Code Grader vs LLM-as-Judge
A code grader scores AI output with deterministic rules — exact match, regex, tests passing. An LLM judge scores it with another model reasoning against a rubric.
| Dimension | Code Grader | LLM-as-Judge |
|---|---|---|
| Open-ended quality (tone, helpfulness, nuance) | Has nothing to check for "is this a good explanation" — there is no pattern to match against. | Wins here Can evaluate nuance the way a human reviewer would, at a fraction of the cost and time. |
| Reproducibility | Wins here Same input, same score, every single run, with zero variance. | Can vary run to run and carries known biases that have to be actively corrected for. |
| Cost per eval run | Wins here Near-instant and effectively free to run thousands of times in CI. | Costs a model call per graded example — real money and latency at scale. |
| Trust required before first use | Wins here The rule either matches or it doesn't — no validation step needed. | Needs its scores checked against real human labels (e.g., Cohen's kappa) before the numbers can be trusted. |
| Coverage of subjective tasks | Cannot touch summarization quality, groundedness, or writing style. | Wins here Purpose-built for exactly these judgment calls, with an auditable rubric and justification. |
| Bias risk | Wins here None — a rule has no preference for longer answers or answers that appear first. | Carries known biases: position, verbosity, and self-preference, all measurable and correctable but present by default. |
| Best practice at scale | Should be the default wherever a mechanical check exists. | Should be reserved, and validated, for the genuinely subjective remainder a code grader cannot reach. |
When to choose Code Grader
Use a code grader wherever correctness is mechanically verifiable — schema validity, exact answers, unit tests passing.
When to choose LLM-as-Judge
Use an LLM judge, validated against real human labels first, where quality is genuinely subjective and a human reviewer would otherwise be the only option.
The verdict
This is not a system-wide choice — mix scorer types by property. Use a code grader for anything mechanically checkable, and reserve a validated LLM judge for what is left. Most mature eval suites run both side by side.