The [ Q ] Benchmark
An insurance-specific measure of AI capabilities for real insurance tasks. See how leading frontier models actually perform on the workflows your team runs every day.
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What We Tested
Frontier models evaluated head-to-head on real insurance workflows.
Multiple Frontier Models
Head-to-head comparison of leading AI models from Anthropic, OpenAI, Google, xAI, and others on identical tasks.
Realistic Data
Every task uses realistic documents covering variety in format, layout, and quality that mirrors what your team sees in production.
Tiered Complexity
Tasks range from single-document extraction to multi-document workflows and end-to-end submission decisioning.
Rigorous Scoring
Per-field granularity with both strict and lenient scoring modes to separate substantive errors from cosmetic differences.
What's Inside
The report covers tasks broadly applicable to most underwriting operations.
Single-Document Extraction
How well do models handle extracting structured data from a single, short-format insurance document? This mirrors everyday workflows like processing applications and evidence of coverage.
Large-Document Extraction
When documents exceed the context window, models need additional engineering to perform. We measure how accuracy holds up on lengthy, real-world insurance documents.
Multi-Document Extraction
Insurance submissions often span multiple files. We test each model's ability to synthesize the correct value when information appears across several sources.
Submission Decisioning with Reasoning
An end-to-end workflow that makes a quote, refer, or decline decision and extracts the supporting reasons — testing both the decision and its justification against real business rules.
Cross-Cutting Analysis
How model rankings shift by task complexity, when agentic approaches outperform single-pass methods, and what the gap between strict and lenient scoring reveals about real-world readiness.
Methodology
Every task in the [ Q ] Benchmark uses realistic insurance documents that reflect the variety of formats, layouts, and quality levels your team encounters in production.
Scoring is applied at per-field granularity: every extracted value is scored independently against a rubric. We use both strict scoring (exact character-level match) and lenient scoring (normalized for whitespace, punctuation, and case) to separate substantive errors from cosmetic differences.
Benchmark Disclaimer
The benchmark results presented in this report are provided for informational and research purposes only. They reflect performance under specific evaluation conditions and may not represent real-world performance, reliability, safety, or suitability for any particular use case. Differences in evaluation setup, datasets, prompts, and model configurations may produce different results, and small score differences may not be statistically meaningful.
For a full explanation of benchmark limitations and interpretation guidelines, please visit: www.gocomplete.ai/legal/benchmark-disclaimer
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