ChatGPT or Claude: judge the model by the task
Whether ChatGPT or Claude fits better shows only in a test on your own task, with scoring set in advance and the full cost of running it in production counted.
Whether ChatGPT or Claude fits a company better only shows on a recurring task of its own. Both models get the same inputs, and the scoring is fixed before the test. A ranking isn't enough for that. Benchmarks measure other people's tasks, and your own documents, formats and requirements aren't in them. On top of that, ChatGPT and Claude aren't fixed tools with fixed traits. The results depend on the specific model, on how you access it, on which tools it can use and on the context you give it.
Settle the access first
There are two routes. Employees work in a workspace hosted by the provider, and both providers offer business and enterprise plans for that. Or your own applications call the models through the API, built into workflows and automation. In a Microsoft setup, Copilot is another option. A business account is a company workspace, and its use falls under the company's rules.
So which access you test comes before anything else. A test in the chat window says little about how the same model behaves inside an application.
Before the test, data protection needs to be settled: a data processing agreement under the GDPR, where and for what purpose data is processed, and whether inputs are used for training. Security is part of it from the start, even for a test.
Make the task comparable
The order: pick the task, fix the scoring in advance, give both models the same inputs, check the results, assess the total effort. For text, accuracy, completeness and rework count. For data extraction, the format and whether every field is filled count as well. Hard cases belong in the test just as much as easy ones.
Without fixed criteria, all you have at the end is an impression, and that depends on who happened to run the test.
Count the whole effort
The effort of a model is made up of several items: tokens for context and output, extra tool calls and retries when a result falls short. With long documents, the context window decides how much gets processed at once and how often you have to split the input. Speed and upkeep in production count too. So a low list price doesn't make a solution cheaper to run. Prices and model limits change often, so check the current state with the provider before you decide.
At SUMAX every AI call is booked individually with its cost, by now tens of thousands every month. Only that booking shows what a task actually costs. The price per license tells you little about it.
Keep switching possible, then check quality again
Between our applications and the AI models sits our own layer. It lets us switch the model without rebuilding every application. A switch is still a change to the work itself, because output formats, quality and error behavior can change with the model.
So after every switch, the same test runs again, with the same inputs and the same scoring. That way the first comparison becomes the yardstick for every later switch.
A comparison that supports a decision
An example: both models have to pull tasks out of the same approved meeting notes. It is set in advance which fields are needed and what to do when nobody is named as owner. If an answer makes up an owner, it fails, even if the rest looks good.
Easy and hard cases both go through the test. The record holds the model, settings, date and the corrections needed. That makes the comparison repeatable and traceable later, even for someone who wasn't there for the first run.
The results go to people who keep working with them. Whoever passes on an AI result has to check it first. So the model that should win is the one whose output can be checked reliably with the least effort.
Both models get the same inputs, and the scoring is fixed before the test.