Prove the lift · AI model performance evaluation

gemini-2.5-pro vs grok-4

+12-point lift for gemini-2.5-pro on support-ticket-triage

Test summary

This benchmark evaluates gemini-2.5-pro against grok-4 on the support-ticket-triage v1 dataset (6 rows), scored by an LLM-as-judge (claude-haiku-4-5). gemini-2.5-pro reached a 65% pass rate versus 53% for grok-4 — a +12-point lift.

Base model
grok-4
via xai · 53% pass rate
Refined model
gemini-2.5-pro
via google · 65% pass rate · 2.3s p95
Base pass rate
53%
Refined pass rate
65%
Refined p95
2.3s
Est. tokens saved
8.5K
+0pts
refined beats base on 6 rows
0%
Base model
0%
Invoked-refined
1 improved2 regressed3 unchanged

Per-row results

1Extract the affected order id from a free-text complaint100%100%0
2Detect churn-risk sentiment in a renewal thread100%0%-100
3Route a multi-part ticket to the correct queue100%100%0
4Classify: billing dispute vs refund request vs cancellation0%100%+100
5Priority-tag an outage report vs a feature question100%0%-100
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.

Try Invoked
Generated with Invoked · Browse all benchmarks