Methodology — Claude vs ChatGPT on a $51,200 Vendor Decision

Same prompt. Same day. Unedited first responses. The full test setup, what each AI actually said, and how to replicate it on your own real decision.

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1. Why this test exists

Most Australian small-business owners are paying for both Claude and ChatGPT (around twenty dollars a month each) and defaulting to whichever they signed up for first. That's fine when the worst-case output is a slightly awkward marketing email. It's not fine when an AI is informing a hiring call, a supplier negotiation, or a capital-allocation decision.

The risk in business-AI use isn't the subscription cost. It's the cost of acting on bad output that the AI presented with complete confidence. This test was designed to surface that gap — head-to-head, on a real decision a real business owner is actually trying to make.

2. The scenario

Three suppliers. Total annual spend across the category: $51,200. The business is consolidating to reduce overhead and is evaluating a switch.

SupplierAnnual costPayment termsContract termsNotable
ALowest unit price30 days upfront12-month renewableCheapest on face value, but cash-flow hit in Q1
BMid-rangeNet-3012-month with 90-day exit clause, $4,800 penaltyStrongest SLA — but the exit penalty is the trap
CHighest costNet-60Month-to-month, no lock-inMost expensive, but maximum flexibility

The exit clause on Supplier B is the load-bearing detail. It's the kind of clause that gets buried at the bottom of page seven of a contract, never makes it into the comparison spreadsheet, and shows up as a $4,800 invoice after the switch.

3. The exact prompt

Identical prompt sent to both AIs. No tweaking between attempts. First response only — no cherry-picking.

You are a senior operations advisor helping a small business owner make a vendor consolidation decision.

SCENARIO
Total annual spend across category: $51,200
Number of current vendors: 3 (A, B, C)
Category: SaaS — operations + admin tooling
Business size: 12 staff, ~$2.4M annual revenue
Decision deadline: end of Q1

SUPPLIER DATA
[full data table as above, with payment terms, contract length, exit clauses, SLA notes]

YOUR TASK
1. Identify the lowest unit-cost supplier on face value.
2. Identify all material constraints I should consider beyond unit cost.
3. Recommend a consolidation strategy with a clear primary recommendation.
4. Label every assumption you're making about my business that could change the recommendation.
5. State the conditions under which you would revisit the recommendation.

The full prompt template (with placeholders for your own scenario) is available at /stacksensible/templates/business-decision-framework.

4. What each AI said

Claude — structured reasoning

Flagged the 90-day exit clause on Supplier B as a material constraint without being explicitly asked to prioritise it. Quoted (paraphrased): "any cost saving from consolidating to Supplier A is partially offset by the potential triggering of early exit conditions on the existing Supplier B agreement. Confirm this before committing."

Weighted qualitative factors explicitly — not just price. Considered SLA reliability, payment-term flexibility, Q4 volume risk.

Landed on a recommendation (Supplier A) with a clear confidence qualifier and the conditions that would change it.

ChatGPT — fast and wrong

Confident recommendation, well-formatted. Ignored the exit clause entirely. Not mentioned. Not weighted.

Cited an expected saving of 18%, described as "typical industry consolidation savings." That figure has no source. The number was invented and presented as context.

A business owner trusting this output would have initiated the switch, triggered the Supplier B termination, and received a four-thousand-eight-hundred-dollar fee they were not expecting.

5. The steelman follow-up — the single most useful prompt

After each AI gave its recommendation, the same follow-up was sent:

What's the strongest argument against your recommendation? Steelman the opposing view.
What would have to be true for that opposing view to win?

Claude — genuine counter-case

Argued that consolidating to a single vendor creates concentration risk — if that supplier has a service disruption in Q4, there's no fallback. Questioned its own volume assumption. Flagged that the cost-saving projection was sensitive to a payment-terms variable it had estimated rather than confirmed.

This is the move that converts an AI from autocomplete into a thinking partner.

ChatGPT — softened restatement

Response: "While the consolidation strategy has merit, some stakeholders may prefer to maintain supplier diversity."

That's not a counter-argument. That's a disclaimer. The difference is everything.

6. The verdict — match the tool to the decision weight

The point of the test isn't "Claude wins." It's: the same tool is wrong for different jobs, and most business owners use the same tool for everything.

The error isn't picking the wrong tool. The error is using one tool for both jobs.

7. How to replicate on your own decision

  1. Take any pending business decision that's sitting above $10,000 in value or that you can't easily reverse.
  2. Paste the full context into both Claude (claude.ai) and ChatGPT (chatgpt.com) using the prompt template as your structure.
  3. Get the recommendation from each. Don't read them yet — just collect.
  4. Send the steelman follow-up to both, in the same conversation.
  5. Compare what came back. Strong signals: specific risks named, specific assumptions labelled, conditions for revisiting stated. Weak signals: confident percentage figures without source, generic "consider both sides" framing.

8. Replication caveats

For developers If you're building decision-support into an agent workflow rather than chatting manually: Claude's API supports prompt caching for system prompts (~90% cost reduction on repeated calls) and Anthropic's prompt engineering guide is worth reading. For ChatGPT, OpenAI's prompt engineering guide covers the same ground.
General information disclaimer. This is research and methodology, not financial / legal / investment / tax advice. The vendor scenario is composite. Outputs from AI systems are not warranted to be accurate. Verify any recommendation against your specific business context and a licensed adviser before acting on it. Daniel Gosling, BlackPan Media, and StackSensible accept no liability for decisions made using this methodology or its outputs.