Benchmark Report#

Overview#

This benchmark evaluates the performance of various Large Language Models (LLMs) and parsing strategies in extracting and parsing document content using Lexoid.

Each approach is evaluated based on a comparison between the parsed content and the manually created ground truths of several documents, with a similarity metric indicating the accuracy of the parsing process.

Similarity Metric#

The similarity metric is calculated using the following steps (see calculate_similarity() in lexoid/core/utils.py for the implementation).

  1. Markdown Conversion Both parsed and ground truth documents are converted to HTML, standardizing their format across structural elements like tables and lists.

  2. HTML Tag Removal All HTML markup is stripped away, leaving only the pure textual content. This ensures the comparison focuses on the actual text rather than formatting.

  3. Sequence Matching Python’s SequenceMatcher compares the extracted text sequences, calculating a similarity ratio between 0 and 1 that reflects content preservation and accuracy.

Running the Benchmarks#

Setup Environment Variables#

Create a .env file with the necessary API keys:

OPENAI_API_KEY=your_openai_key
GOOGLE_API_KEY=your_google_key
HUGGINGFACEHUB_API_TOKEN=your_huggingface_token
TOGETHER_API_KEY=your_together_api_key

Running the Benchmark Script#

# Clone the repository
git clone https://github.com/oidlabs-com/lexoid.git
cd lexoid

# Install dependencies
pip install -r requirements.txt

# Run benchmarks
python tests/benchmark.py

Customizing Benchmarks#

You can modify the test_attributes list in the main() function to test different configurations:

  • parser_type: Switch between LLM and static parsing

  • model: Test different LLM models

  • framework: Test different static parsing frameworks

  • pages_per_split: Adjust document chunking

  • max_threads: Control parallel processing

Benchmark Results#

Here are the detailed parsing performance results for various models:

Rank

Model

Mean Similarity

Std. Dev.

Time (s)

Cost ($)

1

gemini-2.0-flash

0.829

0.102

7.41

0.00048

2

gemini-2.0-flash-001

0.814

0.176

6.85

0.000421

3

gemini-1.5-flash

0.797

0.143

9.54

0.000238

4

gemini-2.0-pro-exp

0.764

0.227

11.95

TBA

5

AUTO

0.76

0.184

5.14

0.000217

6

gemini-2.0-flash-thinking-exp

0.746

0.266

10.46

TBA

7

gemini-1.5-pro

0.732

0.265

11.44

0.003332

8

accounts/fireworks/models/llama4-maverick-instruct-basic (via Fireworks)

0.687

0.221

8.07

0.000419

9

gpt-4o

0.687

0.247

10.16

0.004736

10

accounts/fireworks/models/llama4-scout-instruct-basic (via Fireworks)

0.675

0.184

5.98

0.000226

11

gpt-4o-mini

0.642

0.213

9.71

0.000275

12

gemma-3-27b-it (via OpenRouter)

0.628

0.299

18.79

0.000096

13

gemini-1.5-flash-8b

0.551

0.223

3.91

0.000055

14

Llama-Vision-Free (via Together AI)

0.531

0.198

6.93

0

15

Llama-3.2-11B-Vision-Instruct-Turbo (via Together AI)

0.524

0.192

3.68

0.00006

16

qwen/qwen-2.5-vl-7b-instruct (via OpenRouter)

0.482

0.209

11.53

0.000052

17

Llama-3.2-90B-Vision-Instruct-Turbo (via Together AI)

0.461

0.306

19.26

0.000426

18

Llama-3.2-11B-Vision-Instruct (via Hugging Face)

0.451

0.257

4.54

0

19

microsoft/phi-4-multimodal-instruct (via OpenRouter)

0.366

0.287

10.8

0.000019