Would not surprise me if an innatentive human student would answer that with the same code. After all school “trains” people to expect such challenges to be solveable. A more attenive human might say “we can’t know” or provide an upper limit to the number of potential blue balloons.
Or we can try to rewrite the challenge sentences with different wording. As long as the new sentences convey the same problem you would expect that a system who can “understand” them would generate the same or similar solution.
Curiously this kind of thought experiment also shows a weakness of the Turing-test as originally formulated. A machine correctly solving these word puzzle variations could “prove” that it “understands” the sentences, but it would also reveal that it is not a human. Since i would expect a real human to protest against the inanity of the challenges quite fast. ;)
Given the question: "Jane has 9 balloons. 6 are green and the rest are blue. How many balloons are blue?" The model outputs: "jane_balloons = 9; green_balloons = 6; blue_balloons = jane_balloons - green_balloons; print(blue_balloons)"
That seems like a good justification of a (very simple) step-by-step reasoning process!