Short answer: Both are good enough. The decision doesn’t come down to the model, but to three things: what work your team actually hands off, how deep the tool reaches into your data, and which plan you book. If you work a lot with long documents and exact requirements, you’re better off with Claude. If you need image generation, voice features and the widest range of integrations, ChatGPT is the right call. And in both cases: the personal plan has no place in a business.
As of: August 2026. Prices and features in this market change fast; all figures were checked on the providers’ own sites on the day of publication.
Disclosure: I make my living from consulting and training around Claude. That’s exactly why this piece states up front when you should choose ChatGPT instead, and names those cases first.
The key points
- The gap between the models has narrowed: for standard tasks, both deliver usable results.
- The plan decides your data protection, not the vendor: personal plans train on your content by default with both vendors, business plans don’t.
- ChatGPT wins on ecosystem, image and voice features, and wherever staff already know the tool from personal use.
- Claude wins on long documents, on following instructions precisely, and on coding.
- The most expensive mistake isn’t picking the wrong tool, it’s picking none, running both in parallel, with no rules, on personal accounts.
How I compare
I don’t judge AI tools by benchmark scores, but by seven criteria drawn from consulting practice: real workload, context connectivity, data protection and contract terms, team rollout, total cost over twelve months, exit cost, and pace of development. Why these seven and not the leaderboards is explained in my evaluation framework. This comparison works through the seven points in order.
The plans at a glance
A note on the table: Anthropic quotes list prices in US dollars, OpenAI in euros for the EU view. I deliberately don’t convert. Exchange rates and taxes would create a false sense of precision that isn’t there.
| Plan | Claude | ChatGPT |
|---|---|---|
| Free | Free, $0 | Free, €0 |
| Personal entry | Pro, $17/mo. billed yearly · $20 monthly | Go €8 · Plus €23/mo. |
| Personal power user | Max, from $100/mo. | Pro, from €103/mo. |
| Business plan | Team, $20/seat billed yearly · $25 monthly | Business, €21/user billed yearly · €26 monthly |
| Minimum seats | 2 seats (up to 150) | 2 users |
| Large enterprise | Enterprise, from 150 seats | Enterprise, price on request |
So the business plans are practically neck and neck. If you’re hoping to make the call on seat price alone, you won’t find it here, and that’s good news, because it points you toward the questions that actually cost money.
The most important point: the plan decides your data protection
If you take only one sentence from this piece, take this one: with both vendors, content on personal plans is used for model training by default, with an opt-out. On business plans, there is no training by default.
Anthropic states an opt-out for Free, Pro and Max; for Team and Enterprise it states “no model training on your content by default.” OpenAI’s comparison table for personal plans reads “Content is used to train our models. Opt-out available,” while for Business it explicitly states: no use of your business data for training.
What that means in practice: the riskiest setup in a mid-sized business isn’t “we picked the wrong vendor.” It’s “three employees are using their personal free accounts for quotes, contracts and HR matters, and nobody knows.” You don’t fix this shadow use by choosing a vendor, you fix it with a paid business account for everyone who needs one, plus a policy that makes that clear.
Two details that tend to get lost in tenders: at Anthropic, the feedback button (thumbs up/down) sends the complete conversation, which is then stored, decoupled from the user ID, for up to five years; administrators can turn the feature off organization-wide. At OpenAI, data residency across ten regions is an Enterprise feature, not part of the Business plan. For EU customers, according to the footer of Anthropic’s own website, the contracting entity is Anthropic Ireland Limited.
This is a practical assessment, not legal advice; the binding review of your case belongs with your data protection officer. I go deeper in the GDPR comparison of the four major vendors.
When ChatGPT wins
When you need images, voice and breadth. Image generation, voice mode with video, a very large range of connectors and apps, that’s ChatGPT’s territory. If your marketing regularly produces images, or you want to seriously use dictation and voice features, that’s not a side note, it’s the main reason for the decision.
When your team already knows it. This is an underrated argument. If two-thirds of your staff already use ChatGPT personally, you save measurable training effort, and you bring exactly the shadow use that would otherwise remain your biggest data protection risk into a controlled setting. A tool people actually use beats a theoretically better one nobody opens.
When you need ready-made connections to standard software. The Business plan ships with connectors to Microsoft 365, Google Drive, Slack, GitHub, Linear and Figma. If that’s exactly your landscape and you want fast results, you save yourself integration work.
When you need data residency in a specific region and are willing to move to the Enterprise plan. Ten regions to choose from is a solid argument for regulated industries.
When Claude wins
With long documents and large volumes of text. Contracts, tenders, expert reports, series of meeting minutes, if your work consists of working through a lot of text and turning it into something precise, that’s Claude’s strength. It’s also the area where I see the clearest differences in projects.
When instructions need to be followed exactly. “Stick to this structure, use these terms, don’t invent anything.” In my experience, Claude follows instructions like that more reliably. For boilerplate text, standardized replies and anything that gets checked afterward, that saves rework.
For coding and automation. If your company develops software in-house or you’re automating workflows, this is a strong argument. How the tools stack up there I compare separately in the piece on coding agents.
When you want to build your own connections. The Model Context Protocol lets you connect line-of-business applications that have no ready-made connector. That’s the path by which a chat window becomes a tool that knows your systems.
The expensive mistake: both in parallel, with no rules
The most common situation I come across isn’t the wrong decision, it’s the avoided one. Two departments use different tools, part of the staff works with personal accounts, there’s no policy, and nobody can say which company data ended up where.
That doesn’t cost you in licenses, it costs you in three other places: double the training effort, double the data protection review, and a body of knowledge that can’t be shared, because one department’s templates don’t work in the other. Why I still don’t argue for a single monoculture tool, but for one standard tool plus a deliberately allowed second tool, is in the piece One AI tool, or several?
Three scenarios with a clear recommendation
Tax or law firm, 8 people
Recommendation: Claude Team. The work consists of long documents and exact wording, client data makes data protection the most important criterion, and nobody needs image generation. More important than the tool choice, though, is that all eight people get a business account and personal accounts are explicitly banned for client files.
Trade business with an office, 25 people, 5 of them office-based
Recommendation: ChatGPT Business for the five office seats. Quotes, emails, tender documents, correspondence with authorities, that’s standard work both tools handle well. Familiarity is the decisive factor: if office staff already know the tool from personal use, it’s productive in two weeks instead of two months. The 20 people on-site don’t need any access for now.
Agency, 15 people
Recommendation: both, but with a clear division of labor. This is the case where two tools genuinely pay off: ChatGPT for imagery, voice features and fast brainstorming, Claude for concepts, long copy and anything that ends up in front of the client. The precondition is a written rule for which tool does what, otherwise you pay twice and get one and a half tools’ worth.
What you need to settle regardless of the tool
- A one-page policy: what’s allowed in, what isn’t, which access is permitted. Nobody reads anything longer.
- Business accounts for everyone who needs one. Skimp here, and you push the problem into personal accounts you don’t control.
- A named person who answers questions. Without one, every rollout fizzles out after three weeks.
- Prompt hygiene: no complete personnel files, no credentials, no unredacted customer data, unless that’s been explicitly cleared.
- A filing system for what works. Good templates belong in your own archive, not in a chat window’s history.
Frequently asked questions
Is the free plan enough for a small business?
Yes, to try it out; no, for actual business use. On both vendors’ free plans, content is used for training by default. As soon as customer, HR or contract data is involved, you need a business plan, regardless of company size.
What does switching cost if we choose wrong?
The license itself is cancellable monthly, that’s not the problem. What’s expensive is the training and everything you’ve built yourselves: templates, policies, integrations. Which is why my advice is to keep everything you’ve written yourselves in your own archive; then switching costs a week of work instead of a project.
Is Claude really better at coding?
In my project practice, yes, clearly. But that’s only relevant if you actually develop software or automate workflows. For a business with no development work, this criterion simply isn’t a decision factor, and I wouldn’t sell it as one.
Can we sign a data processing agreement with the business plan?
Both vendors provide the corresponding contract documents for their business plans. Check in your specific case who your contracting party is, where the data is processed, and which subprocessors are involved, and have that countersigned by whoever is responsible for data protection at your organization.
How long will this comparison stay valid?
The prices and features, probably a few months; the structure of the decision, considerably longer. I re-check this piece every quarter and update the figures. If the date above is more than six months old, treat the numbers as a rough guide, not a basis for a decision.