RRepoGEO

REPOGEO REPORT · LITE

QwenLM/Qwen3-VL-Embedding

Default branch main · commit c27c3a8b · scanned 5/20/2026, 6:38:41 PM

GitHub: 1,241 stars · 103 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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/Qwen3-VL-Embedding, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    State-of-the-art multimodal embedding and reranking models built on Qwen3-VL, supporting text, images, screenshots, videos, and mixed-modal inputs for advanced information retrieval and cross-modal understanding.
  • mediumreadme#2
    Slightly rephrase the README's opening sentence for clearer solution positioning

    Why:

    CURRENT
    **State-of-the-art multimodal embedding and reranking models built on Qwen3-VL, supporting text, images, screenshots, videos, and mixed-modal inputs for advanced information retrieval and cross-modal understanding.**
    COPY-PASTE FIX
    Qwen3-VL-Embedding and Qwen3-VL-Reranker provide state-of-the-art solutions for multimodal information retrieval and cross-modal understanding, generating embeddings and reranking results from text, images, screenshots, videos, and mixed-modal inputs, all built on Qwen3-VL.

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 QwenLM/Qwen3-VL-Embedding
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI CLIP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI CLIP · recommended 1×
  2. OpenCLIP · recommended 1×
  3. Google PaLM-E · recommended 1×
  4. Flamingo · recommended 1×
  5. Meta ImageBind · recommended 1×
  • CATEGORY QUERY
    How can I generate embeddings for mixed inputs like text, images, and video for retrieval?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. OpenCLIP
    3. Google PaLM-E
    4. Flamingo
    5. Meta ImageBind
    6. Hugging Face Transformers
    7. ViLT
    8. ALBEF
    9. PyTorch Video
    10. R3D_18
    11. MC3_18
    12. R2PLUS1D_18
    13. BERT
    14. Sentence-BERT
    15. Weaviate
    16. Pinecone
    17. Qdrant
    18. FAISS
    19. Annoy

    AI recommended 19 alternatives but never named QwenLM/Qwen3-VL-Embedding. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help with reranking search results from multimodal inputs for better relevance?
    you: not recommended
    AI recommended (in order):
    1. Haystack (deepset-ai/haystack)
    2. Jina (jina-ai/jina)
    3. Faiss (facebookresearch/faiss)
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)
    6. Hugging Face Transformers (huggingface/transformers)
    7. Weaviate (weaviate/weaviate)
    8. Elasticsearch (elastic/elasticsearch)
    9. OpenSource Learning to Rank for Elasticsearch (o19s/elasticsearch-ltr)
    10. Milvus (milvus-io/milvus)
    11. Zilliz Cloud

    AI recommended 11 alternatives but never named QwenLM/Qwen3-VL-Embedding. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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/Qwen3-VL-Embedding?
    pass
    AI named QwenLM/Qwen3-VL-Embedding 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/Qwen3-VL-Embedding in production, what risks or prerequisites should they evaluate first?
    pass
    AI named QwenLM/Qwen3-VL-Embedding 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/Qwen3-VL-Embedding solve, and who is the primary audience?
    pass
    AI named QwenLM/Qwen3-VL-Embedding explicitly

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

Embed your GEO score

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QwenLM/Qwen3-VL-Embedding — 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