RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/DeepSeek-VL

Default branch main · commit 681bffb4 · scanned 5/20/2026, 10:58:01 PM

GitHub: 4,108 stars · 593 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 deepseek-ai/DeepSeek-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
    Integrate core differentiator into README introduction

    Why:

    CURRENT
    Introducing DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. DeepSeek-VL possesses general multimodal understanding capabilities, capable of processing logical diagrams, web pages, formula recognition, scientific literature, natural images, and embodied intellige
    COPY-PASTE FIX
    Introducing DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. DeepSeek-VL stands out for its strong balance of high performance across diverse vision-language tasks (especially complex multimodal reasoning) and computational efficiency, all within a fully open-source model with a permissive license. It possesses general multimodal understanding capabilities, capable of processing logical diagrams, web pages, formula recognition, scientific literature, natural images, and embodied intelligence.
  • mediumtopics#2
    Add 'multimodal-ai' to repository topics

    Why:

    CURRENT
    foundation-models, vision-language-model, vision-language-pretraining
    COPY-PASTE FIX
    foundation-models, vision-language-model, vision-language-pretraining, multimodal-ai
  • lowreadme#3
    Add a 'Why DeepSeek-VL?' or 'Comparison' section to README

    Why:

    COPY-PASTE FIX
    ## Why DeepSeek-VL?
    DeepSeek-VL offers a unique combination of high performance across diverse vision-language tasks, particularly complex multimodal reasoning, with strong computational efficiency. As a fully open-source model with a permissive license, it provides a robust and flexible foundation for real-world AI applications, distinguishing it from other models in the ecosystem.

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 deepseek-ai/DeepSeek-VL
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LLaVA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LLaVA · recommended 2×
  2. BLIP-2 · recommended 2×
  3. CoCa · recommended 2×
  4. CLIP · recommended 2×
  5. InstructBLIP · recommended 1×
  • CATEGORY QUERY
    What open-source models can help me with real-world vision-language understanding tasks?
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. BLIP-2
    3. InstructBLIP
    4. MiniGPT-4
    5. OpenFlamingo
    6. CoCa
    7. CLIP

    AI recommended 7 alternatives but never named deepseek-ai/DeepSeek-VL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    I need a pre-trained vision-language foundation model for advanced multimodal AI applications.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini
    3. Claude 3 Opus/Sonnet
    4. LLaVA
    5. BLIP-2
    6. CoCa
    7. CLIP

    AI recommended 7 alternatives but never named deepseek-ai/DeepSeek-VL. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 deepseek-ai/DeepSeek-VL?
    pass
    AI named deepseek-ai/DeepSeek-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 deepseek-ai/DeepSeek-VL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deepseek-ai/DeepSeek-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 deepseek-ai/DeepSeek-VL solve, and who is the primary audience?
    pass
    AI named deepseek-ai/DeepSeek-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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deepseek-ai/DeepSeek-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