Compare AI models with a blind marketing task review
Compare AI models using equal briefs, hidden model labels, declared acceptance rules and a complete cost and latency record for the tested workflow.
What needs to be held constant?
Use identical task inputs, supplied facts and acceptance criteria when comparing candidate configurations. If one model gets a stronger brief or more editing help, the result describes different workflows. That may be a valid comparison, but label it accordingly and include the additional work in the cost boundary.
OpenAI’s evaluation documentation describes criteria-based tests and reports. Conclusions remain limited by the evaluation set’s coverage. The protocol below is an original practical design for comparing drafts, not a claim that blind review eliminates every source of bias. [Working with evals]
A reproducible blind-review protocol
- Freeze a versioned set of representative briefs, fact packs and required output formats.
- Generate the declared number of responses for each candidate under recorded settings.
- Assign neutral output IDs and randomize presentation order; keep the mapping restricted until review ends.
- Review factual failures and score the anchored rubric without model labels.
- Record acceptance, repairs and disagreement before revealing identities.
- Compare the results with generation cost, latency and review effort.
Use realistic difficult cases, including missing prices, conflicting evidence and narrow claims that tempt exaggeration. A set containing only easy generic descriptions can hide the failures that matter in production. Do not silently drop failed or incomplete responses from the comparison.
An illustrative results sheet
| Measure | Candidate A | Candidate B |
|---|---|---|
| Accepted unchanged | 5 | 6 |
| Accepted after repair | 3 | 2 |
| Rejected | 2 | 2 |
| Hard factual failures | 1 | 0 |
| Generation cost, USD | $0.80 | $1.20 |
| Review and repair time | 50 minutes | 35 minutes |
At an illustrative $40 hourly review rate, A costs $0.80 + $33.33 = approximately $34.13 for eight accepted outputs, or about $4.27 each. B costs $1.20 + $23.33 = approximately $24.53, or about $3.07 each. Calculate with unrounded minutes and rates. These are teaching figures, not provider prices or measured model performance.
The hard-failure row still matters. If a factual failure is disqualifying for the intended task, a low cost cannot rescue that candidate. Investigate the failure and test a revised configuration before expanding use. A small difference in accepted counts should not be presented as a statistically established advantage.
What should the decision record include?
- Task types, languages and evidence conditions represented in the set.
- Candidate version and relevant generation settings.
- Requested, completed, accepted, repaired and rejected output counts.
- Observed latency distribution and timeout handling, not only a convenient fastest result.
- Total included cost and the review-rate assumption.
- Uncovered tasks and the conditions that would trigger reevaluation.
Latency needs an operational definition: time to first response, full validated output or accepted edited draft. Choose the one relevant to the workflow and preserve the others separately if useful. A fast response that requires extensive repair may be slower at the point where the work becomes usable.
I would retain the neutral output IDs and scoring notes after choosing a candidate. When a model version, brief or fact policy changes, the same evaluation set becomes a useful regression check. Revisit the set when the real work changes rather than treating an old winner as permanent.
Sources
AI-assisted writing and an AI-generated editorial cover. Source links and checked dates are listed below; illustrative examples are labeled in the text.
- Working with evals — checked 2026-10-18