Stop Paying for the Newest AI Models. You Really Don't Need Them for Most Tasks

New AI models seem to come out almost every week, and this pace of releases doesn't seem likely to slow down anytime soon. Prior to the US government banning and then reinstating Fable 5, it was the newest flagship AI model around. Since then, however, Gemini 3.6 Flash, GPT-5.6 , Kimi K3, and Opus 5 have all debuted. And by the time you're reading this, some other model will likely claim its brief moment atop some benchmark or popularity list. But are new AI models actually worth using? The short answer is not really. I test every major new AI model, and the harsh reality is that they likely won't meaningfully improve your everyday interactions. Here's what you need to know, and when you might actually want to upgrade.

AI Models Are Getting Better at Coding, But Not Much Else

New AI models always offer loads of technical improvements over their predecessors. For example, Google's 3.6 Flash model is incredibly fast, and it can spin up AI agents to divvy up work during complex tasks. Meanwhile, GPT-5.6 is just as capable as (and much cheaper than) Fable 5, which was intelligent enough to catch mistakes that top models from just a few months ago missed. However, improvements are almost overwhelmingly related to coding. For example, with OpenAI's release of GPT-5.6 , benchmarks primarily highlight improved capabilities in coding and cybersecurity.

It's also important to keep in mind that AI model benchmarks don't necessarily translate to real-world performance, even with coding. Besides, the overwhelming majority of people don't code, let alone dabble in vibe coding . Most people, if they use AI at all, rely on AI chatbots to answer questions, do research, or search the web. If you don't care about how cleanly an AI can write lines of code, the minimal difference between the best models of today and those from a year or even two prior might surprise you.

For Most Everyday Questions, Older Models Still Hold Up

Over a year ago, before GPT-5 launched, I built a PC. During that process, I used GPT-4o for quick answers to a ton of different questions. I asked questions such as whether MSI Afterburner was still the best software for GPU overclocking, what to keep in mind when preparing parts for a water-cooling loop for assembly, and what was the most effective way to clean a PC fan, among other things. 

I asked GPT-5.5 Instant the same questions, and I got the same answers: Yes, Afterburner is still the de facto choice of overclocking software; one should clean watercooling components first with distilled water; and PC fans can be sorted with a can of compressed air, isopropyl alcohol, and a microfiber cloth. 

You can spend $10,000 on a gaming PC, but if you only use it to browse and stream, it won't feel much different than a Chromebook. The same goes for AI chatbots and models.

But what about deep research tasks? Looking back over my ChatGPT history, I found a deep research report I generated with GPT-4o about overclocking a Ryzen 7 9800X3D CPU, and I used the same prompt to generate a new report with GPT-5.5. The new report is more focused and works better as a rule-of-thumb guide, but the original included more details on stability testing and voltage tweaking, which are nice to have. Of course, neither is perfect; deep research reports are jumping off points, not definitive sources of truth. I find it difficult to definitively call one out as much better than the other.

This trend continues when I go even further back. For example, Claude still lets you use Opus 3, which came out in March of 2024—over two years ago at the time of writing. I provided the same math questions to both models in the form of a screenshot from a Harvard math class test exam . Opus 3 got five questions wrong, while Opus 4.8 at the highest intelligence settings got two questions wrong. Yes, that's an improvement, but once again, it's not a night-and-day difference.

All of the above experiences are anecdotal, but you can run the same tests for yourself, and I expect you will get similar results. When it comes to using an AI chatbot to discuss various topics, the experience doesn't change all that much with each new model release. Sure, like I saw with Opus 3 to Opus 4.8, big gaps in release dates can result in more substantial differences, but this isn't the case most of the time.

Paying for AI Models Is More About Access Than Version Number

It almost goes without saying that if a new model is available to you for free, you should use it to your heart's content. After all, you're only ever a new account away from more usage if you need it. This holds true for all the mainstream chatbots, including ChatGPT, Claude, and Gemini. 

However, that's not all you need to consider. ChatGPT and Claude, for example, don't make their complex reasoning models available for free. While the version number might not be that important, as discussed above, going from an everyday-use model (Sonnet, for example) to a complex reasoning model (Opus, for example) can make a major difference; complex reasoning models spend more time thinking about prompts.

The good news is that for casual queries that still require reasoning, such as a troublesome math problem, the difference between complex reasoning models is minimal. Whether it's ChatGPT, Claude, or Gemini, they can all tackle math problems and the like. You could sign up for a premium ChatGPT or Claude plan to use their complex reasoning models, which are more intelligent in many ways, but you can use Gemini's for free.

If you already pay for a premium chatbot subscription, it's important to approach your allotted usage efficiently. For example, while you can whack GPT-5.5's intelligence all the way up from Instant to Extra High, the latter sucks up usage much faster. And Extra High might be overkill, anyway. For example, I sent the same aforementioned math problems to GPT-5.5 Extra High, Medium (its second-lowest intelligence setting), and Instant. With the Extra High and Medium modes active, ChatGPT answered all the questions correctly; with the Instant option, it got just one wrong. The upshot is that you might be able to avoid burning usage allotments on higher intelligence settings that won't perform any better.

Most importantly, you shouldn't pay for a chatbot subscription just to use the latest model, unless you really need it. Chances are good that you just won't be able to make much use of that extra intelligence.

Where New Models Actually Matter: Image and Video Generation

Generating media with AI isn't the same as using it to answer questions or do research: New image and video generation models almost always outperform older models in meaningful ways. For example, Nano Banana ( a Technical Excellence award winner ) was a huge upgrade to Gemini's image generation capabilities, catapulting it to the front of the pack of AI image generators .

Luckily, AI image generation is usually free, albeit for a limited number of images, even with a top-tier model like Nano Banana Pro. However, best-in-class AI image generation models shine the most when generating photorealistic images, so you might not need to bother with them if you want to create something more abstract or artistic.

With AI video generation , you will notice a marked difference between top models, such as Google's Veo 3.1, and the rest of the competition, particularly with photorealistic videos. However, unlike with AI image generation, you usually have to pay for AI video generation, so you need to weigh how much you need the best quality versus what you're willing to spend. 

Don't Confuse Hype Cycles With Real-World Gains

It's always tempting to try out the latest and greatest edition of something. After all, don't you deserve the best? But don't let the marketing take you in.

You can spend $10,000 on a gaming PC, but if you only use it to browse and stream, it won't feel much different than a Chromebook. The same goes for AI chatbots and models. If you can get responses you're happy with from older or less intelligent models, spending money, usage credit, or both on more capable LLMs is useless.

I'm not saying to avoid the top models at all costs, but rather to be deliberate about which ones you use. After all, you can even make a model with average intelligence more useful through good prompt engineering .

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