RRepoGEO

REPOGEO REPORT · LITE

MiniMax-AI/MiniMax-M1

Default branch main · commit 2abb4f45 · scanned 5/13/2026, 11:48:00 PM

GitHub: 3,151 stars · 281 forks

AI VISIBILITY SCORE
33 /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
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 MiniMax-AI/MiniMax-M1, 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
    Reposition the README H1 to specify category and prevent misinterpretation

    Why:

    CURRENT
    # MiniMax-M1
    COPY-PASTE FIX
    # MiniMax-M1: The World's First Open-Weight, Large-Scale Hybrid-Attention Reasoning Model
  • mediumreadme#2
    Enhance README introduction to highlight advanced reasoning and novel attention mechanisms

    Why:

    CURRENT
    We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.
    COPY-PASTE FIX
    MiniMax-M1 is the world's first open-weight, large-scale hybrid-attention reasoning model, specifically designed to excel at advanced logical reasoning and complex inference tasks through its novel attention mechanisms.
  • lowtopics#3
    Expand repository topics for better categorization and keyword matching

    Why:

    CURRENT
    large-language-models, llm, minimax-m1, reasoning-models
    COPY-PASTE FIX
    large-language-models, llm, minimax-m1, reasoning-models, hybrid-attention, open-weight-llm, ai-reasoning

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 MiniMax-AI/MiniMax-M1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Claude 3 Opus
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Claude 3 Opus · recommended 1×
  2. GPT-4 · recommended 1×
  3. Llama 3 70B Instruct · recommended 1×
  4. Mixtral 8x7B Instruct · recommended 1×
  5. Qwen 1.5 72B Chat · recommended 1×
  • CATEGORY QUERY
    Which open-weight large language models excel at advanced logical reasoning capabilities?
    you: not recommended
    AI recommended (in order):
    1. Claude 3 Opus
    2. GPT-4
    3. Llama 3 70B Instruct
    4. Mixtral 8x7B Instruct
    5. Qwen 1.5 72B Chat
    6. Gemma 7B Instruct

    AI recommended 6 alternatives but never named MiniMax-AI/MiniMax-M1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a powerful LLM utilizing novel attention mechanisms for complex inference tasks.
    you: not recommended
    AI recommended (in order):
    1. Google Gemini
    2. Anthropic Claude 3
    3. OpenAI GPT-4
    4. Mistral Large / Mixtral 8x7B
    5. Perplexity Labs' models
    6. Databricks DBRX

    AI recommended 6 alternatives but never named MiniMax-AI/MiniMax-M1. 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 MiniMax-AI/MiniMax-M1?
    pass
    AI named MiniMax-AI/MiniMax-M1 explicitly

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

  • If a team adopts MiniMax-AI/MiniMax-M1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MiniMax-AI/MiniMax-M1 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 MiniMax-AI/MiniMax-M1 solve, and who is the primary audience?
    pass
    AI did not name MiniMax-AI/MiniMax-M1 — 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?

Embed your GEO score

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MARKDOWN (README)
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MiniMax-AI/MiniMax-M1 — 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