RRepoGEO

REPOGEO REPORT · LITE

QwenLM/Qwen-VL

Default branch master · commit aa00ed04 · scanned 5/30/2026, 7:33:13 AM

GitHub: 6,660 stars · 488 forks

AI VISIBILITY SCORE
54 /100
Needs work
Category recall
1 / 2
Avg rank #6.0 when recommended
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 QwenLM/Qwen-VL, 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
    Clarify Qwen-VL's suitability for building conversational AI applications

    Why:

    COPY-PASTE FIX
    Add a sentence early in the README, perhaps after the model links, like: "Qwen-VL provides powerful, open-source vision-language models, including Qwen-VL-Chat, designed for seamless integration into conversational AI applications and multimodal understanding tasks."
  • highhomepage#2
    Add official homepage URL to repository metadata

    Why:

    COPY-PASTE FIX
    https://tongyi.aliyun.com/qianwen
  • mediumlicense#3
    Clarify the specific license(s) in the README

    Why:

    COPY-PASTE FIX
    Add a section or line in the README, e.g., 'This project is licensed under [Specify License Name(s) and terms, e.g., 'the Apache 2.0 License for code and CC-BY-NC-4.0 for model weights']. Please refer to the LICENSE file for full details.'

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
1 / 2
50% of queries surface QwenLM/Qwen-VL
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
LLaVA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LLaVA · recommended 2×
  2. Fuyu-8B · recommended 2×
  3. CogVLM · recommended 2×
  4. GPT-4V · recommended 1×
  5. OpenAI API · recommended 1×
  • CATEGORY QUERY
    How can I integrate a powerful vision language model for conversational AI applications?
    you: not recommended
    AI recommended (in order):
    1. GPT-4V
    2. OpenAI API
    3. Google Gemini
    4. Google AI Studio
    5. Vertex AI API
    6. Llama 3
    7. LLaVA
    8. Fuyu-8B
    9. BLIP-2
    10. CogVLM
    11. InstructBLIP

    AI recommended 11 alternatives but never named QwenLM/Qwen-VL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source large vision language models for multimodal understanding tasks?
    you: #6
    AI recommended (in order):
    1. LLaVA
    2. CogVLM
    3. Fuyu-8B
    4. MiniGPT-4 / MiniGPT-v2
    5. BakLLaVA
    6. Qwen-VL ← you
    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 QwenLM/Qwen-VL?
    pass
    AI named QwenLM/Qwen-VL explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts QwenLM/Qwen-VL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named QwenLM/Qwen-VL 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 QwenLM/Qwen-VL solve, and who is the primary audience?
    pass
    AI named QwenLM/Qwen-VL explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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QwenLM/Qwen-VL — 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