RealKIE-FCC

RealKIE-FCC is a key information extraction benchmark drawn from real enterprise documents (FCC filings), part of the RealKIE suite of five novel datasets for enterprise key information extraction. Models must convert documents to markdown and extract structured fields against a specified JSON schema. Nova 2 reports results on a human-verified version of the dataset.

Nova 2 Pro from Amazon currently leads the RealKIE-FCC leaderboard with a score of 0.670 across 3 evaluated AI models.

Paper
About this benchmark

What RealKIE-FCC measures

RealKIE-FCC is a multimodal benchmark that evaluates large language models on multimodal, document understanding, and vision tasks. LLM Stats tracks 3 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.6, with the leader reaching 0.7.

Compare leaders on the best AI for multimodal, best AI for document understanding and best AI for vision leaderboards.

Publication

Paper
RealKIE: Five Novel Datasets for Enterprise Key Information Extraction
Authors
Benjamin Townsend, Madison May, Katherine Mackowiak, Christopher Wells
Published

Abstract

We introduce RealKIE, a benchmark of five challenging datasets aimed at advancing key information extraction methods, with an emphasis on enterprise applications. The datasets include a diverse range of documents including SEC S1 Filings, US Non-disclosure Agreements, UK Charity Reports, FCC Invoices, and Resource Contracts. Each presents unique challenges: poor text serialization, sparse annotations in long documents, and complex tabular layouts. These datasets provide a realistic testing ground for key information extraction tasks like investment analysis and contract analysis. In addition to presenting these datasets, we offer an in-depth description of the annotation process, document processing techniques, and baseline modeling approaches. This contribution facilitates the development of NLP models capable of handling practical challenges and supports further research into information extraction technologies applicable to industry-specific problems. The annotated data, OCR outputs, and code to reproduce baselines are available to download at https://indicodatasolutions.github.io/RealKIE/.

AmazonNova 2 Pro leads with 67.0%, followed by AmazonNova 2 Lite at 62.1% and AmazonNova 2 Omni at 59.8%.

Progress Over Time

Interactive timeline showing model performance evolution on RealKIE-FCC

State-of-the-art frontier
Open
Proprietary

RealKIE-FCC Leaderboard

3 models
ContextCostLicense
1
21.0M$0.30 / $2.50
3
Notice missing or incorrect data?

FAQ

Common questions about RealKIE-FCC.

What is the RealKIE-FCC benchmark?

RealKIE-FCC is a key information extraction benchmark drawn from real enterprise documents (FCC filings), part of the RealKIE suite of five novel datasets for enterprise key information extraction. Models must convert documents to markdown and extract structured fields against a specified JSON schema. Nova 2 reports results on a human-verified version of the dataset.

What is the RealKIE-FCC leaderboard?

The RealKIE-FCC leaderboard ranks 3 AI models based on their performance on this benchmark. Currently, Nova 2 Pro by Amazon leads with a score of 0.670. The average score across all models is 0.630.

What is the highest RealKIE-FCC score?

The highest RealKIE-FCC score is 0.670, achieved by Nova 2 Pro from Amazon.

How many models are evaluated on RealKIE-FCC?

3 models have been evaluated on the RealKIE-FCC benchmark, with 0 verified results and 3 self-reported results.

Where can I find the RealKIE-FCC paper?

The RealKIE-FCC paper is available at https://arxiv.org/abs/2403.20101. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does RealKIE-FCC cover?

RealKIE-FCC is categorized under multimodal, document understanding, and vision. The benchmark evaluates multimodal models.

Which model offers the best value on RealKIE-FCC?

Among models scoring within 10% of the leader, Nova 2 Lite from Amazon is the cheapest, at $0.30 per million input tokens with a score of 0.621.

How recent are the RealKIE-FCC leaderboard results?

The RealKIE-FCC leaderboard was last updated in June 2026 and currently includes 3 evaluated models.

More evaluations to explore

Related benchmarks in the same category

View all multimodal
Humanity's Last Exam

Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions

visionmultimodal
78 models
MMMU

MMMU (Massive Multi-discipline Multimodal Understanding) is a benchmark designed to evaluate multimodal models on college-level subject knowledge and deliberate reasoning. Contains 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering across 30 subjects and 183 subfields.

multimodalmultimodal
62 models
MMMU-Pro

A more robust multi-discipline multimodal understanding benchmark that enhances MMMU through a three-step process: filtering text-only answerable questions, augmenting candidate options, and introducing vision-only input settings. Achieves significantly lower model performance (16.8-26.9%) compared to original MMMU, providing more rigorous evaluation that closely mimics real-world scenarios.

multimodalmultimodal
53 models
CharXiv-R

CharXiv-R is the reasoning component of the CharXiv benchmark, focusing on complex reasoning questions that require synthesizing information across visual chart elements. It evaluates multimodal large language models on their ability to understand and reason about scientific charts from arXiv papers through various reasoning tasks.

multimodalmultimodal
37 models
MathVista

MathVista evaluates mathematical reasoning of foundation models in visual contexts. It consists of 6,141 examples derived from 28 existing multimodal datasets and 3 newly created datasets (IQTest, FunctionQA, and PaperQA), combining challenges from diverse mathematical and visual tasks to assess models' ability to understand complex figures and perform rigorous reasoning.

multimodalmultimodal
36 models
AI2D

AI2D is a dataset of 4,903 illustrative diagrams from grade school natural sciences (such as food webs, human physiology, and life cycles) with over 15,000 multiple choice questions and answers. The benchmark evaluates diagram understanding and visual reasoning capabilities, requiring models to interpret diagrammatic elements, relationships, and structure to answer questions about scientific concepts represented in visual form.

multimodalmultimodal
32 models