I built a pytest harness that runs the same set of questions through five language models at once - a free local Llama, plus GPT, DeepSeek, and two Claude models - and compares them on the three things a team pays for: cost per query, speed, and answer quality. The plan was simple. Run the grid, read the scoreboard, say which model to use.
The scoreboard came back clean and easy to read. This is the story of why I didn't trust it, and what I found when I checked. The thing I stopped trusting wasn't any of the models. It was the tool I was using to score them.
It's also the first project in this series that spends real money. Every one before it ran locally, for free. Here each call costs something, and the whole comparison came to about 21 cents. That price is small, but it changed how I tested, and not in the way I expected.
The scoreboard, and why I didn't stop there
Five models, the same ten questions, twice each, every call measured. Here is the run, ordered by quality score (a second model grades each answer on correctness and relevance, combined into a 0-1 score, pass line 0.7):
