTs

It is developed by OpenAI but is not intelligent. GPTBot is basically a data collector for training GPT models. It crawls the web and goes through pages, checks what the page is about and collects the data.
It doesn’t analyze the data, just collects.

Sites can choose whether to allow or reject GPTBot. Many big websites like New York Times have banned their data to be used for training AI models.

DALL·E — AI That Paint Your Words:

DALL·E is OpenAI’s image generation model. While GPT models process and respond in text, DALL·E creates images from written prompts, combining creativity with logic.

How It Learns and Creates:

It uses Contrastive Language–Image Pre-training (CLIP) to connect words with pictures. Trained on millions of captioned images from the internet, it can generate realistic or imaginative visuals. OpenAI has not disclosed exact datasets due to copyright and licensing concerns. For instance, if a student types “a science lab floating in space,” DALL·E can generate a matching image.

OpenAI Codex: Natural Language to Code

Codex is an OpenAI model designed for programming. It can read plain English instructions and translate them into code, making it a bridge between natural language and programming languages. It knows 12 programming languages.

OpenAI Five: Gaming AI That Beat Champions

It is an AI tool developed specifically to play DOTA 2. Valve released the multiplayer online battle arena video game DOTA 2 in 2013. The purpose was to build team coordination, strategic planning, and complex decision-making — all by using AI.

This tool is most near to mimicking emotions.
They aimed at making AI capable of taking complex decisions that involve retreating or being aggressive in “pressure” of gaming moments.

Guess what?
OpenAI Five succeeded in the mission.

Training:

Its training was crazy. The model trained itself by non-stop playing real games on a number of high-performance computers daily.
The experience it gained in one day was equivalent to the experience a human would gain in 180 years.
It also mimicked human instinct in gaming by displaying very calculated risky movements and pure logical movements.

Fun Fact:

It shook the world by defeating the DOTA 2 world champions “Team OG”.
It is the most fascinating AI tool to me. Though it is not available for public use because of its extreme hardware requirements and game-specific design, it is available for research and refining.

SORA: From Text to Video

It is a tool that converts your text to video without any camera. It generates constant image frames and displays them as a video.

The word “Sora” is originally Japanese meaning “sky.”
The aim was to make imagination reach the sky by converting any prompt into cinematic, clear, realistic and high-definition video.

Training:

Its training was difficult because you have to train AI like a cinematographer.
It had to learn to imagine scenes, maintain camera angles, expressions, and natural motion and consistent frames.
Along with this, logic and cause-effect were also taught to it.
Before launching, Hollywood filmmakers have tested it to ensure that it is useful for creative videography.