RRepoGEO

REPOGEO REPORT · LITE

MoonshotAI/Kimi-VL

Default branch main · commit 41d5ef07 · scanned 6/29/2026, 5:57:59 PM

GitHub: 1,201 stars · 85 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
28 /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
2 / 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 MoonshotAI/Kimi-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

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

OVERALL DIRECTION
  • mediumhomepage#1
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://huggingface.co/collections/moonshotai/kimi-vl-a3b-67f67b6ac91d3b03d382dd85
  • mediumreadme#2
    Enhance the README's introductory sentence to highlight core differentiators

    Why:

    CURRENT
    We present **Kimi-VL**, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers **advanced multimodal reasoning, long-context understanding, and strong agent capabilities**—all while activating only **2.8B** parameters in its language decoder (Kimi-VL-A3B).
    COPY-PASTE FIX
    MoonshotAI/Kimi-VL is an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) designed for **state-of-the-art multimodal reasoning, long-context understanding, and robust agent capabilities**, excelling in complex tasks like multi-turn agent interactions and high-resolution image/video comprehension.

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 MoonshotAI/Kimi-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. CogVLM · recommended 2×
  3. Fuyu-8B · recommended 2×
  4. BakLLaVA · recommended 1×
  5. MiniGPT-4 · recommended 1×
  • CATEGORY QUERY
    What open-source vision-language models provide strong multimodal reasoning and long-context understanding for agents?
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. CogVLM
    3. Fuyu-8B
    4. BakLLaVA
    5. MiniGPT-4
    6. Qwen-VL

    AI recommended 6 alternatives but never named MoonshotAI/Kimi-VL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which efficient vision models excel at complex image, video comprehension, and OCR for agent interactions?
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini 1.5 Pro
    3. LLaVA
    4. InternVL
    5. OWL-ViT
    6. PaddleOCR
    7. Fuyu-8B
    8. CogVLM

    AI recommended 8 alternatives but never named MoonshotAI/Kimi-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
    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 MoonshotAI/Kimi-VL?
    pass
    AI did not name MoonshotAI/Kimi-VL — likely talking about a different project

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

  • If a team adopts MoonshotAI/Kimi-VL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MoonshotAI/Kimi-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 MoonshotAI/Kimi-VL solve, and who is the primary audience?
    pass
    AI named MoonshotAI/Kimi-VL 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 MoonshotAI/Kimi-VL. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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MoonshotAI/Kimi-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