Gemini 3 Pro - PolicyBench v1.0 Results
85 of 147 (57.8%) policies fully verified, 82.8% fixture-level pass rate.
Headline
| Self-reported VALID | Compile OK | Fixture PASS (entry-level) | Fixture pass rate (fixture-level) | Self-report agreement |
|---|---|---|---|---|
| 100.0% | 100.0% | 57.8% | 82.8% | 0.578 |
By category
| Category | Entries | Compile OK | Fixture PASS (entry) | Fixture pass rate |
|---|---|---|---|---|
| application_authz | 3 | 100.0% | 66.7% | 83.3% |
| iac_scanning | 65 | 100.0% | 43.1% | 77.1% |
| kubernetes_admission | 79 | 100.0% | 69.6% | 88.4% |
Performance
Per-call latency from the runner's recorded latency_ms.
| Calls timed | p50 | mean | p95 | max | Total |
|---|---|---|---|---|---|
| 147 | 18.26 s | 20.50 s | 43.31 s | 72.59 s | 50.2 min |
Quality flag distribution
No quality flags raised.
Notable disagreements
Entries where the runner's self-reported status disagrees with the harness verdict. These are the most informative entries for understanding model blind spots.
| Entry | Harness verdict | Disagreement |
|---|---|---|
sp_iac_ckv_ckv_aws_248 |
FIXTURE_FAIL | claimed VALID but every fixture fails |
sp_k8s_gk_k8sdisallowanonymous_disallow-anonymous |
FIXTURE_FAIL | claimed VALID but every fixture fails |
Badge
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Markdown
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Source
- Harness verdict (JSON) - what PolicyBench's evaluator recorded
- Runner result (JSON) - the raw output the runner captured from the model
- Runner source - the script that called the model
Other tools
- PolicyAsLanguage - 90.5% entry-level pass
- Gemini 3 Flash - 54.4% entry-level pass
- Claude Opus 4.7 - 46.9% entry-level pass
- GPT-5 - 36.7% entry-level pass
- Claude Sonnet 4.6 - 36.1% entry-level pass
- GPT-5 mini - 25.2% entry-level pass