Prove the lift · AI model performance evaluation
gemini-1.5-pro vs gemini-1.5-flash
+18-point lift for gemini-1.5-pro on support-ticket-triage
Test summary
This benchmark evaluates gemini-1.5-pro against gemini-1.5-flash on the support-ticket-triage v2 dataset (6 rows), scored by an LLM-as-judge (llama-3.1-8b, run locally). gemini-1.5-pro reached a 72% pass rate versus 54% for gemini-1.5-flash — a +18-point lift.
- Base model
- gemini-1.5-flash
- via google · 54% pass rate
- Refined model
- gemini-1.5-pro
- via google · 72% pass rate · 1.2s p95
Base pass rate
54%
Refined pass rate
72%
Refined p95
1.2s
Est. tokens saved
4.0K
+0pts
refined beats base on 6 rows
0%
Base model0%
Invoked-refined▲2 improved▼0 regressed●4 unchanged
Per-row results
1Detect churn-risk sentiment in a renewal thread0% → 100%+100
2Route a multi-part ticket to the correct queue100% → 100%0
3Classify: billing dispute vs refund request vs cancellation0% → 100%+100
4Extract the affected order id from a free-text complaint100% → 100%0
5Priority-tag an outage report vs a feature question100% → 100%0
6Summarize a 12-message thread into a one-line disposition100% → 100%0
Prove the lift on your own data.
Run any two models over your tasks, judged automatically, and see the improvement — in seconds.
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