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Claude vs ChatGPT: Differences, Plans, Testing

06 November 2026 · DigifyNow

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Claude from Anthropic and ChatGPT from OpenAI are chat apps that run on different language models. Which one fits you better comes down to four things: the model behind your plan, the built-in tools (web search, images, coding agent, connectors), the usage limits of the subscription and the data terms of the plan. Neither one wins outright. Test both on the same real tasks, grade the results blind, and use company data only in business plans or through the API.

What is the difference between Claude and ChatGPT?

Claude is Anthropic's AI assistant, and ChatGPT is OpenAI's. Under the hood both work the same way: a large language model generates a response one token at a time, based on your input and the conversation so far. The differences you notice day to day come from three layers. First, the model itself: Claude models at Anthropic, GPT models at OpenAI. Second, the tools the app puts around the model, such as web search, file analysis or image generation. Third, the rules of your plan: which models you can pick, how many messages you get and what happens to your data. That's why the same prompt can give you a different result on the free tier than on a paid plan. Often there's a different model or a smaller usage allowance behind it.

The amount of text a model can take into account at once is called the context window. It's measured in tokens, and its size depends on the model and sometimes on the plan. You'll find the current figures in the Claude documentation from Anthropic and in the OpenAI developer documentation. Don't rely on numbers from older comparison articles, because these values change with every model generation.

Claude vs ChatGPT in 2026: why comparisons go stale so fast

Search for "Claude vs ChatGPT" today and you'll find conflicting verdicts. One article in the German search results rates ChatGPT ahead for coding, while forum posts on the same results page report that many developers have switched to Claude Code. Both may well have been true, just at different points in time. Both vendors ship new models several times a year, swap out the default model in the app and change the limits. So a test result applies to a specific model version on a specific plan, not to the product name.

For every comparison you read, check three things: the date, the exact model version and the plan. If any one of them is missing, the comparison is a rough pointer at best. A single prompt isn't a test either, because language models produce different answers to the same input. If you also want to weigh Copilot or automation tools alongside these two, our article on AI tools for business compares them by data flow and data processing agreement.

Which AI is better, and for which task?

You can only answer that question task by task. The table shows what matters for typical tasks in small and mid-sized businesses and where tests often fall apart, no matter which of the two tools you use.

On that last row: both assistants can be connected to external systems via MCP. To see what that looks like in practice, check our guides on how to set up Claude MCP in desktop, web and code and on Meta Ads MCP setup in Claude and ChatGPT.

Claude vs ChatGPT pricing: what drives the cost

Both vendors bill in two ways. With a subscription, you pay a fixed amount per person per month and get a usage allowance in return. Once the allowance is used up, you wait until it resets or move to a higher plan. In the search results, users complain mostly about limits during heavy work with Claude Code, limits that only reset after a set time window. With the API, you pay per token; input and output are billed separately, and the price varies by model. Long documents in the context and agents that run in loops drive up usage, because every pass resends the conversation history so far.

Your actual running costs are therefore made up of several items: the number of seats on a team plan, the plan per person (individual subscription or a higher tier), token usage if you use the API, the integration with your systems and the maintenance afterwards. Current prices are on the vendors' pricing pages; they vary by country and sales tax. How subscriptions, tokens and agents add up in day-to-day operation is explained in our article on AI costs with subscriptions, tokens and agents.

Claude Pro vs ChatGPT Pro: same name, different tier

"Pro" means a different tier at each vendor. At Anthropic, Claude Pro is the first paid individual plan, with Claude Max above it. At OpenAI, ChatGPT Plus is the first paid individual plan, and ChatGPT Pro is the higher tier above it. A fair comparison therefore puts Claude Pro next to ChatGPT Plus and Claude Max next to ChatGPT Pro. Compare Claude Pro with ChatGPT Pro and you're comparing an entry-level tier with a tier built for very heavy use.

The higher tiers mainly bring larger usage allowances and, depending on timing, earlier access to new models or features. They make sense for people who work with the assistant for many hours every day, for example with a coding agent like Claude Code. What's included in which subscription is listed in the Claude Code documentation. For occasional use they aren't worth it. They're also the wrong route for team work: individual plans are personal accounts with no central management of users and permissions. That's what the Team and Business plans are for.

What shouldn't you share with ChatGPT or Claude?

Both assistants refuse certain requests, such as instructions that could be used to harm others. For businesses, the reverse question matters more: what should your employees never type in to begin with? On consumer plans, whether conversations are used for training depends on the account setting. For business plans and the API, both vendors state that they don't use inputs for training by default. Check the current terms yourself.

• Personal data of customers or employees in personal accounts: if a service provider processes such data on your behalf, the EU's GDPR requires a data processing agreement. The vendors offer one for their business plans, but not for personal accounts.
• Passwords, API keys and login credentials: they end up in the conversation history and stay stored there.
• Trade secrets such as cost calculations or unpublished proposals, as long as the plan question hasn't been settled.
• Decisions with legal effect without human review, such as terminations or contract clauses. Both models phrase false statements just as convincingly as true ones.

Whether your use meets the requirements of the GDPR depends on how you organize it. The plan alone doesn't settle that. On top of that comes the EU AI Act: under it, deployers of AI systems take measures to support the development of AI literacy among their staff. They don't have to guarantee any particular level of literacy. In everyday practice, that usually means a short usage policy and a briefing for everyone who works with the assistants. The texts are available as the GDPR in the Official Journal of the EU and the AI Act in the Official Journal of the EU.

How to compare Claude and ChatGPT in your own business

The only reliable comparison is one built on your own tasks. Here's how to go about it:

1. Collect tasks: pick a handful of real tasks that come up regularly, such as drafting a proposal, summarizing an RFP or answering a customer complaint.
2. Clean the data: replace names, addresses and prices with placeholders as long as you're working with personal test accounts.
3. Level the playing field: use the same prompt, the same files and comparable plans, meaning Claude Pro next to ChatGPT Plus, and run the tests on the same day.
4. Note the version: record which model was selected in the app. Without that, you can't repeat the test later.
5. Grade blind: the person who will later use the output grades the answers without knowing which tool produced them. The criteria are factual accuracy, rework time in minutes and tone.
6. Test revision rounds: ask for two changes. A tool that nails the first answer but ignores corrections costs you more time day to day.
7. Check limits under real load: work normally with both for a week. Only then will you know whether the allowance is enough.
8. Decide per task: using both can be the right call if their strengths split across different tasks. But then you also need two contracts and two sets of rules.

Where these tests often fail in practice: only one person tests, the one who's already enthusiastic, and nobody else ever gets onboarded. Training on working with AI assistants for the whole team makes the results comparable, because everyone works from the same prompting basics.

When outside help is worth it

For individual employees who write copy or summarize documents, a business plan with a clear usage policy is enough, and you usually don't need outside help for that. It's a different story when the assistant needs to access your own data, such as your CRM, document storage or ad accounts, when many employees need to be onboarded, or when a chat is supposed to become an automated workflow. DigifyNow sets up AI assistants for your business, connects them to your systems and trains your employees. For a first conversation, have the following ready: the tasks from your test, the systems involved, the type of data and the number of users. With a free, no-obligation assessment of your project, we'll work out whether an integration is worth it or whether the standard plan is enough.

Request a free assessment of your project