ChatGPT, Gemini, Claude and Grok compared: which AI model fits your business?

All four providers keep reworking their models. What leads today can fall behind after the next update. On top of that, you are not just comparing a model but a package of model, features, plan and data protection rules. A model can shine at one task and stumble at the next.

That is why I describe characters instead of rankings. These are my assessments from practice, not measurement results. I do not quote benchmark figures, because they are often chosen by the provider and become outdated quickly. Check every assessment against your own tasks.

ChatGPT is where many people start with AI. OpenAI is turning it into a platform: text, files, images, voice and research in one interface, plus features where the AI completes several steps on its own. Which models you get depends on your plan. OpenAI's release notes, for example, state that a new top model first arrived only in paid plans.

Strengths: ChatGPT can do a great deal and knows almost every kind of task. Because so many people use it, you will find guides, templates and experience reports for almost any use case. That is valuable for getting a team started.

Weaknesses: The sheer number of features can be confusing, and plans and model choices are not always easy to understand. As with every language model, it sounds convincing even when it is wrong. Numbers, deadlines and legal statements need to be checked.

Data protection: For ChatGPT Business, Enterprise and the API, OpenAI says inputs and outputs are not used for training by default. Private accounts follow different rules. Check the privacy settings there before you enter business data.

Gemini is Google's AI assistant. According to Google, it processes text, images and audio. The real advantage appears when your business uses Google Workspace: Gemini is then available directly in Gmail, Docs, Sheets, Slides, Drive and Meet and works with content you already have access to. NotebookLM lets you load your own documents as a knowledge base. Since January 2026, Workspace users can also use such notebooks as a source in the Gemini app.

Strengths: If you already live in Gmail, calendar and Drive, you save detours. Research that uses your own documents and Google services is a clear plus.

Weaknesses: The benefit depends heavily on whether you use Google. If your business works with Microsoft or other systems, the main advantage disappears. With Gemini, the account type also matters a lot.

Data protection: In the private Gemini app, Google states that a subset of chats is reviewed by humans and those chats are kept for up to three years. With activity switched off, new chats are not used for training but are still stored briefly. For Google Workspace, Google's privacy hub states that content is not used for training outside your domain without permission. For business use, a Workspace account is therefore different from a private account.

Claude is Anthropic's model. According to Anthropic's documentation, the current models can read text and images and produce text, with a context window of up to one million tokens. Roughly speaking, very long documents fit into one conversation. Anthropic offers several sizes, from a fast model for simple high-volume tasks to a strong model for demanding work.

Strengths: I notice Claude positively with long documents, with writing in a factual tone and with calmly working through complex tasks. I appreciate that it tends to state uncertainty instead of papering over gaps. That is an experience, not a test result.

Weaknesses: According to the documentation, the models produce text. If you want images, video or voice output from a single provider, other providers often serve you better. And Claude is also wrong at times, sometimes with great confidence.

Data protection: Since autumn 2025, users of private plans (Free, Pro, Max) can decide whether their chats are used for training. Agreeing extends retention to up to five years, otherwise it stays at 30 days. Team, Enterprise and the API fall under the commercial terms and are excluded.

Grok comes from xAI and is closely tied to the platform X. It can draw on posts from X in its answers. If you want to know what is being discussed right now, you get a quick overview there. A second point is often overlooked, in my view: Grok has become really interesting for programming and on price.

Where Grok is really strong: Cursor, a widely used development environment with AI, lists the Grok models in its own documentation as Cursor models, jointly trained with xAI (named SpaceXAI there). They are included in the usage allowance of the paid Cursor plans. Models from Claude, GPT and Gemini are not part of that allowance there, but are billed at the providers' list prices. xAI itself also expressly recommends its current top model for code in its documentation. That matches my experience: Grok in Cursor is a really strong combination for me. There are also faster variants, which cost correspondingly more.

On price, according to the price lists of xAI and Cursor (as of 19 October 2026), Grok is among the cheaper top models of the four providers, especially for output. That is a price comparison, not a quality comparison. Whether the cheaper model is just as good for your tasks only a test with your own material will show. I do not quote concrete prices because they change often.

Where I look closely, as with any model: posts on X are not a verified body of knowledge, and rumours and exaggeration are part of it. The closeness to X is therefore both an advantage and a risk. I check contracts, calculations and customer communication from Grok just as I do from every other model before anything goes out.

Data protection: According to X's help page, training on personal data is switched off through a setting, which is not the default. Even then, feedback such as thumbs up or down can be used for training. In 2025, the Irish data protection authority opened an inquiry into whether X lawfully used posts of European users to train Grok. Please check the current status yourself. Check the privacy settings, also in Cursor, before you enter source code or business data.

All four make mistakes and occasionally invent details that sound plausible. A person checks what goes out. That is not an argument against AI, it is the rule of the game.

The plan decides data protection at least as much as the provider. Private free accounts and business plans differ in training, retention and contractual basis. For customer data, employee data and contracts, you belong in a plan with a contract, usually with a data processing agreement, and in clear team rules. Whether that is enough in your case is for a data protection adviser to decide.

And every provider changes plans, features and terms. Before you decide, look at the current pages I link below.

Take five real tasks from last week: a customer reply, a summary, a long report, a piece of research, a spreadsheet. Give the models under consideration the same tasks, as a test without confidential data. Then compare which answer you would have to rework the least.

Also ask: which systems do we already use, Google, Microsoft or something else? Which data is involved? Who on the team will use it, and who checks the results? Often the answer is one standard model for the team and a second for special cases. More accounts than that usually just create tool chaos.

If you program or have software built: explicitly include Grok in Cursor in your test. The price looks good, and you will see for yourself after a few tasks whether the quality is sufficient for your code.

Then local models are an option. They run on your own computer or server. They are not a replacement for everything the large cloud models can do, though. When this pays off and where the limits are, I describe in a separate article: local AI models, when they are worth it for your business and when not.