Almost every business owner who starts taking AI seriously runs into the same question of whether to use Claude, ChatGPT or Gemini. Comparison articles online tend to be full of benchmark scores that are hard to relate to daily work, and they date quickly because all three providers release new models regularly.
Choosing an AI model for business works better when you use criteria that fit your own needs. This article sets out those criteria, gives a neutral overview of each provider, and explains how to test models on your company's own data before you decide.
Start with the task
The first question is what work you want AI to help with. Summarising long contracts at a notary's office, replying to customer chats for an online shop, extracting data from a distributor's invoices and writing code for an internal app are very different jobs. A model that leads in one may not be the best at another.
Write down two to five core tasks, each with sample input and a description of what a good result looks like. That list becomes the basis for testing later, and it is far more useful than any general ranking.
Keep in mind that every provider offers models in several sizes. Larger models are usually stronger at complex reasoning, while smaller ones are faster and cheaper. For simple tasks such as sorting emails, a small model is often enough.
Criteria for choosing an AI model for business
Indonesian language quality
All three models handle Indonesian, but you need to judge the quality in your own business context. Check whether the output sounds natural or translated, whether the model understands local terms such as PPN, BPJS, SPK or faktur pajak, and whether it can switch between a formal register for official letters and a relaxed one for WhatsApp replies. Test mixed Indonesian and English too, since that is how many teams actually write.
Cost
Through the API, cost is usually charged per token, with different rates for input and output. Prices also vary widely between model sizes. Estimate costs from your real volumes, such as documents per month and their average length. For per-user subscriptions, compare features and usage limits, because the price per seat does not tell the whole story.
Data terms
Read each service's data terms. Consumer accounts, business plans and the API often have different rules on retaining history and using data to train models. If you process personal data about customers or employees, review these terms with UU PDP in mind, including where the data is processed.
Integration
Think about where the AI will be used. If your team works in Google Workspace, Gemini is already part of that ecosystem. If your company runs on a particular cloud, check which models are available there. Claude is offered through the Anthropic API, Amazon Bedrock and Google Cloud Vertex AI. OpenAI models are offered through the OpenAI API and Microsoft Azure. Gemini is offered through Google AI Studio and Vertex AI. For connecting AI to internal systems, MCP (Model Context Protocol), an open standard introduced by Anthropic in 2024, is now supported by many AI clients, including Claude and ChatGPT.
A neutral overview of the three providers
Claude is built by Anthropic, ChatGPT by OpenAI and Gemini by Google. Each offers a chat app for individuals, plans for teams and enterprises, and an API for developers. All three can read long documents and images and write code, and their relative strengths shift from one release to the next.
So the differences that matter most to a company usually sit outside the model itself. The ecosystem you already use, the data terms, how billing works and the support available to your team often count for more than small gaps in answer quality.
Test on your own data
A simple test over one or two weeks is usually enough to make a sound decision.
- Prepare 20 to 50 real examples from your core tasks, with personal data masked.
- Decide how you will score the results, for example accuracy, completeness, tone and how much editing is needed.
- Run the same examples through each model with the same instructions.
- Ask the people who normally do the work to score the output, ideally without knowing which model produced what.
- Record response times and estimated cost per task as well.
- Repeat the test whenever a new model comes out or the task changes.
The results are often mixed. One model may suit document summaries best, while another is more economical for high-volume classification.
Be ready to switch
The AI model market moves fast, so avoid depending entirely on one provider from the start. Keep your instructions, test examples and validation rules outside any single platform. If you build an integration, design the application so the model can be swapped with a configuration change. Many companies end up using more than one model, each for the task it handles best.
Choosing a model per task
Averoa builds AI integrations with Claude, ChatGPT or Gemini, including choosing the right model for each task, testing on real data, and usage limits that keep API costs predictable. You can read more on our AI integration page.
