I Asked ChatGPT and Cashowa the Same Money Questions — Here's What I Got
This felt like a useful experiment when I decided to do it, and the results turned out to be more revealing than I expected.
This felt like a useful experiment when I decided to do it, and the results turned out to be more revealing than I expected.
The premise was simple: take five real personal finance questions — the kind a person might actually ask when trying to make a financial decision — and put them to both ChatGPT and Cashowa. Not to shame either tool, but to understand what each one is actually good at and where the fundamental differences lie.
What I found is that the difference isn't really about intelligence. ChatGPT is a remarkably capable tool. The difference is about architecture — what the tool is built to do when numbers get involved.
Question one: "How much should I have in my emergency fund?"
ChatGPT's answer:
ChatGPT gave a confident, detailed answer. It explained the three-to-six-month rule, described the difference between needs and total expenses, noted that freelancers should aim higher, and mentioned that job stability matters. It was thorough, readable, and accurate as general guidance.
What it didn't do was give me a number based on my actual situation. The answer was education, not calculation. It couldn't be, because ChatGPT didn't know my monthly expenses, my income stability, or my employment situation. It answered the general question well.
Cashowa's answer:
Cashowa asked me to either upload my bank statement CSV or tell it my monthly essential expenses. Once I provided the data, it calculated: essential expenses multiplied by the coverage multiplier appropriate for my employment situation (in my case, 4 months for a stable salaried role with reasonable job market demand), plus a buffer for irregular emergencies. It showed me the formula and the inputs. It produced a specific dollar figure.
For a question like this, ChatGPT gives you the framework to think about the answer yourself. Cashowa gives you the answer derived from your actual numbers. Both are useful — but they're doing different things.
Question two: "What will my mortgage payment be on a $380,000 home with 10% down at 7% interest over 30 years?"
This is a math question with a specific, calculable right answer. The standard amortisation formula produces it deterministically: given these inputs, the payment is $2,532.66 per month.
ChatGPT's answer:
ChatGPT gave me a figure. It was close — within $20 of the correct answer — but it was wrong. I ran the formula separately to verify. ChatGPT didn't show any formula or working. It produced the number as if it had calculated it, but the number wasn't quite right.
When I asked it to show its working, it produced a plausible-looking calculation — but the intermediate steps didn't perfectly match the standard amortisation formula. The model was pattern-matching to what the answer should look like, not running the formula.
Cashowa's answer:
Cashowa called the mortgage calculation tool, plugged in the inputs, and returned $2,532.66. It showed me the amortisation formula with my values substituted. I could verify every step. The answer was correct.
What this tells you:
For specific mathematical calculations, the gap between "generating text that looks like a calculation" and "running the actual calculation" produces real errors. Usually small errors. Sometimes larger ones. And nothing in ChatGPT's presentation told me which type I'd received.
Question three: "Am I spending too much on food?"
This is a more complex question because the answer requires knowledge of my actual spending.
ChatGPT's answer:
ChatGPT gave general benchmarks: food spending for one person typically ranges from $200 to $600 a month depending on location, lifestyle, and whether you cook regularly. It explained that the USDA publishes food cost plans. It noted that the right percentage of income varies significantly. All accurate. None of it told me whether my food spending was too high.
Cashowa's answer:
I uploaded three months of bank statements as a CSV. Cashowa categorised all transactions, identified grocery and restaurant spending, calculated my monthly average across both categories, compared it to my take-home income as a percentage, and flagged that my restaurant and delivery spending had increased month over month in the last quarter. It told me my food category was running at 23% of take-home — meaningfully above the 15% benchmark for my income level — and showed me which merchants were the largest contributors.
What this tells you:
Some questions can only be answered by looking at your data. For questions about your actual financial behaviour, a general-purpose language model can give you benchmarks but not analysis. A tool connected to your real data can give you both.
Question four: "Should I pay off my car loan early or invest the extra money?"
This is a judgment question with a mathematical component: the comparison of guaranteed interest savings versus expected investment return.
ChatGPT's answer:
ChatGPT handled this well. It explained the comparison clearly: if your car loan rate is lower than your expected investment return, investing mathematically wins. It noted the psychological value of being debt-free. It explained that guaranteed interest savings are more certain than expected investment returns. It was accurate, balanced, and useful.
This is the category where general-purpose language models shine: conceptual questions where the value is in understanding a framework rather than in computing a personalised answer.
Cashowa's answer:
Cashowa asked for the loan rate, remaining balance, monthly payment, and investment context. It then ran the comparison with the actual numbers: the guaranteed return from early payoff versus the expected return from investing the same amount. It showed me the break-even point. It flagged that the car loan rate I'd entered (9.2%) was high enough that early payoff compared favourably to index fund investing in expected-value terms, particularly accounting for the certainty difference. It produced a recommendation grounded in my specific situation rather than a general framework.
What this tells you:
For judgment questions, ChatGPT gives you a reasoning framework. Cashowa gives you the reasoning applied to your numbers. Both are more useful than the other in different scenarios: if you want to understand the concept, ChatGPT's clear explanation is valuable. If you want an answer for your specific loan at your specific rate, Cashowa's computation is the relevant one.
Question five: "Can I afford to take three months off work?"
ChatGPT's answer:
ChatGPT gave me a helpful framework: calculate monthly essential expenses, multiply by three, add a buffer, and compare to current savings. It noted I'd need to account for health insurance if it comes from an employer, the impact on retirement savings, and potential tax implications. Thorough, accurate, good general guidance.
Cashowa's answer:
Cashowa pulled my average monthly essential expenses from my uploaded statements, checked my current stated savings balance, calculated the runway, and built a projection showing which month the savings would run out under a few different assumptions: maintaining current spending patterns, reducing to bare essentials, or getting some consulting income during the period. It flagged that my emergency fund would be depleted by month two under the current spending assumption, and produced a timeline with the variables visible. It also surfaced that cutting certain expenses would extend the runway by approximately six weeks.
What this tells you:
This is where the architectural difference produces the most practical value. A question this complex — with multiple variables, dependencies, and time-sensitivity — is genuinely hard to answer without real data. ChatGPT gives you the questions to ask yourself. Cashowa answers them.
The honest summary
ChatGPT is excellent for:
Understanding financial concepts and frameworks
Getting balanced perspectives on financial decisions
Explaining jargon or exploring general principles
Questions where the value is in understanding rather than in a personalised calculation
Cashowa is better suited for:
Questions that require your actual financial data
Specific mathematical calculations that need to be right
Analysing your real spending, income, or business performance
Building plans and projections from your numbers rather than general assumptions
They're not really in competition. A useful analogy: a financial textbook can teach you everything about how compound interest works, but it can't tell you how compound interest applies to your specific savings at your specific rate on your specific timeline. Both are valuable; they do different things.
The difference that matters for real financial decisions is the one question produces accurate, personalised, verifiable answers and the other produces accurate general guidance. For decisions that affect your actual money, the personalised calculation is the relevant output.
Worth noting: Cashowa's full tracking suite — spending reports, budgets, net worth tracker, savings goals — is free forever. The AI features (like the financial planner and business audit) run on a credit model, with 10 free credits every month and no card required to try. So the comparison above doesn't require a paid commitment to experience directly.
Frequently asked questions
Is ChatGPT always wrong about financial calculations?
No. ChatGPT gets many financial calculations right, particularly simple ones with well-established formulas. The problem is that there's no reliable indicator of when it's right versus when it's close-but-wrong — it presents both with equal confidence. For financial decisions, that uncertainty is a meaningful risk.
Can ChatGPT be made more reliable for finance with the right prompting?
Somewhat. Asking it to show its working, to double-check its calculation, or to state its formula before applying numbers can reduce but not eliminate errors. The fundamental issue is that the model generates text rather than running computation — better prompting improves the text, not the underlying process.
Does Cashowa use ChatGPT or the same technology underneath?
Cashowa uses large language models for the conversational layer — understanding questions, explaining results, answering follow-ups. The distinction is in what handles the numbers: Cashowa routes numerical calculations through a computation layer rather than leaving them to the language model's generation. The approach to ensuring numbers are right is architectural, not a matter of which underlying model is used.
If the calculation is what matters, couldn't I just use a financial calculator or spreadsheet?
You could — for calculations where you know which formula to use and you're comfortable setting up the inputs. The value of a conversational interface is that you can ask in plain English without knowing the formula, and the model figures out which calculation to run. It also handles messy or unstructured data (like a bank statement export) in a way a spreadsheet can't easily replicate.
Is this comparison fair to ChatGPT?
It's fair as a comparison of what each tool is designed to do. ChatGPT is a general-purpose conversational AI not specifically designed for financial calculation accuracy. Cashowa is a financial AI built specifically with computation accuracy as a design priority. The comparison isn't "ChatGPT is bad" — it's "these tools are designed for different things, and that matters for finance specifically."
Should I stop using ChatGPT for financial questions?
Not necessarily. Using it to understand financial concepts, think through frameworks, or explore the dimensions of a decision is genuinely useful. Just treat its numeric outputs with appropriate scepticism — verify specific calculations independently, and for consequential decisions, use a tool that can ground its answers in your actual data.