Claude vs ChatGPT for business: which one should your team actually use?

The short answer

If your team mostly writes, analyses documents, or works with code, start with Claude. If your team needs image generation, voice, or the widest possible range of third-party integrations, start with ChatGPT.

If you can afford both, run both. The overlap is large but the edges are genuinely different, and at roughly AUD $30 per person per month each the cost of choosing wrong is far higher than the cost of having both.

The model you pick matters less than whether your team opens it on a Tuesday afternoon when they are busy. Adoption beats capability, every time.

Where each one is genuinely stronger

These are the differences we notice in daily work rather than the ones that appear in marketing material. They are also the ones most likely to still be true in six months, because they follow from design choices rather than from a benchmark result.

What it actually costs an Australian business

Both charge around USD $20 per user per month for the paid individual tier, which lands near AUD $30 once the exchange rate and GST are accounted for. Team and business tiers cost more per seat and add admin controls, shared workspaces and a commitment that your data is not used to train the public models.

For a team of ten running both, budget roughly AUD $7,200 a year. Put that next to the cost of the time involved: at a fully loaded rate of $60 an hour, the subscription pays for itself if it saves each person two hours a month. In our experience it saves considerably more than that, and the businesses that fail to see a return are almost always the ones where nobody was ever shown how to use it.

The tier that matters commercially is the business one. On the individual consumer tiers, the default data settings are less favourable, and for client work that is not a trade-off worth making. We cover this in our privacy policy and it is worth checking what your own obligations are under the Australian Privacy Principles.

The mistake almost everyone makes

Choosing on benchmarks. Public benchmark leaderboards change every few months, the differences at the top are small, and none of them measure the thing that determines your return: whether a busy person reaches for the tool instead of doing the task the old way.

The second mistake is buying licences without building anything on top of them. A licence gives one person a better result on one task. It does not compound. What compounds is a system: a saved brief, a defined process, an agent that runs the same way every time. That is the difference between using AI and having AI, and it is the whole argument in our piece on AI marketing strategy.

How to decide in a week

If you would rather not spend the week, that is roughly what the first call with us covers. We will tell you which one fits your workflows, and we will tell you if the honest answer is that neither is your bottleneck.

• Pick the three tasks that actually eat your team's time. Not the interesting ones, the repetitive ones.
• Give two people a paid seat on each tool for a week and have them do those three tasks in both.
• Record only two things: how long it took, and whether the output needed rewriting before it could be used.
• Pick the winner per task, not overall. It is completely normal to land on different tools for different jobs.
• Then build a system around whichever won, because the tool is not the value. The repeatable process is.

What we actually run, and why

Claude does the heavy lifting: long-document analysis, the writing systems behind four client content engines, and the multi-agent runtimes we build. It follows a detailed brand brief more reliably, which matters enormously when output has to ship without a rewrite. That reliability is the entire argument in our guide to brand voice training.

ChatGPT covers image generation and quick exploratory research where breadth beats depth. We also keep it because a meaningful number of clients use it, and being fluent in the tool your client already has is worth something.

Neither is used for research where the source matters. For that we use Perplexity, because it cites as it goes.