CLI: compare
vally compare <experiment-dir> [options]vally compare --baseline <path> --treatment <path> [options]Description
Section titled “Description”Compares a treatment run against a baseline and reports a signed, treatment-relative verdict plus metric deltas and statistics. The prompt judge compares each stimulus against its rubric, which is read from the trajectories — so no eval spec is required.
Comparison mode always supplies each run’s output, metrics, and session timeline.
It does not compare workspace diffs or golden patches. If an eval spec configures
evidence with diff or golden_patch, compare warns that those selections
are ignored.
There are two input modes:
- Experiment mode —
vally compare <experiment-dir>. Reads each variant’sresults.jsonlfrom an experiment output directory, discovers the baseline variant from the embedded provenance, and fans out into one baseline-vs-treatment comparison per other variant. - Two-run mode —
vally compare --baseline <path> --treatment <path>. Compares two independent runs of the same eval spec (for example, a CI regression check across time). Trajectories are paired by stimulus name and trial index.
Options
Section titled “Options”| Flag | Type | Required | Description |
|---|---|---|---|
<experiment-dir> |
string | mode | Experiment output directory (experiment mode). Mutually exclusive with the two-run flags. |
--baseline <path> |
string | mode | Baseline run directory or JSONL file (two-run mode). |
--treatment <path> |
string | mode | Treatment run directory or JSONL file (two-run mode). |
--baseline-variant <name> |
string | No | Experiment mode: override the baseline variant recorded in the output. |
-e, --eval-spec <path> |
string | No | Optional eval spec to override the rubric embedded in the trajectories. |
--judge-model <model> |
string | No | Model for the comparison judge. |
--judge-reasoning-effort <level> |
string | No | Reasoning effort for the comparison judge model: low, medium, high, or xhigh. |
--output <path> |
string | No | Write per-treatment comparison records to this JSONL file. |
--fail-on-regression |
boolean | No | Exit non-zero if a treatment scores significantly worse than the baseline — a negative mean score whose 95% CI is entirely below zero (or every comparison fails). |
--param <key=value> |
string | No | Set a param value (repeatable, e.g. --param MODEL=gpt-4o). Overrides eval param files and .vally.yaml. |
--verbose |
boolean | No | Show per-stimulus and per-trial detail. |
Provide either an experiment directory or both --baseline and
--treatment — not both.
Output
Section titled “Output”For each treatment, compare prints:
- Win rate — wins / ties / losses across the paired trials.
- Mean score — the signed treatment-relative score in
[-1, 1](+means the treatment is better) with a 95% confidence interval. - McNemar test — a paired test on pass/fail flips between baseline and treatment, when the trajectories carry grade results.
- Metric deltas — per-metric
treatment − baselinedifferences (tokens, turns, tool calls, wall time, errors) with confidence intervals and a high-variance flag.
Failed judge calls are excluded from the statistics.
Scoring scale
Section titled “Scoring scale”The comparison verdict uses a signed five-bucket magnitude, from the treatment’s perspective:
| Verdict | Score |
|---|---|
| much better | +1.0 |
| slightly better | +0.4 |
| equal / tie | 0 |
| slightly worse | -0.4 |
| much worse | -1.0 |
The judge runs each comparison twice with the two responses swapped (position-swap debiasing); if the two directions disagree on the winner, the result is forced to a tie.
Exit codes
Section titled “Exit codes”| Code | Meaning |
|---|---|
0 |
Comparison completed (and no regression, when --fail-on-regression) |
1 |
Error occurred, or a regression was detected with --fail-on-regression |
Examples
Section titled “Examples”# Compare every variant of an experiment against its baselinevally compare ./experiments/output/2026-06-26T12-00-00
# Ad-hoc comparison of two independent runs (CI regression check)vally compare \ --baseline ./results/main \ --treatment ./results/pr-branch \ --fail-on-regression
# Override the rubric with an eval spec and pick a judge modelvally compare ./experiments/output/latest \ --eval-spec eval.yaml \ --judge-model gpt-5.5 \ --verbose
# Write machine-readable comparison recordsvally compare \ --baseline ./results/v1.jsonl \ --treatment ./results/v2.jsonl \ --output ./comparison.jsonlWorkflow
Section titled “Workflow”A typical A/B comparison:
- Run eval on the baseline:
vally eval --eval-spec eval.yaml --output-dir ./baseline - Make your change (update a skill, change a model, etc.).
- Run eval on the treatment:
vally eval --eval-spec eval.yaml --output-dir ./treatment - Compare:
vally compare --baseline ./baseline --treatment ./treatment
For multi-variant experiments, run vally experiment run and point compare at
the experiment output directory instead — it compares every variant against the
declared baseline in one pass.
Comparing multi-turn evals
Section titled “Comparing multi-turn evals”Multi-turn stimuli (those that use turns instead of prompt)
compare exactly like single-turn ones — there is no special flag. compare pairs
trajectories by stimulus name and trial index, and the prompt judge reads the full
recorded conversation for each side, so it judges the end-to-end interaction (memory,
follow-ups, recovery) against the stimulus rubric.
# Baseline and treatment specs both contain the same multi-turn stimulivally eval --eval-spec eval.yaml --output-dir ./baseline# ...change a skill/model/prompt...vally eval --eval-spec eval.yaml --output-dir ./treatment
# Same command as any other comparison — turns are handled automaticallyvally compare --baseline ./baseline --treatment ./treatment --verboseNotes for multi-turn comparisons:
- Give each multi-turn stimulus a stable
name— pairing is by name, so renaming a stimulus makes it look like a removed/added case rather than a change to compare. - A
rubricthat describes the whole conversation (not just the last turn) gives the judge the context it needs;--verboseshows the per-stimulus verdict. - The
turnsmetric in the metric deltas reflects agent conversation turns during execution, which is independent of how many promptturnsyou authored.
See Writing Eval Specs → Multi-turn conversations
for how to author these stimuli, and samples/multi-turn-memory/
for a runnable example.