REPOGEO REPORT · LITE

MoonshotAI/Kimi-VL

Default branch main · commit 41d5ef07 · scanned 5/18/2026, 11:54:48 AM

GitHub: 1,186 stars · 83 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

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

OVERALL DIRECTION

hightopics#1

Add relevant topics to the repository

Why:

CURRENT

(none)

COPY-PASTE FIX

vision-language-model, vlm, multimodal-ai, mixture-of-experts, moe, long-context, agentic-ai, multimodal-reasoning, ocr, image-understanding, video-understanding, mathematical-reasoning

highhomepage#2
Add a homepage URL to the repository metadata
Why:
CURRENT
```
(none)
```
COPY-PASTE FIX
```
https://arxiv.org/abs/2504.07491
```

mediumreadme#3

Enhance README introduction with specific 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

We present **Kimi-VL**, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers **advanced multimodal reasoning, long-context understanding (up to 200K tokens), and strong agent capabilities**—all while activating only **2.8B** parameters in its language decoder (Kimi-VL-A3B). Kimi-VL excels with high-resolution image processing (up to 2K) and extended text input, setting new pareto frontiers for multimodal models.

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

Of all named tools, what % are you?

Top rival

LLaVA-VL/LLaVA

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

LLaVA-VL/LLaVA · recommended 1×
THUDM/CogVLM · recommended 1×
QwenLM/Qwen-VL · recommended 1×
salesforce/LAVIS · recommended 1×
lm-sys/FastChat · recommended 1×

CATEGORY QUERY
What open-source vision-language models excel at long-context multimodal reasoning for agentic tasks?
you: not recommended
AI recommended (in order):
1. LLaVA-NeXT (LLaVA-1.6) (LLaVA-VL/LLaVA)
2. CogVLM (THUDM/CogVLM)
3. Qwen-VL-Max (or Qwen-VL-Chat) (QwenLM/Qwen-VL)
4. InstructBLIP (salesforce/LAVIS)
5. Vicuna-13B (lm-sys/FastChat)
6. Llama-2-70B (facebookresearch/llama)
7. MiniGPT-4 (Vision-CAIR/MiniGPT-4)
AI recommended 7 alternatives but never named MoonshotAI/Kimi-VL. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I build an AI agent for complex image, video, and mathematical reasoning?
you: not recommended
AI recommended (in order):
1. PyTorch
2. torchvision
3. TensorFlow
4. Keras
5. TensorFlow Extended (TFX)
6. JAX
7. Haiku
8. Flax
9. MXNet
10. Julia
11. Flux.jl
12. OpenCV
13. NumPy
14. SciPy
15. SymPy
16. Hugging Face Transformers
17. Detectron2
18. MMDetection
AI recommended 18 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)

[![RepoGEO](https://repogeo.com/badge/MoonshotAI/Kimi-VL.svg)](https://repogeo.com/en/r/MoonshotAI/Kimi-VL)

HTML

<a href="https://repogeo.com/en/r/MoonshotAI/Kimi-VL"><img src="https://repogeo.com/badge/MoonshotAI/Kimi-VL.svg" alt="RepoGEO" /></a>

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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