Claude Large Language Models

Model Evaluation Prompts: 6 for Deciding Whether an Upgrade Is Worth It

Fable 5.1 reports roughly double its predecessor on a science benchmark at twice the price of Opus 5. A benchmark that doubles and a workload that does not improve is a common outcome, and vendor numbers cannot tell you which you are looking at. These six prompts are for testing a model on your own work before you pay more for it.

⌨️ 6 prompts 🕐 Updated Sep 3, 2026

💡 How to use these prompts: Replace everything in [BRACKETS] with your specific details before sending. Click Copy to copy any prompt to your clipboard instantly.

Build a test set from real work

Testing on interesting cases overstates the difference. Testing on your real distribution does not.

I want to compare two models on my actual workload rather than a benchmark.

Here is what I use a model for: [DESCRIBE]

Design me a test set of twenty tasks that:
- Reflects the real distribution of my work, not the interesting edge cases
- Includes tasks I currently get wrong or have to redo
- Has a clear way to judge each one right or wrong

Tell me which tasks are hardest to judge objectively, so I know where my results will be softest.

Score two outputs blind

The last instruction matters. Asked to compare, a model will find a difference whether or not one exists.

Here are two responses to the same task. I have not told you which model produced which.

Task: [TASK]
Response A: [PASTE]
Response B: [PASTE]

Judge them on: correctness, completeness, whether anything is asserted without support, and which I could use with less editing.

Pick one and say why. If they are equivalent, say that rather than manufacturing a difference.

Work out where the cheaper one fails

Narrows an expensive comparison to the handful of tasks where a difference could exist.

I am using [CHEAPER MODEL] and considering [EXPENSIVE MODEL].

Based on what I have described about my work: [DESCRIBE], tell me:
- What categories of task the cheaper model most likely handles fine
- Where it plausibly falls short
- What a failure would look like, so I can spot it
- Which specific tests would separate them

Be concrete about the failure mode, not just that quality might be lower.

Model the real cost, not the rate card

Cache reads and tokenizer differences routinely change the answer that a rate-card comparison gives.

Compare the cost of [MODEL A] and [MODEL B] for my workload: [DESCRIBE VOLUME AND SHAPE]

Include:
- Input and output rates
- Cache read pricing and whether my workload benefits from it
- Any tokenizer differences that change token counts for the same input
- What a typical month costs on each

Price any promotional rate at its post-promotional value.

Decide whether the difference is worth the migration

Asking a model to argue the opposite case surfaces what a positive framing hides.

Testing showed [MODEL B] is better on [WHICH TASKS] by roughly [HOW MUCH], at [PRICE DIFFERENCE].

Tell me:
- What the migration would cost in engineering time and retesting
- What breaks or needs re-tuning
- How long the improvement takes to repay that
- What would have to be true for this to be a mistake

Argue against the migration, then tell me whether the argument holds.

Set up a check that keeps running

Model choice ages. Pricing changes, tokenizers change, workloads change. A monthly check catches it.

I have chosen a model. I want to notice if it stops being the right choice.

Design a lightweight recurring check that would tell me:
- Whether output quality has drifted
- Whether my costs have moved unexpectedly
- Whether my usage pattern has changed enough to reopen the decision

Keep it to something I would actually run monthly. An elaborate process I abandon is worse than none.