ChatGPT vs Claude vs Gemini: An Honest Comparison (2026)

These days you have so many options when it comes to which AI agent or AI provider you can use. If you're wanting to use this at your W2 job you may not have the latitude to pick which AI you use. If your employer already pays for an AI platform I recommend that you use it. If they don't, do your research and perhaps suggest one to them. This section will probably be the one that goes out of date. New products are being offered all of the time. We'll go over what's on the market, what they do well, what they do poorly, but the philosophy and strengths/weaknesses of each provider won't change much over time.

Google Gemini

Let's start with the big dog: Google's Gemini. Surprisingly, Google came late to the party. And there is a very reasonable explanation for this. Google had so much data, and data is the raw material that takes a basic large language model and turns it into a several billion dollar product. Data is the name of the game and Google has been the king of data for a long time in internet years. But Google created a market in which getting the answer to the user as quickly as possible was actually detrimental to their bottom line. Google makes money by having you look at search pages and then click on multiple links on that page. It's how they sell their ads and ad revenue is their number one revenue by far. If Google all of a sudden wanted to give you the exact answer to whatever you're Googling, instead of having you go to a page, maybe two, or three, or four, and read through all of those pages, now they don't get all of those clicks. And their ad revenue goes down dramatically. This is the simple reason why even though Google was ahead in machine learning, manpower, and data for training large language models on, they were actually the last of the big three to show up to the party.

This doesn't mean that Gemini doesn't have some advantages. And while this post isn't necessarily concerned with the future of AI, there is a reason to think that the chips that Google has started to produce themselves will allow them to scale at a rate that the other two big providers, that is Anthropic and OpenAI, simply won't be able to do in the future for the same cost. But let's talk about what Gemini does well. There are a few things, and they mostly involve ecosystem and multimodal inputs. If you use the Google ecosystem, like Google Sheets, Google Docs, Gmail, YouTube, Google Search, and Android, Gemini is natively wired into that ecosystem. So it's very seamless for you to get a Gemini agent to see both your Google Sheets and your Gmail at the same time. It's not that other AI providers can't do this, it's just a little more seamless.

Now there are some things that Gemini can do that others can't. If you take Gemini's image generation tool, Nano Banana, at the time of writing, Nano Banana is far and above the best image generator. If you want to generate mock images for a prototype of just about anything, which I highly recommend doing, Nano Banana is absolutely the best. But please do not put AI slop into the finished product. Nobody wants to see that. Gemini being wired into YouTube is also very helpful. Depending on the subscription level you have, you can drop YouTube links into Gemini and get transcripts or detailed information on a YouTube video, and the other providers simply can't do that yet. The Gemini Flash model is also very quick and is usually very cheap. So for certain applications, a Gemini Flash model agent can be a very economic and low latency choice. But that's mostly if you're trying to build your own agent or build an AI application. For the regular consumer, they're not really going to notice that. Also, in the past, Gemini used to have the longest context of at least one million tokens. That has changed recently, and they're not the only ones. But you may see in the future that Gemini handles more tokens better than the other models. Although there's always a caveat to that, as we've seen in the ChromaDB article about context rot. The long and short of it is that if you're plugged into Google's ecosystem, you need a quick agent or an agent that can handle videos, YouTube links as input, or you need to do high quality image generation, then Gemini is definitely your top pick. NotebookLM is another product tied into their ecosystem with Gemini being the main feature, which allows you to keep a large project with multiple files and file types like pictures, videos, PDFs, and text, and keep an agent in that context. Which some people find extremely useful. Gemini, however, is not the cheapest. It is not the most intelligent in my experience. And it is perhaps the weakest at coding tasks out of the big three frontier models. In day to day tasks though, you probably won't notice much difference.

OpenAI / ChatGPT

Now on to OpenAI and ChatGPT. If you're reading this, you've almost certainly heard of ChatGPT. ChatGPT has had an interesting history. The company OpenAI started as a nonprofit in Silicon Valley and for several iterations of ChatGPT they struggled to develop a product that could make any money. Their actual claim to fame in the beginning was creating a model that could play the video game Diablo II quite well. This helped give them some traction in the media, and eventually they developed ChatGPT 3. This was the first large language model that most developers that got into AI early started to use. It was actually the first one that I ever came into contact with. I remember being blown away at the things it was capable of doing and didn't quite understand right away what this sort of technology could be useful for. But I did immediately recognize that it was going to change the technology industry in which I worked. At the time I was trying to teach myself web development and design, which was quite difficult for me. And I used that very early version of ChatGPT to build HTML and CSS templates for me to get started. It did take a few more iterations for ChatGPT to become dominant in the LLM space. And what we call the first-mover advantage went to ChatGPT.

ChatGPT has several products that it is integrated with. There is ChatGPT in the web, which you may have used before, and it is a chat user interface. There's also ChatGPT Work, which is a desktop app that is very similar to the web UI, but it can control your computer to a degree. Something we call computer use for AI, which allows it to be more deeply integrated into what you do. And then there's also a coding application for ChatGPT called ChatGPT Codex, which is an analog for Claude Code, which we'll talk about later. In my opinion, ChatGPT is an all around solid provider. One of the things that you'll hear people talk about is tone. And that is something that is entirely subjective. Some people appreciate the tone of one model over another. And all large language models do have a different tone. Just like people do in the way that they write. I would say that even applies to the way that each model writes code. But if you use these models for writing tasks, that's something to be aware of. Don't just go for whatever is seemingly the latest and greatest. That's also a problem with how quickly new models are pushed out. In general, I would say there is a consensus that Claude models have a more natural and agreeable tone. That, however, changes from release to release, as a few of the most recent ones from Anthropic have not been well received, and I personally have not liked the change in tone from Opus 4.6 to 4.7 and 4.8. Again that is entirely subjective. So try all of them and see which one you like, especially if you're going to be doing a task that is summary, research, or writing intensive. With that being said, ChatGPT is a very strong product for just about anything that you want to do that's a core function of these large language models. And the prices are very similar to the others. One thing to note, however, is that OpenAI is often not the first one to the party as far as AI products go. Anthropic was much more deft and quick to deploy a very capable command line interface coding agent and desktop user interface application for their AI agent. But that doesn't mean that OpenAI's own offering of basically the same product isn't going to be better or worse for your use case. It's something that you'll have to experiment with.

Anthropic / Claude

Last and certainly not least of the big three is Anthropic and their Claude model. Claude is well known for a few things. One is its talent with writing tasks. It's also well known and has a good reputation for being clear when maybe you've got the wrong idea, i.e. it doesn't try to butter you up. It's also probably the best coding model, although that is also subjective and depends on how you code, what you code, and what language you're using, and as the models continue to progress, that is not going to be a hard and fast rule. In my own experience, I do prefer to use Claude for most tasks, and I work heavily in code development, but I have seen ChatGPT beat Claude on a few very specific tasks. I would, however, pick a Claude Opus model ninety-nine times out of a hundred. But again, your mileage may vary, and it's critical that you actually experiment with at least two models. Anthropic has Claude Code, which was the first very capable command line interface agent. It was so good that last year, around Thanksgiving, it sparked a revolution in the programming community, and that's when programming quickly became AI focused. And most programmers use some AI on a regular basis to generate code, at least for prototypes. Anthropic also developed Claude CoWork, a desktop app, although recently integrated into their web UI, that is similar to the ChatGPT Work desktop application. It allows the Claude model to see local files that you've given it permission to, and some limited computer use. In my opinion, you could start with Claude and you would probably be just fine depending on what you want to use it for. It's the one that I ended up using, although I was initially a ChatGPT and sometimes Gemini user. I am certainly not above switching to a new provider, and I do pay for a plan with Anthropic, but there are plenty of times when I try the other models and see how they attack a certain problem. And these can all change in the future as these labs release new models and new applications surrounding them. Interestingly enough, the team at Anthropic were largely an offshoot of employees that left OpenAI. So there is definitely some bad blood between the two companies, but there is also some good competition and they keep each other on their toes, they keep the models competitive, and the pricing.

Quick Comparison

	Gemini	ChatGPT	Claude
Best at	Google ecosystem, image gen, YouTube/video	All-around, broad plugin ecosystem	Writing, coding, directness
Weakest at	Coding	Sometimes late to new product categories	Smaller integration ecosystem
Standout product	Nano Banana, NotebookLM	Largest user base	Claude Code (first strong CLI agent)
Best for you if	You live in Google's ecosystem	You want a solid all-around tool	You write code or do language-heavy tasks

Pricing

All of the AI models tend to have a free tier which doesn't give you access to the strongest models or all of the applications that they have. Normally it's just the web chat UI and a very limited number of prompts. After that there's normally a step up that gets you access to most of what they have, but a limited number of input and output tokens. And then there's a higher tier for power users. Pricing changes frequently across all providers, so check each one's current pricing page rather than relying on numbers that might be outdated by the time you read this. Obviously videos and images are going to burn through your allowance much more quickly than text. And you should also look at which AI agent products you're wanting to use from one of these providers.

A Note on Chinese Models

There are also many very capable Chinese AI models and providers. These models came about generally after the infamous DeepSeek was created, which many of these models are made using China's standard copycat process of taking very high quality American goods or technologies and then reverse engineering them or simply stealing intellectual property to make them and do it much more cheaply. While it seems to me that the big three AI providers are most certainly using your data, if you're using the paid tier you have some agreement to privacy. And if you're using the API endpoint, your right to privacy might be even stronger, and the same goes for an enterprise level agreement. However, if you look at the end user legal agreement for these Chinese models, they tend to be much worse. And you are not only using their product, you are the product. That is to say, they are almost certainly using your data to train their future models on, or to sell your private data to data collectors.

I've tried many of the Chinese models myself on non-critical data, and some of them are quite capable and are much cheaper. I would say this generally only factors in if you are very dead set on using AI often and you have a very low budget, or if you're trying to create an AI application and you're worried about scaling a specific step in that AI application that might have high traffic. Even so, I would caution against using any of these Chinese models unless you're going to run them locally on your own machine.

The Challenge

Use an AI provider, one of the big three, that you haven't used before. Use the free tier and try a few different prompts. Try some difficult prompts and take a look at how well it performs for your use cases. Also take stock in how you feel about the tone of the response.

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Not sure which one is right for your project? Book a call and I'll help you figure it out.