I made 3 cheap LLMs rewrite 30 terrible dating bios and had a blind judge score the cringe. The cheapest model won.
I wanted to test whether price predicts writing quality on a task where "sounds human" is everything, so I built a small pipeline on Evaligo building a profile for a dating site.
The test set was 30 intentionally bad bios - the "gym, tacos, dogs" one-liner, the "partner in crime, fluent in sarcasm" special, one all-emoji, one bitter rant. A separate model (not one of the contestants) scored every output 0–1 against a rubric: reuses the person's real details, no clichés, the three tones actually differ, tip is specific not generic. Minus 0.5 if it invented facts. The judge never saw which model wrote what.
Results over 30 bios each:
| Model | score | Flawless bios | $ / profile | Latency |
|---|---|---|---|---|
| gpt-5.6-luna | 0.86 | 17/30 | $0.0006 | 4.6s |
| gemini-3.5-flash | 0.79 | 7/30 | $0.0163 | 8.4s |
| claude-haiku-4-5 | 0.67 | 4/30 | $0.0024 | 5.5s |
The interesting failures:
- Haiku's worst scores all came from the cliché-stuffed bios — it echoed "partner in crime" energy back instead of replacing it.
- Flash wrote well but its witty versions drifted "a bit forced" per the judge, and 27x the cost was mostly invisible thinking tokens.
- Luna's only recurring weakness: the three tones occasionally blurred together. It never invented facts.
Also learned the hard way that sample size matters: at n=6 flash and haiku tied, at n=30 they clearly separated. The whole benchmark cost about $0.90.
Happy to share the rubric or the raw score distributions if anyone wants to poke at the method. (Disclosure: I build the tool I ran this on, so questions about the harness are fair game too.)