Coverage Cat AI Insurance Bench

We’re Coverage Cat. We built an AI insurance benchmark to measure how frontier models perform on real insurance workflows, not trivia or generic reasoning tasks.

TL;DR

Coverage Cat’s AI Insurance Benchmark compares 7 frontier models on two insurance-specific evals:

• Price estimation: can a model estimate umbrella insurance quote ranges accurately?
• Brokerage/Agent task: can a model answer practical insurance questions the way a good broker or agent would?

The public leaderboard ranks models by Elo Rating, Win Rate, Quote Coverage, Quote MAPE, Winkler Loss, and judged brokerage-task quality.

View it here: Coverage Cat AI Insurance Benchmark (https://www.coveragecat.com/ai/leaderboard)

The Problem

Most AI benchmarks don’t tell you whether a model is useful inside a real insurance workflow or whether consumers can really trust it for insurance information.

Insurance work is full of messy edge cases: pricing uncertainty, underwriting rules, coverage terminology, household risk factors, and questions where the answer needs to be both correct and concise.

There has been good prior work on insurance AI benchmarks, including UNDERWRITE, which evaluates multi-turn underwriting agents with domain experts, noisy tools, and simulated users. That work focuses on enterprise underwriting agent behavior and found meaningful gaps between general model performance and enterprise readiness.

Our benchmark is different: it focuses on consumer insurance workflows Coverage Cat actually needs to automate, including umbrella quote estimation and broker/agent-style answers. It is also designed as a public, continuously updated leaderboard rather than a one-time research benchmark.

What We Built

The first public version includes:

• 2,800+ price-estimation scenarios
• 19,000+ quote model results
• 1,200+ brokerage/agent task questions
• 8900+ QA model results
• 7 tested models

The benchmark uses anonymized eval data, no-retention/no-logging model platforms, and aggregate-only public snapshots. Customer data is not exposed to model providers.

What’s Next

We’ll update the leaderboard soon with newer frontier models as they become available through our approved model-provider paths. We also expect to expand the benchmark with more brokerage workflows, richer human review, and additional insurance product lines.

Our goal is simple: make it obvious which AI models are actually good at insurance work and help those models improve.

View the leaderboard: Coverage Cat AI Insurance Benchmark (https://www.coveragecat.com/ai/leaderboard)

If you’re shopping for umbrella coverage, you can also start here: Coverage Cat Umbrella Insurance (https://www.coveragecat.com/insurance-types/umbrella-insurance).