ChatGPT, Claude or Gemini — how to match the model to the task in your business
ChatGPT, Claude or Gemini? For a business, it helps to attach that question to a specific task. Will the model extract details from enquiries, draft replies or help review documents? That determines what you need to compare and how to recognise a useful result.
When choosing a chat tool for everyday work, the application matters too: file access, collaboration and whether the team can use it comfortably. For an API automation, you test a particular model version, instruction and set of inputs. Record those details so you can reproduce the comparison later.
Start with several kinds of examples
For an initial comparison, you could collect 20–30 tasks from everyday work. This is a sample for finding differences, not sufficient evidence that an entire deployment is reliable. Include ordinary cases, incomplete ones and some that take a person a moment to work through.
If the model will read quote requests, prepare a short email with all the details, a longer thread containing an order change and a message that omits the quantity. For each example, record the expected output: product name, number of units, deadline and any missing information.
Decide in advance whether the model should leave a field blank or ask for clarification. Otherwise, you might reward an answer that looks complete but contains a guessed number.
Testing on tasks that reflect actual use is also recommended in OpenAI’s evaluation documentation. Combine the results with an assessment by someone who knows the work.
Compare answers without the model names
Give several selected models the same examples and instructions. Initially, this makes differences between the outputs easier to identify. Record the model versions, settings and test date.
Then ask the person who currently does the task to assess the answers. Hiding the model name helps them focus on what they received. For quote requests, a simple breakdown is useful:
| What to check | Example error |
|---|---|
| Agreement with the email | The model changed the quantity or deadline. |
| Missing information | It added a measurement the customer never supplied. |
| Usability | The data is correct but needs to be copied manually into the right fields. |
| Correction time | Reviewing the draft requires reading the whole email thread again. |
Customer-facing text also needs a tone check. An answer can be factually correct while making commitments the company does not want to make, or sounding too formal. A few approved writing examples in the instructions can help.
After the initial comparison, you can refine the instructions for promising models. Keep additional examples aside for the final check, though. If you adjust the prompt only against the same emails, you still do not know how it will handle new ones.
Check the cost of the whole task and the response time
A model’s price matters alongside how much data it receives and how often it is called. A cheap model needing frequent retries or manual corrections can work out worse than a more expensive model that handles the task better. The post on AI implementation costs explains how to account for those expenses.
Check how long the user waits too. A few extra seconds may not matter for a report prepared overnight. For a suggestion needed during a customer call, the delay will be more noticeable.
Record errors and failed attempts as well as successful answers. If the tool needs to handle many tasks at once, it also needs a test under that load and a check of the selected API’s limits.
Establish what data you can send
Before using company materials, check the terms for the specific service and plan. Use of data for training, retention and processing location are separate questions. Data not being used for training does not automatically mean it is never stored. For example, OpenAI’s API data documentation distinguishes training, logs and the state retained by individual features.
In my projects, I choose the provider and configuration according to the data requirements. I do not assume one processing setup will suit every implementation. If a test only needs to recognise the type of enquiry, check whether it can be run with names and contact details removed.
What to do with the comparison
Choose the option that meets the task’s requirements and fits the cost of the whole process. Where results are similar, ease of maintenance, available integrations and data handling terms can help decide.
When changing models, return to the saved examples. The same instruction may produce different answers, so quality and integration behaviour need checking again. The test set will also help after a prompt change or the addition of a new document type.