RRepoGEO

REPOGEO REPORT · LITE

meituan-longcat/LongCat-Flash-Chat

Default branch main · commit e929faeb · scanned 7/1/2026, 9:52:57 AM

GitHub: 1,341 stars · 67 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
23 /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
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 meituan-longcat/LongCat-Flash-Chat, 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
  • highabout#1
    Add a concise project description

    Why:

    COPY-PASTE FIX
    LongCat-Flash-Chat is a powerful and efficient language model with an innovative Mixture-of-Experts (MoE) architecture, designed for cost-effective, high-speed inference in conversational AI applications.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ["large-language-model", "llm", "mixture-of-experts", "moe", "conversational-ai", "flash-attention", "efficient-inference", "chat-model"]
  • mediumreadme#3
    Add a clear, concise summary sentence directly under the main heading

    Why:

    CURRENT
    # LongCat-Flash-Chat

<div align="center">
    COPY-PASTE FIX
    # LongCat-Flash-Chat

LongCat-Flash-Chat is a powerful and efficient language model featuring an innovative Mixture-of-Experts (MoE) architecture, optimized for high-speed, cost-effective inference in real-time conversational AI applications.

<div align="center">

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 meituan-longcat/LongCat-Flash-Chat
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mixtral 8x7B
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Mixtral 8x7B · recommended 1×
  2. Grok-1 · recommended 1×
  3. Qwen1.5-MoE · recommended 1×
  4. DeepSeek-MoE · recommended 1×
  5. Switch Transformers · recommended 1×
  • CATEGORY QUERY
    What are the best large language models using Mixture-of-Experts for efficient processing?
    you: not recommended
    AI recommended (in order):
    1. Mixtral 8x7B
    2. Grok-1
    3. Qwen1.5-MoE
    4. DeepSeek-MoE
    5. Switch Transformers

    AI recommended 5 alternatives but never named meituan-longcat/LongCat-Flash-Chat. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking powerful and efficient language models for building responsive conversational AI applications.
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 (and GPT-4 Turbo)
    2. Anthropic Claude 3 (Opus, Sonnet, Haiku)
    3. Google Gemini (Pro, 1.5 Pro)
    4. Meta Llama 3 (8B, 70B)
    5. Mistral AI (Mistral 7B, Mixtral 8x7B)
    6. Cohere Command R/R+

    AI recommended 6 alternatives but never named meituan-longcat/LongCat-Flash-Chat. 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 meituan-longcat/LongCat-Flash-Chat?
    pass
    AI did not name meituan-longcat/LongCat-Flash-Chat — 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 meituan-longcat/LongCat-Flash-Chat in production, what risks or prerequisites should they evaluate first?
    pass
    AI named meituan-longcat/LongCat-Flash-Chat 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 meituan-longcat/LongCat-Flash-Chat solve, and who is the primary audience?
    pass
    AI named meituan-longcat/LongCat-Flash-Chat explicitly

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

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meituan-longcat/LongCat-Flash-Chat — 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