OpenAI's changelog records the release of GPT‑6.1 Sol on 29 September 2026. The steady succession of releases interests me as a developer, but it also raises a practical question once again: what should be tested before a new model is brought into an Arabic workflow? To my mind, the answer starts with a sample that represents the actual task, followed by a comparison whose results can be explained.
A small set that resembles real work
I suggest preparing test files that are cleared for use, including an Arabic letter, a table, a scanned document, and attachments that conflict on a specific piece of information. Unnecessary personal data is removed, and the reference answers are written before the comparison is run. Having a reference guards against being impressed by confident-sounding wording that misses a key piece of information.
Easy files alone are not enough. It helps to include a question the sources do not answer, a name that resembles another name, and two dates that refer to different events. These cases test the model's ability to keep facts apart, and to state that information is missing rather than completing the text with a guess that merely sounds linguistically right.
Acceptance criteria follow the task
If the task is data extraction, I look at whether the fields are correct and the numbers are preserved exactly as given. If it is preparing a summary, I check whether exceptions, decisions and responsibilities have been kept. Code generation, for its part, needs appropriate execution and testing, because a good explanation does not prove that the program is correct.
- Accuracy of the output and its link to the attached source.
- Correct handling of Arabic names, numbers and terminology.
- Ability to state that the evidence is insufficient.
- Execution time, cost and human review time.
When comparing, I recommend running the important cases more than once with the same settings. I also record the model name, its version, the test date and how the files were prepared. Without this information it is hard to explain later why a result differed, or to tell whether an improvement came from the model or from a change in the data.
A choice that can be reviewed
One model may suit quick drafts while another is better for complex analysis. That is why I prefer to link the choice to the type of task and the level of review required. I am not looking for the “best model” in absolute terms, but for the most suitable one for each task, under clear review conditions.
In professional applications, I find the best decision is one that can be explained: here is the set of cases, here are the errors that appeared, here is their impact, and here are the conditions of use. The latest announcement gives us a new candidate to test, while local evidence determines how well it fits the service.