5 Best Document Parsers in 2026 (Tested on Financial PDFs)

I’ve spent a lot of time working with PDFs that looked simple at first glance but quickly turned messy once I tried to convert them into clean, usable data. Tables break, headings lose hierarchy, footnotes disappear, and suddenly the output needs more cleanup than expected. That’s what pushed me to test multiple document parsers using the same financial report and compare how they actually perform in real scenarios.

In this article, I compare five leading document parsers, Gemini 2.5 Pro, LandingAI, LlamaParse, Dolphin, and Nanonets, to show how they handle tables, headings, footnotes, and markdown. I’ll break down which tools are fastest, which preserve structure best, what they really cost, and when self-hosting is worth it, so you can choose the right parser based on your stack, budget, and deadlines.

1. LandingAI

Backed by Andrew Ng, Landing AI has built its Agentic Document Extraction tool to simplify document parsing without complex setup. The platform offers a free playground where you can quickly test its capabilities, making it ideal for teams who want to experiment before committing. 

Its well-structured documentation and fine-grained API controls make integration smooth for developers. While the output is generally accurate and reliable, users should note that pricing starts at $0.03 per page, which is manageable for smaller projects but may become costly at scale. Overall, it’s a strong choice for accuracy-focused, low-effort deployment.

How to use LandingAI?

Getting started with LandingAI is simple and only takes a few minutes:

Requirements: 

• Get your API key from Landing AI settings.
• Install the required library: pip install agentic-doc

Code: 

import time
from agentic_doc.parse import parse

# Parse a local file
start = time.time()
result = parse("QuantumLeap Analytics Inc. - Q3 2024 Financial Report.pdf")
end = time.time()

# Get the extracted data as markdown
print("Extracted Markdown:")
print(result[0].markdown)

time_taken = end - start
print(f"Total time taken: {time_taken}")

Output:

Link: LandingAI output

Issues of LandingAI

• Markdown formatting – In my tests, most content was extracted correctly, but the markdown structure wasn’t always consistent. Headings on the second page, for example, were misaligned or downgraded in hierarchy, which means additional cleanup is sometimes required before using the output directly.
• Footnotes parsing – The parser struggled with inline notes and footnotes, either skipping them or placing them out of context. This can be a problem when working with research papers, legal contracts, or financial reports, where footnotes often carry critical details.
• Processing time – On my RTX 4090 benchmark, parsing took ~41.88 seconds for a mid-sized financial PDF. While acceptable for occasional use, this may feel slow if you’re processing high volumes or need real-time results.

Pros of LandingAI

• Ease of use – Setup is straightforward: grab an API key, install the library, and you’re ready. Even teams without machine learning expertise can integrate it quickly.
• Accuracy in structured data – For most financial tables and document sections, LandingAI produced accurate results with minimal errors. This makes it especially useful for business documents where numbers and formatting are important.
• Developer-friendly documentation – Their API docs are clear and come with sample code, making integration smoother compared to some open-source alternatives.

Cons of LandingAI

• Costly at scale – At $0.03 per page, costs can grow quickly for enterprises processing thousands of documents monthly. Unlike open-source models, there’s no way to self-host to cut costs.
• Closed source – Users have little flexibility to customize or fine-tune the underlying model. If the parser makes mistakes, you can’t directly improve it.
• Slower than competitors – Tools like Dolphin processed the same file in under 10 seconds. While LandingAI is accurate, its speed lags behind lighter-weight or GPU-optimized solutions.

2. Dolphin by ByteDance

Dolphin is an open-source document parsing model released by ByteDance and available on Hugging Face. It’s designed to handle standard PDFs and text-heavy documents reasonably well, but like many open-source parsers, it can stumble when faced with more complex structures such as nested tables, multi-column layouts, or documents with heavy formatting.

Being open source, Dolphin gives developers the flexibility to self-host, experiment, and customize workflows without recurring per-page costs. However, it does require a dedicated GPU (around 5.8 GB of VRAM), making it more suited to teams who are comfortable with infrastructure management.

In my benchmarks, Dolphin delivered decent accuracy on headings and simple tables, but occasionally misordered content or misformatted markdown in dense financial reports. On the plus side, it was fast (~7.1 seconds) compared to most commercial tools, making it attractive for projects where speed matters more than perfect structure.

How to use Dolphin by ByteDance?

• Requirements: ~5.8 GB GPU, Python 3.8+, and standard ML libraries.
• Setup: Install dependencies and follow instructions from Hugging Face or the official GitHub repo.

Output:

Link: Dolphin by ByteDance Output Link

Issues:

• Markdown formatting – Dolphin produced raw text with very little markdown structure, so headings and sections often appeared unformatted. This makes the output harder to use directly in reports.
• Heading order problems – In multi-section documents, Dolphin occasionally shuffled heading levels or misplaced them entirely, reducing readability.
• Symbol parsing errors – We noticed $ signs in financial data were often mis-parsed as $/$, which could confuse automated workflows relying on accurate currency data.
• Processing time – On our RTX 4090, Dolphin was fast at ~7.13 seconds per document, but the accuracy trade-off means extra cleanup is often required.

Pros of Dolphin by ByteDance: 

• Open source – Free to use, with source code available for customization and improvements.
• Self-hosting – No dependency on third-party servers; you control performance, privacy, and scaling.
• Speed – One of the fastest parsers tested, especially compared to heavier cloud-based tools.

Cons of Dolphin by ByteDance:

• Inconsistent accuracy – While it handles simple documents well, Dolphin struggles with nested tables, footnotes, and complex layouts.
• High GPU requirement – Needs ~5.8 GB GPU memory, which can be a barrier for smaller teams.
• Extra cleanup needed – Since formatting isn’t reliable, you often need post-processing before using the parsed output.

3. LlamaParse

LlamaParse is the cloud-based document parsing solution offered by LlamaIndex. It’s designed to handle a wide variety of document types, from research papers and contracts to financial reports. One of its biggest advantages is accessibility,  every user gets 10,000 free credits per month, making it easy to try before scaling. 

Because it’s a cloud service, LlamaParse doesn’t require heavy GPU resources like Dolphin. Integration is straightforward, with a simple Python SDK and support for multiple file formats. In our tests, it managed standard PDFs well, preserving tables and headings, but struggled slightly with very complex documents (e.g., multi-level nested tables).

Best for: Developers and teams who want a lightweight, cost-effective parser with generous free usage, and don’t want to worry about GPU or infrastructure management.

How to use LlamaParse?

Requirements: 

• Obtain the API key from Llama Cloud
• pip install llama-cloud-services

Code:

from llama_cloud_services import LlamaParse
from dotenv import load_dotenv
import os
import time

load_dotenv()

parser = LlamaParse(
    api_key=os.getenv("LLAMA_CLOUD_API_KEY"),
    num_workers=4,
    verbose=True,
    language="en",
)

start = time.time()
result = parser.parse("QuantumLeap Analytics Inc. - Q3 2024 Financial Report.pdf")
end = time.time()

# Get the markdown documents from the result
markdown_documents = result.get_markdown_documents(split_by_page=True)

# Combine all markdown content
markdown_content = ""
for i, doc in enumerate(markdown_documents):
    if i > 0:
        markdown_content += "\n\n---\n\n"  # Add page separator
    markdown_content += doc.text

# Save to markdown file
with open("markdown_output_llama_parse.md", "w", encoding="utf-8") as f:
    f.write(markdown_content)

print(f"Markdown content saved to markdown_output_llama_parse.md")
print(f"Time taken: {end - start:.2f} seconds")
print(f"Number of pages processed: {len(markdown_documents)}")

Output:

Link: LlamaParse Output

Issues: 

• Missed sections – In our tests, LlamaParse skipped important parts such as Miss Stock-Based Comp. Allocation and Customer Segmentation. For documents where every detail matters (like financial reports or legal texts), these omissions can be critical.
• Structural inconsistencies – While most content was captured, the parser occasionally scrambled document hierarchy, leading to misplaced headings or incomplete table alignment. This means extra cleanup may be required before using the output directly.
• Processing time – Parsing took around 53.68 seconds on our benchmark document, making it slower than Dolphin and LandingAI. For small-scale usage, this is fine, but at scale, the lag may be noticeable.

Pros of LlamaParse: 

• Easy to use – Since LlamaParse is cloud-based, you don’t need to worry about GPUs or heavy local setup. Just grab an API key, install the SDK, and you’re ready to parse.
• Accurate for most documents – It handles standard PDFs, contracts, and reports quite well, preserving headings, tables, and text structure in clean markdown format.

Cons of LlamaParse:

• Struggles with complexity – When dealing with highly complex layouts, such as deeply nested financial tables or multi-section reports, LlamaParse can miss sections or distort the structure.
• Cloud-only dependency – Requires an internet connection and API access, so it’s not suitable for offline or on-premise use cases.

4. Nanonets

Nanonets-OCR-s is one of the most accurate open-source models we tested for document parsing. It consistently captured even the most complex tables, multi-level headings, and footnotes with high precision, making it stand out from other open-source alternatives like Dolphin.

The trade-off, however, is performance. Nanonets is very GPU-intensive, requiring around 17.7 GB of VRAM, and it runs noticeably slower compared to competitors (our benchmark clocked ~83 seconds per document). For teams with limited compute resources, this can be a bottleneck. On the other hand, if infrastructure isn’t a constraint, you can pair Nanonets with vLLM to dramatically speed up processing, though that’s a more advanced setup.

How to use Nanonets?

Requirements: 

• pip install transformers torch torchvision accelerate pillow pdf2image python-docx

Code:

import os
from PIL import Image
from transformers import AutoTokenizer, AutoProcessor, AutoModelForImageTextToText
from pdf2image import convert_from_path
import torch
import sys
import time


OUTPUT_DIR = "output"
os.makedirs(OUTPUT_DIR, exist_ok=True)


# Load model
model_path = "nanonets/Nanonets-OCR-s"
model = AutoModelForImageTextToText.from_pretrained(model_path, torch_dtype="auto", device_map="auto")
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_path)
processor = AutoProcessor.from_pretrained(model_path)


BASE_PROMPT = (
    "Convert this document into a clean, structured markdown format with headings, lists, tables, "
    "and proper indentation wherever relevant. Remove irrelevant artifacts or OCR errors."
)


def warmup_model():
    """Warmup the model to avoid flawed timings"""
    print("Warming up model...")
    # Create a small dummy image
    dummy_image = Image.new("RGB", (224, 224), color="white")
    dummy_prompt = "Extract text from this image."

    # Run a few warmup iterations
    for i in range(3):
        try:
            _ = process_image(dummy_image, dummy_prompt)
        except Exception as e:
            print(f"Warmup iteration {i+1} failed: {e}")
            continue

    print("Model warmup completed.")


def process_image(image: Image.Image, prompt: str):
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": prompt},
            ],
        },
    ]
    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt").to(model.device)

    output_ids = model.generate(**inputs, max_new_tokens=15000, do_sample=False)
    generated_ids = [output_ids[len(input_ids) :] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
    output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
    return output_text[0]


def handle_pdf(file_path: str):
    images = convert_from_path(file_path, dpi=300)
    all_text = ""
    for image in images:
        text = process_image(image, BASE_PROMPT)
        all_text += text + "\n\n"
    save_path = os.path.join(OUTPUT_DIR, os.path.splitext(os.path.basename(file_path))[0] + ".md")
    with open(save_path, "w") as f:
        f.write(all_text)
    print(f"[✓] Processed PDF: {file_path} -> {save_path}")
    return save_path


def main():
    if len(sys.argv) != 2:
        print("Usage: python run_ocr_nanonets.py <file.pdf>")
        sys.exit(1)

    file_path = sys.argv[1]

    # Check if file exists
    if not os.path.exists(file_path):
        print(f"Error: File '{file_path}' not found.")
        sys.exit(1)

    # Check if file is PDF
    ext = file_path.lower().split(".")[-1]
    if ext != "pdf":
        print("Error: Only PDF files are supported.")
        sys.exit(1)

    # Warmup the model
    warmup_model()

    print(f"Processing PDF: {file_path}")
    start = time.time()
    output_file = handle_pdf(file_path)
    end = time.time()

    print(f"Time taken: {end - start:.2f} seconds")


if __name__ == "__main__":
    main()

Output:

Link: Nanonets Output

Issues: 

• Formatting challenges – While Nanonets captured all the raw data correctly, the tabular markdown formatting was imperfect, so some tables rendered as if entire columns were missing. This means the information is there, but you may need to clean or reformat it before use.
• Performance time – Parsing took around 83.22 seconds per document on our RTX 4090. For large-scale workloads, this delay can become significant.

Pros of Nanonets: 

• Open Source – Free to use and fully transparent, giving developers the freedom to adapt or extend it for custom needs.
• Highly Accurate – Among all open-source models tested, Nanonets delivered the most consistent and detailed extraction, especially with complex documents containing nested tables, footnotes, and multi-section layouts.

Cons of Nanonets:

• GPU Intensive – Requires around 17.7 GB of VRAM, which makes it impractical for smaller setups without high-end hardware.
• Very Slow – Accuracy comes at the cost of speed, making it less suitable for time-sensitive parsing tasks.

5. Gemini-2.5-pro

After testing all five tools on the same document, Gemini-2.5-pro consistently delivered the most reliable results overall. It consistently delivered near-perfect results in our testing, accurately preserving headings, nested tables, footnotes, and complex document structures with minimal errors. Compared to other paid tools, Gemini stands out for its balance of accuracy, speed, and cost-effectiveness.

The best part is accessibility: you can test Gemini for free in Google AI Studio or directly through Gemini before moving into paid API usage. For developers, programmatic access is available with clear documentation, though it requires setting up a Google Cloud project and enabling the Gemini API.

How to use it Gemini-2.5-pro?

Requirements: 

• A Google account (free to start).
• For advanced programmatic access, you’ll need a paid API setup via Google Cloud, but for most users, the web interface is enough.

Prompt:

Convert the contents of this PDF into well-formatted Markdown.
Preserve all structural elements like headings, lists, tables, and paragraphs.
Maintain the original formatting and hierarchy. Ensure the output is clean.

Output:

Link: Gemini-2.5-pro

Pros of Gemini-2.5-pro 

• Easy to use – No complex setup or GPU requirement. You can start parsing right away using Google AI Studio or the Gemini web app, making it accessible even for non-developers.
• Highly accurate – Gemini consistently preserved headings, tables, footnotes, and formatting better than almost every other tool tested, delivering production-ready markdown with minimal cleanup.
• Scalable – Works equally well for single-document use cases and large-scale workloads when paired with the paid API, offering flexibility for teams of any size.

Cons of Gemini-2.5-pro

• Paid solution – While free trials exist, programmatic access through the API requires a Google Cloud account with billing enabled, which can add up at scale.
• Closed source – Unlike Dolphin or Nanonets, you can’t self-host or customize Gemini. You’re dependent on Google’s ecosystem for updates, performance, and pricing.
• Data dependency – Since it’s cloud-based, all processing happens on Google’s servers, which may raise privacy concerns for sensitive documents.

Comparison of Document Parser Tools in 2026

Conclusion

After testing these tools on the same financial document, it became clear that each parser has its own trade-offs. Dolphin is lightweight and open-source but struggles with complex layouts. LandingAI is easy to integrate but becomes expensive at scale. LlamaParse is simple to get started with, though it can miss details in dense financial reports. Nanonets delivers the most accurate extraction among open-source options, but its GPU requirements and slower speed limit where it makes sense. Gemini-2.5-pro consistently offered the best balance of accuracy, speed, and ease of use, as long as a paid, closed-source solution is acceptable.

Based on my testing, Nanonets is the right choice if you need maximum accuracy with full self-hosted control. But if you’re looking for a reliable, scalable parser that works well out of the box, Gemini-2.5-pro stands out as the most practical option.