How Do You Compare AI Models on Your Own Tasks Instead of Public Benchmarks?

Run the same inputs through every model you're considering, score the outputs blind or against a reference answer set, and record quality, cost, and speed per output. Public leaderboards tell you how models perform on someone else's tasks; this method tells you which model wins on yours. It's worth doing whenever your workload is specific enough that a general ranking might not transfer — for example, language detection, listing localization, or grading writing level.

Why public benchmarks aren't enough

A model that tops a general leaderboard can lose on your task, and a cheaper model can tie or beat an expensive one. Evaligo's own published experiments show this pattern clearly:

The takeaway across all three: price did not predict quality. That's the reason to test on your own inputs rather than trust a ranking.

The core method: same inputs, multiple models, one scoreboard

The fair-comparison rule is simple — every model must see identical inputs, and every output must be scored the same way. Evaligo's flow builder is built around this: you build a flow of prompts, agents, and tools, then run it on several models as variants.

Step 1 — Build the flow

Describe the flow in one sentence and the builder assembles it: prompts, web scraping, data extraction, tools, and agents as connected nodes. You can drag nodes on the canvas to edit, or change the flow by instruction. The point is that the flow stays constant while the model changes — that's what makes the comparison valid.

Step 2 — Pick models as variants

Select the models you want to compare. This includes OpenAI, Google, and Anthropic models, plus open-source options such as DeepSeek, MiniMax, Kimi, and Qwen, reachable through a single OpenRouter key. Each model becomes a variant of the same flow.

Step 3 — Score every output blind

Use blind AI judge panels, or grade against a gold/reference answer set. Blind judging reduces the bias you'd get from knowing which model produced which output. In the localization experiment, a blind three-judge panel produced the scores.

Step 4 — Read the scoreboard

For each output you get quality, cost, and speed together — not just a score. This is what lets you catch the "same score, 32× the price" case, which a quality-only view would hide.

Step 5 — Route and deploy

Once you know which model wins for which input type, you can train a per-input model router on the results, then deploy the flow as an API. Routing matters when no single model wins everywhere — you send each input to the model that performs best on that kind of input.

What to record, and why each metric matters

A model that ties on score but costs a fraction as much is usually the right default; keep the expensive model only for input types where it measurably wins.

Common pitfalls

• Different inputs per model. If models don't see identical inputs, the comparison is meaningless.
• Unblinded scoring. Knowing which model produced an output biases the judgment.
• Quality-only evaluation. You'll miss cases where a cheap model ties an expensive one.
• Assuming price predicts quality. The writing-level experiment showed the opposite.
• Picking one model for everything. Per-input routing often beats a single global choice.

Getting started

Evaligo offers a free plan with no credit card, and you can run your first A/B test free. If you want to see the method in action before building your own, the published benchmarks (six models, the same 30 inputs, three blind judges) are available as a full write-up.