Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.
/ quick answer
Both score AI output. Production suites need both layers, at different frequencies. Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.
| Dimension | Option A | Option B |
|---|---|---|
| Cost | Assertion: free | Model-graded: tokens per case |
| Speed | Assertion: milliseconds, every commit | Model-graded: minutes, nightly |
| Objectivity | Assertion: deterministic | Model-graded: variance between runs |
| Coverage | Assertion: structure and facts | Model-graded: tone, reasoning, helpfulness |
| Maintenance | Assertion: brittle to format change | Model-graded: rubric drift over time |
- →Assertion: schema validity, required fields, forbidden content, exact IDs.
- →Model-graded: writing quality, answer completeness, reasoning soundness.
What is the difference in Model-Graded Evals vs Assertion Evals?
Both score AI output. Production suites need both layers, at different frequencies.
What are the main points of comparison?
Cost: Assertion: free vs Model-graded: tokens per case · Speed: Assertion: milliseconds, every commit vs Model-graded: minutes, nightly · Objectivity: Assertion: deterministic vs Model-graded: variance between runs · Coverage: Assertion: structure and facts vs Model-graded: tone, reasoning, helpfulness · Maintenance: Assertion: brittle to format change vs Model-graded: rubric drift over time
Which one should I choose?
Assert everything you can express as a rule and run it on every commit. Reserve model grading for subjective quality, run it nightly, and pin the grader model so scores stay comparable.