Claude is generally stronger at long structured writing, code, careful reasoning and following detailed instructions, while ChatGPT leads on image generation, voice and the breadth of its plug-in ecosystem. Many Australian businesses use both, or run an AI agent that picks the right model for each task.
On this page
If you have to pick one and your team does knowledge work, code or document processing, start with Claude. If your team mostly produces creative content and images, or works in Microsoft Office or the GPT ecosystem, start with ChatGPT. Many teams run both.
Comparison by task
The table reflects how the two products typically behave at the time of writing. Both change quickly, so test them on your own work.
| Job to be done | ChatGPT (OpenAI's GPT models) | Claude (Anthropic's Sonnet and Opus models) |
|---|---|---|
| Long-form structured writing | Good. Tends to be more conversational. | Stronger. Better at keeping structure, voice and accuracy across thousands of words. |
| Code generation and review | Solid, particularly with popular languages such as Python and JavaScript. | Stronger, especially for editing existing codebases, careful refactors and following coding standards. |
| Following nuanced instructions | Good. Will sometimes skip parts of long prompts. | Stronger. Generally better at honouring constraints in long, detailed prompts. |
| Creative writing and brainstorming | Stronger. More inventive, more willing to play. | Good. Tends to be more conservative. |
| Image generation | Stronger, with image generation and editing built in. | Not a native feature: Claude reads images but doesn't generate them. |
| Voice mode | Stronger, with natural back-and-forth conversation. | Available but less polished. |
| Document and PDF analysis | Good. | Stronger with large documents, and several at once, with better recall. |
| Web search and current events | Built-in search with citations. | Built-in search with citations. Both work well. |
| Reasoning on hard problems | The reasoning models are very strong. | The latest Sonnet and Opus models are very strong, with a different style of thinking. |
| Business data handling | Business and Enterprise tiers: no training on your data, SOC 2, configurable retention. | Team and Enterprise tiers: no training on your data, SOC 2, configurable retention. |
| Integration ecosystem | Much broader: custom GPTs, apps and a large third-party network. | Smaller native ecosystem, but MCP (Model Context Protocol) is the open standard many agent builders have adopted. |
| Agentic work (code, tools, command line) | Capable code and tool features. | Claude Code is widely used for autonomous coding work on a developer's machine. |
| Australian data residency | Depends on the product tier and platform, and changes often. Check current options. | Depends on the product tier and cloud platform, and changes often. Check current options. |
Choosing by daily work
When Claude fits
- Writing or editing long structured documents (proposals, reports, specifications, contracts)
- Reading or summarising large documents, PDFs or codebases
- Writing or maintaining software
- Working on tasks where following detailed, nuanced instructions matters
- Building AI agents using MCP or command-line workflows
When ChatGPT fits
- Producing creative content, marketing copy or social posts
- Generating or editing images and visual content
- Talking rather than typing (driving, walking, dictating)
- Working inside Microsoft Office, where Copilot is built on OpenAI models
- Using the largest ecosystem of plug-ins and custom GPTs
When to run both
- A team doing a mix of all of the above
- Using the best model for each specific task
- Building an AI agent that routes each task to the right model (OpenClaw, n8n or custom code)
Data and security
Both Anthropic (Claude) and OpenAI (ChatGPT) offer business tiers with data-handling commitments: no training on your data, configurable retention, audit logging and SOC 2. Their free and consumer tiers may use your conversations for training unless you opt out, so check the settings before your team pastes in anything sensitive.
For Australian businesses with data-sovereignty concerns, a cleaner design is to use either model through its API while keeping documents and sensitive context on your own systems. That can be a custom AI agent, or a tool like OpenClaw that works this way by design. Files stay out of third-party apps while each task still uses the best model.
Whether processing can stay inside Australia depends on the product tier and the cloud platform you use, and the options change often. For most business use this isn't a blocker. For strictly regulated industries, check the current options carefully, or consider models running on site or open-weight models hosted on Australian infrastructure.
What Industrial AI uses
For the engineering work behind custom AI systems, Industrial AI mainly uses Claude: Claude Code for development and the Anthropic API in production. Claude is strong at following detailed instructions, working with code and handling structured workflows.
When a business wants its own AI agent, Industrial AI usually sets it up on OpenClaw, an open-source agent framework that runs on the customer's own devices. OpenClaw can call Claude, GPT, Gemini or open-weight models depending on the task, so you're not locked to one vendor and each job can go to the model that suits it.
Where ChatGPT is the better tool for a task, such as image generation, some creative work, or a team built around Microsoft Copilot, it is used instead.
Getting started
If you're exploring: sign up for both consumer subscriptions, give them the same five tasks from a typical day, and compare the results. At the time of writing each costs roughly $30 AUD a month. Cancel the one you use less.
If you're rolling out to a team: start with whichever ecosystem your existing software lives in. If your team is on Microsoft 365, Copilot is the easiest path. If you're flexible on tools, Claude's Team plan is where many knowledge-work teams land.
If you're building an AI agent or automation: don't pick a model first. Pick an agent framework that lets you swap models (OpenClaw, n8n or custom code), then choose the best model per task and change models as they improve. That avoids vendor lock-in.
If you're training your team: the hands-on ChatGPT workshop and Claude workshop run on your own tasks, and the AI voice agent shows what a model can do once it's working as an agent rather than a chat window.