REPOGEO REPORT · LITE

X-PLUG/mPLUG-DocOwl

Default branch main · commit f91a7685 · scanned 5/15/2026, 8:38:16 AM

GitHub: 2,406 stars · 154 forks

AI VISIBILITY SCORE

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface X-PLUG/mPLUG-DocOwl, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README opening to highlight specialization and open-source nature

Why:

COPY-PASTE FIX

X-PLUG/mPLUG-DocOwl is an open-source, specialized Multimodal Large Language Model (MLLM) designed for advanced OCR-free document understanding. It excels in interpreting complex visual documents, including tables and charts, across multiple pages, offering a powerful alternative to general-purpose MLLMs and commercial document AI solutions.

mediumhomepage#2
Add a project homepage URL
Why:
COPY-PASTE FIX
```
https://[your-project-homepage-url-here]
```

lowreadme#3

Add a 'Why mPLUG-DocOwl?' section

Why:

COPY-PASTE FIX

## Why mPLUG-DocOwl?

Unlike general-purpose Multimodal Large Language Models (MLLMs) or commercial document AI services, mPLUG-DocOwl offers a specialized, unified multimodal approach for comprehensive OCR-free document understanding. Key differentiators include:
*   **Specialized for Complex Documents:** Optimized for intricate visual documents, including tables and charts, across multiple pages.
*   **Efficiency:** Achieves state-of-the-art performance with highly efficient token encoding (e.g., 324 tokens per document image for DocOwl2).
*   **Open-Source & Research-Driven:** Provides training code and models for finetuning, fostering research and custom applications.
*   **SOTA Performance:** Demonstrated leading results on benchmarks like ChartQA (TinyChart: 83.6 > Gemini-Ultra 80.8 > GPT4V 78.5).

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface X-PLUG/mPLUG-DocOwl

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Gemini 1.5 Pro

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Gemini 1.5 Pro · recommended 2×
GPT-4o · recommended 1×
Claude 3 Opus/Sonnet · recommended 1×
LLaVA-Med/LLaVA · recommended 1×
Fuyu-8B · recommended 1×

CATEGORY QUERY
What are the best multimodal large language models for OCR-free document understanding?
you: not recommended
AI recommended (in order):
1. GPT-4o
2. Gemini 1.5 Pro
3. Claude 3 Opus/Sonnet
4. LLaVA (Large Language and Vision Assistant) (LLaVA-Med/LLaVA)
5. Fuyu-8B
6. Donut (Document Understanding Transformer) (naver-ai/donut)
AI recommended 6 alternatives but never named X-PLUG/mPLUG-DocOwl. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I extract information from tables and charts in multipage documents using AI?
you: not recommended
AI recommended (in order):
1. Google Cloud Document AI
2. Azure AI Document Intelligence
3. Amazon Textract
4. OpenAI GPT-4V (Vision)
5. LLaVA
6. Gemini 1.5 Pro
7. LayoutParser
8. Tesseract OCR
9. PaddleOCR
10. Pandas
11. camelot-py
12. tabula-py
13. Nanonets
14. Rossum
AI recommended 14 alternatives but never named X-PLUG/mPLUG-DocOwl. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of X-PLUG/mPLUG-DocOwl?
pass
AI named X-PLUG/mPLUG-DocOwl explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts X-PLUG/mPLUG-DocOwl in production, what risks or prerequisites should they evaluate first?
pass
AI named X-PLUG/mPLUG-DocOwl explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo X-PLUG/mPLUG-DocOwl solve, and who is the primary audience?
pass
AI named X-PLUG/mPLUG-DocOwl explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of X-PLUG/mPLUG-DocOwl. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/X-PLUG/mPLUG-DocOwl.svg)](https://repogeo.com/en/r/X-PLUG/mPLUG-DocOwl)

HTML

<a href="https://repogeo.com/en/r/X-PLUG/mPLUG-DocOwl"><img src="https://repogeo.com/badge/X-PLUG/mPLUG-DocOwl.svg" alt="RepoGEO" /></a>

Pro

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X-PLUG/mPLUG-DocOwl — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite