ChatGPT 3 vs ChatGPT 4: What’s The Difference?

When ChatGPT 4 launched, the internet went wild. Everyone said it was a massive upgrade. But if you have been using ChatGPT 3 or 3.5 for your content, your email marketing, or your niche research, you probably had one question: is it actually that much better, or is this just hype? In this breakdown, Alston Godbolt ran a direct, side-by-side comparison of both models using the exact same prompts across six different tasks to find out what is real and what is noise.

The short answer is yes, there is a real difference. But the more useful answer is knowing where that difference shows up and where both models hit the same wall. If you are trying to build a blog, write email sequences, or research niche ideas for an online business, the comparison below will save you time and help you use these tools more effectively.

What You’ll Walk Out With

A clear picture of the raw size difference between ChatGPT 3 and ChatGPT 4 and what it means in practice
Real side-by-side test results from six different task types
Which model does a better job surfacing niche blog topics with less competition
Which model writes stronger, more emotionally engaging email copy
The shared knowledge cutoff limitation and how it affects your research workflow
Alston’s core rule: use AI as a guide, not a ghostwriter
Which model generates better lead magnet titles you can actually use on a landing page
A free tool at finder.platformproof.com to match you to the income method most likely to work with your skills right now

The Model Behind the Upgrade: 175 Billion vs 1 Trillion Parameters

Before getting into the actual tests, there is one number worth understanding. ChatGPT 3 was trained on 175 billion parameters. ChatGPT 4 was trained on one trillion parameters. If that scale is hard to picture, consider this: GPT-4 has roughly six times more parameters than GPT-3. That is not a minor patch or a version bump. That is a completely different foundation the model is built on.

Parameters in a language model are roughly the learned connections the model uses to generate responses. More parameters generally mean the model can hold more nuance, produce more detailed answers, and handle more complex requests without losing context. That said, raw size is not everything. The quality of the training data, the fine-tuning process, and the instructions the model was optimized against all play a role in what you actually see in the outputs. So the only way to know whether the size difference matters is to look at what each model actually produces when given identical prompts.

The six tests below answer that question with real examples.

Test 1: Finding the Best Fishing Spots in Wisconsin

The first prompt was: “Assume our target audience are fishermen. Where is the best place to catch fish in Wisconsin?”

ChatGPT 3 opened with a disclaimer. It said, as an AI language model, it does not have access to real-time fishing data and locations. Then it gave a list of popular spots: Lake Winnebago, Green Bay, Door County, and the Wisconsin River. The information was accurate and a reasonable starting point, but the preface about not having real-time data added friction. If you were trying to use that output directly in a blog post, you would need to cut that disclaimer before the content would read naturally.

ChatGPT 4 skipped the disclaimer entirely and went straight to answering. It spent more time on the response, went into greater depth, and specifically mentioned Sturgeon Bay as one of the best spots in Wisconsin for catching smallmouth bass. That level of specificity is exactly what makes content feel researched rather than generic. If you are writing a fishing blog about Wisconsin, the GPT-4 answer gives you more to work with directly and requires less editing before it is useful.

The difference here was not dramatic, but it was consistent. GPT-4 answered more directly and gave more specific information without being asked to go deeper.

Test 2: New Sports Niche Blog Ideas

The second prompt asked both models to suggest the newest sports someone could start a blog about. The goal was to surface topics that are growing but not yet saturated, which is one of the most reliable strategies for building a content business when you are starting from scratch with no existing audience.

ChatGPT 3 responded with: Esports, Ultimate Frisbee, parkour, obstacle course racing, CrossFit, and pickleball. It prefaced the answer with a note that it does not have access to real-time data. These are reasonable topics, but most of them are already well-established as content niches. A pickleball blog started today would be competing against hundreds of sites that have been publishing for years, with audiences already built and domain authority already accumulated.

ChatGPT 4 skipped the disclaimer and went straight to the answer. Its list included drone racing, fin swimming, padball, and foot golf. Fin swimming was new to Alston. Padball was unfamiliar enough that even the pronunciation was not immediately clear. That is actually a useful signal for a niche. When a knowledgeable person researching a topic does not immediately recognize the name, there is likely far less existing content and more room to build early authority without going up against established players.

For anyone trying to pick a niche where they can rank and grow faster, GPT-4 gives a meaningful edge in ideation. It surfaced ideas that GPT-3 simply did not include in its output.

Test 3: Writing a YouTube Script for PS5 Games for Beginners

The third prompt was: “Create a YouTube video script for the best PlayStation 5 video games for beginners.”

ChatGPT 3 returned an outline. The games it suggested were Astros Playroom, Spider-Man, Sackboy, Demon Souls, and Ratchet and Clank. A couple of those picks raised a flag immediately. Demon Souls is widely considered one of the most punishing games in the PlayStation library, built around the idea that failure is constant and expected. That is not a beginner-friendly recommendation. Beyond the game selection, the output was just an outline. Someone would still need to write the full script from scratch before recording anything.

ChatGPT 4 wrote an actual script. It generated a title first: Top Five PlayStation 5 Games for Beginners: Dive Into Gaming With Ease. That is a title you could use on a video without changing a word. The script opened with “Hey everyone, welcome back to the channel” and moved through each game with actual script language, including notes for background music and scene transitions. It was not just a list of games with short descriptions. It was a structured draft of what someone would say on camera.

Alston made an important point during this test that applies to everything AI produces. ChatGPT should not be your sole creative writer. The reason is straightforward: people buy from people. They follow creators, not content machines. If your YouTube channel or blog sounds like it was written entirely by software, your audience will notice that disconnect and your retention will suffer. Use the script as a guide and a starting structure. Go in afterward and add your own voice, your own experience with the games, and your own take on what makes something good for a beginner. That is what builds an audience that actually comes back.

Test 4: A Three-Day Email Sequence for Men Over 50 Who Want to Lose Weight

This was the most detailed prompt of the session. The full prompt was: “Write a three-day email sequence recommending products that can help with weight loss. My target audience is men over 50 who are 30 pounds overweight. They want to lose weight without going to the gym. They have money to buy things, but only if it is necessary.”

ChatGPT 3 returned a three-day sequence with these subject lines: “Welcome to Our Weight Loss Journey,” “Tips for Losing Weight Without Going to the Gym,” and “Our Final Recommendation for Weight Loss Products.” The structure followed the prompt correctly. But the opening line of the first email read: “We’re excited to help you achieve your goals and improve your health.” That is generic copy. It does not speak to the specific frustration of a man in his 50s who has tried to lose weight before. It would not stand out in a crowded inbox and would not drive strong click rates.

ChatGPT 4 opened very differently. The first email asked: “Are you tired of carrying around those extra 30 pounds? Ready to feel healthier, lighter, and more confident without stepping foot inside a gym?” That shift in language is the difference between copy that gets deleted and copy that gets read. The subject line for day two read: “Boost Your Weight Loss With This Secret Weapon.” That kind of subject line creates curiosity without being misleading. It recommended adjustable dumbbells as a specific product a man over 50 could use at home, and it included emojis throughout the sequence that were ready to paste directly into an email marketing platform without additional formatting work.

The quality difference here was the clearest of all six tests. For anyone running affiliate marketing campaigns or building an email list for a fitness-related product, GPT-4 produces copy that is meaningfully closer to ready-to-use. GPT-3 gives you a structure to fill in. GPT-4 gives you a draft to edit.

Not sure which online income method actually fits your skills and schedule?

The free quiz at finder.platformproof.com takes about two minutes and matches you to the specific method most likely to work for where you are right now.

Test 5: The Knowledge Cutoff Test

The fifth test was designed to expose a known limitation in both models. The question was simple: “Who won the 2022 World Series?”

ChatGPT 3 said it cannot predict future events such as the winner of the 2022 World Series. It stated its knowledge cutoff date as September 2021, which meant anything that happened after that date was outside its training data. At the time of recording, the 2022 World Series had already been played and concluded, but neither model had access to that result because it fell after the training cutoff.

ChatGPT 4 gave a similar response. It said it could not provide information on the 2022 World Series winner because its knowledge only went up to September 2021. What GPT-4 added was a redirect. It told the user to consult a reliable and up-to-date source, like a sports news website or a search engine. That small addition is actually useful. Rather than just saying it does not know, it pointed toward what to do next.

The takeaway is direct. Neither model is a substitute for current research. If you are writing content that references events, products, prices, or trends from the past year or two, you need to verify that information from a live source. Use AI to draft structure, strengthen your writing, and generate ideas. Do the factual verification yourself before anything goes live.

Test 6: Lead Magnet Ideas for the Same Target Audience

The final test asked both models to generate lead magnet ideas for the same target audience used in the email sequence test: men over 50 who want to lose weight without going to the gym and are willing to spend money if necessary.

ChatGPT 3 returned this list: a seven-day meal plan, a home workout guide, a healthy recipe ebook, a fitness tracker app, exclusive discounts, and an online community. Each of those is a valid concept. The problem is that they are presented as concepts, not as finished ideas. A “healthy recipe ebook” is a category. It is not something a reader would see described on a landing page and immediately feel compelled to download.

ChatGPT 4 gave those same ideas much more specificity. Instead of “no-gym workout guide,” it returned the title The No-Gym Weight Loss Guide for Men Over 50. Instead of “meal plan ebook,” it returned The 50 Plus Men’s Guide to Healthy Eating: Meal Plans and Recipes. It also generated what looked like an additional email sequence outline without being asked, which showed the model anticipating the next logical step in a content funnel without prompting.

Titled lead magnets are directly usable. You can hand the title to a designer, upload the finished guide to a landing page, and use the exact words in your opt-in copy. A concept requires an extra step of naming and positioning it before it becomes useful. GPT-4 skipped that step automatically.

Honest Drawbacks of Both Models

ChatGPT 4 is clearly the better model across these tests. But several limitations apply to both versions that are worth being clear about before you build your entire content workflow around either one.

The knowledge cutoff is the biggest shared limitation. At the time of this comparison, both GPT-3 and GPT-4 had a training cutoff of September 2021. That means any events, products, tools, platforms, or trends that emerged after that date simply do not exist in their training data. If you ask about a product launched in 2022 or later, a recent platform update, or a current news story, you will either get a wrong answer or a polite refusal. Always cross-check time-sensitive facts from a current source before including them in published content.

GPT-4 has message limits. During the recording, Alston flagged that GPT-4 restricts the number of requests or messages you can send within a session. That means you need to be deliberate about how you prompt it. Front-load the critical details in your initial question. Provide the full context upfront rather than asking follow-up questions to refine the answer, because each follow-up costs one of your available requests.

Neither model fully replaces a human voice. GPT-4 writes better copy than GPT-3, but it still sounds like AI trying to sound like a person when you read it carefully. There is a particular rhythm and a set of phrases that show up repeatedly in AI-generated output. Experienced readers notice this. More importantly, AI-generated content lacks the personal credibility that builds an audience over time. The fishing spots example is useful information, but if you are running a fishing blog, your readers want to know that you have actually stood on the bank at Sturgeon Bay and landed a smallmouth bass there. That kind of credibility cannot be generated. It has to come from you.

The practical approach is to treat both models as research and drafting tools. Generate, review, edit, and humanize. Add specifics from your own experience. Replace anything that sounds generic with language that sounds like something you would actually say. That pass is where your brand voice gets built into something the model gave you as a starting point.

A Simple Framework for Choosing Between the Two Models

Based on the six tests, here is a practical framework for deciding which model to use depending on what you are actually trying to accomplish.

Use ChatGPT 3 or 3.5 when you need fast, general-purpose research or a rough starting point for content you plan to rewrite heavily. It handles straightforward prompts well, responds quickly, and works fine for brainstorming when you just need a list of options to react to. If you are working in high volume and the depth of each individual output matters less than the speed of generating rough drafts, GPT-3 is a practical choice that gets the job done without the message limits of GPT-4.

Use ChatGPT 4 when the quality of the output is the point. If you are writing a real email sequence for an actual subscriber list, GPT-4’s emotional targeting and specific language will produce something much closer to a finished draft. If you are researching niche topics and want to surface ideas that your competitors may not have considered, GPT-4 is more likely to give you those. If you need a working title for a lead magnet or a YouTube video, GPT-4 returns something directly usable rather than a concept you need to finish naming yourself.

The one thing that does not change between models is your role in the process. Generate the output. Read it through completely. Edit out anything that sounds generic or would not survive in your voice. Add the personal details and real experience that make the content worth reading. No output from either model should go live without that pass.

Find Your X

AI tools like ChatGPT 4 are genuinely useful for building an online income, but they are a tool set on top of a business model, not a business model on their own. Before you decide whether to build a blog, run an affiliate marketing funnel, grow a YouTube channel, or build an email list, the more important question is which of those methods actually fits your skills, your schedule, and where you are right now.

The free quiz at finder.platformproof.com takes about two minutes and matches you to the specific income method most likely to work for your situation. It is a faster way to answer “what should I actually be building?” than trying to test every option one at a time and hoping something sticks.

Frequently Asked Questions

What is the main difference between ChatGPT 3 and ChatGPT 4?

The most significant difference is model size and the depth of outputs it produces. ChatGPT 3 was trained on 175 billion parameters and ChatGPT 4 was trained on one trillion parameters. In practice, GPT-4 produces more detailed answers, writes stronger email copy, generates usable titles and full scripts rather than just outlines, and surfaces niche ideas that GPT-3 does not include. GPT-3 answers the question. GPT-4 answers the question and anticipates what you need next.

Is it worth upgrading from ChatGPT 3 to ChatGPT 4?

For most content creators and online business builders, yes. The quality difference is most visible when writing email copy, creating YouTube scripts, or generating lead magnet ideas. If your content needs to convert readers into subscribers or buyers, GPT-4 is significantly more effective at producing emotionally targeted language that feels specific rather than generic. The main trade-off is that GPT-4 had message limits at the time of this comparison, so you need to plan your prompts more carefully and front-load the key context in each request.

Does ChatGPT 4 have real-time internet access?

At the time of Alston’s comparison, neither GPT-3 nor GPT-4 had real-time internet access. Both models had a training knowledge cutoff of September 2021. When asked who won the 2022 World Series, both said they could not answer because the event fell after their cutoff date. If you are using these models for research on current events, new products, or recent trends, you need to verify that information from a current source before including it in published content.

Can ChatGPT write a complete email sequence I can use for affiliate marketing?

ChatGPT 4 can write a strong draft. In Alston’s test, it produced a three-day email sequence with emotionally targeted subject lines, specific product recommendations like adjustable dumbbells for home workouts, and direct copy that opened with “Are you tired of carrying around those extra 30 pounds?” rather than the flat “We’re excited to help you” that GPT-3 returned. That said, always read through the sequence and edit it to sound like you. Replace generic phrases and add personal credibility before you send anything to a real list.

Should I use ChatGPT as my main content writer?

No, and Alston was direct about this in the video. AI tools work best as a guide and starting point, not as your sole writer. People buy from people. They follow creators because of the personality and lived experience behind the content, not the content itself as a document. If your blog, YouTube channel, or email list sounds like it was written entirely by software, your audience will feel that disconnect. Use ChatGPT to generate outlines, draft copy, brainstorm angles, and produce first versions. Then go through the output and make it yours before it goes live.

What are lead magnets and how can ChatGPT help me create them?

A lead magnet is a free resource you offer in exchange for someone’s email address. Common examples include ebooks, meal plans, guides, checklists, and templates. ChatGPT 4 can help generate both the concept and a specific, usable title for a lead magnet. In Alston’s test, it returned “The No-Gym Weight Loss Guide for Men Over 50” and “The 50 Plus Men’s Guide to Healthy Eating: Meal Plans and Recipes” rather than generic category names like “meal plan ebook.” Those titles are ready to use on a landing page or in an opt-in form headline.

How do I find a content niche with less competition using ChatGPT?

Ask GPT-4 specifically for newer or emerging options in a topic category rather than the most popular choices. In Alston’s sports blog test, GPT-4 returned fin swimming, padball, drone racing, and foot golf, all of which have significantly less existing content than Esports or pickleball. As a general rule: the less familiar the topic is to you when you first read it, the more likely it has less competition online. After GPT-4 gives you a list, follow up by checking search volume and looking at how many established sites already exist in that niche before committing.

How do I get started using AI tools to make money online?

The first step is identifying which income method actually fits your skills and available time. AI tools can support blogging, affiliate marketing, email list building, YouTube scripting, and social media content, but each of those paths has different startup costs, time requirements, and skill demands. The free quiz at finder.platformproof.com helps you answer that question in about two minutes and points you toward the specific path most likely to work for your situation rather than leaving you to test everything and hope something sticks.

Sources

Alston Godbolt, “ChatGPT 3 vs ChatGPT 4: What’s The Difference?” – direct side-by-side comparison using identical prompts across six task categories, published on YouTube at https://youtu.be/HQXCQH2Az7I
ChatGPT 3 trained on 175 billion parameters; ChatGPT 4 trained on one trillion parameters, as discussed in the video
Knowledge cutoff for both models at time of recording: September 2021