---
title: "Financial Benchmarks"
description: "Ramp describes how it benchmarks large language models (LLMs) used in financial products against day-to-day tasks such as invoice extraction, financial-statement OCR, policy review, accounting autocoding, compliance detection, and fund routing. These benchmarks combine task-specific metrics with cost, latency, reasoning effort, human decisions, historical context, and ground-truth datasets where available. For contextual invoice OCR, perfect extraction requires every field to match the user's final bill, while financial-statement OCR uses a 1% relative-error threshold against transcriptions from over 500 real P&L documents. Results show application-specific trade-offs: Gemini 3 Flash is described as a cost-efficient leader for several visual and financial tasks, while Claude models lead some high-accuracy or low-miss-rate settings, and behavior can differ within one provider. The framework emphasizes Pareto trade-offs and continuous testing, with plans to expand from benchmarking current performance to hill climbing on capabilities."
---

# Financial Benchmarks

[Ramp](https://yomu.fyi/company/ramp) · Kedar Thakkar, Anton Biryukov, Ashwin Kumar, Ryne Carbone · Mar 9, 2026

**Type:** Benchmark

## Summary

Ramp describes how it benchmarks large language models (LLMs) used in financial products against day-to-day tasks such as invoice extraction, financial-statement OCR, policy review, accounting autocoding, compliance detection, and fund routing. These benchmarks combine task-specific metrics with cost, latency, reasoning effort, human decisions, historical context, and ground-truth datasets where available. For contextual invoice OCR, perfect extraction requires every field to match the user's final bill, while financial-statement OCR uses a 1% relative-error threshold against transcriptions from over 500 real P&L documents. Results show application-specific trade-offs: Gemini 3 Flash is described as a cost-efficient leader for several visual and financial tasks, while Claude models lead some high-accuracy or low-miss-rate settings, and behavior can differ within one provider. The framework emphasizes Pareto trade-offs and continuous testing, with plans to expand from benchmarking current performance to hill climbing on capabilities.

## Context

Ramp uses benchmarks to quantify the value of increasing model intelligence on concrete financial work, support internal development velocity, and maintain trust and reliability while iterating quickly. The tasks involve trade-offs among accuracy, disagreement, uncertainty, cost, latency, coverage, and manual-review burden.

## Approach / What changed

The framework evaluates models on application-specific datasets and metrics, including perfect invoice extraction, financial-statement match rates at multiple error thresholds, policy-agent disagreement and unsure rates, accounting Accuracy@K, compliance adherence, and fund-routing override rates. It also compares reasoning effort, cost, latency, historical context, tools such as web search, and Pareto-frontier trade-offs.

## Takeaways

- Gemini 3 Flash nearly matches GPT 5.1 with high reasoning on contextual invoice OCR while costing less than one-third as much; higher inference-time reasoning improves performance for almost all models.
- Financial-statement OCR was evaluated on over 500 real P&L documents using a 1% relative-error match rate, with a secondary analysis at 5% and 10% tolerances.
- Policy-agent evaluation balances disagreement rate against unsure rate because minimizing disagreements alone could lead the model to forward most transactions for human review.

**Tags:** [AI](https://yomu.fyi/topic/ai), [Document AI](https://yomu.fyi/topic/document-ai), [LLMs](https://yomu.fyi/topic/llm), [Performance](https://yomu.fyi/topic/performance)

- Source: [Ramp](https://builders.ramp.com/post/financial-benchmarks)
- Source URL: https://builders.ramp.com/post/financial-benchmarks
- Ingested by Yomu: 2026-09-01T01:34:55.586Z

[Read original post](https://builders.ramp.com/post/financial-benchmarks)