RRepoGEO

REPOGEO REPORT · LITE

MiniMax-AI/MiniMax-M1

Default branch main · commit 2abb4f45 · scanned 6/24/2026, 10:53:11 AM

GitHub: 3,159 stars · 284 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
27 /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
1 / 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 and clarify the README's main heading

    Why:

    CURRENT
    # MiniMax-M1
    COPY-PASTE FIX
    # MiniMax-M1: The World's First Open-Weight Hybrid-Attention Reasoning LLM
  • mediumreadme#2
    Add a concise, keyword-rich introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    MiniMax-M1 is the world's first open-weight, large-scale hybrid-attention reasoning model. This cutting-edge Large Language Model (LLM) combines a hybrid Mixture-of-Experts (MoE) architecture with novel attention mechanisms to achieve advanced logical problem-solving capabilities, making it ideal for complex reasoning tasks.
  • lowreadme#3
    Add a 'Comparison' or 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., `## 4. Comparison with Other LLMs` or `## 4. Key Differentiators`, explaining how MiniMax-M1 stands out from other open-weight LLMs, specifically highlighting its hybrid-attention and MoE architecture for complex reasoning tasks.

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
Llama 3 70B Instruct
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 3 70B Instruct · recommended 1×
  2. Mixtral 8x22B Instruct · recommended 1×
  3. Llama 3 8B Instruct · recommended 1×
  4. Mixtral 8x7B Instruct · recommended 1×
  5. Gemma 7B Instruct · recommended 1×
  • CATEGORY QUERY
    What open-weight large language models are best for complex reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. Llama 3 70B Instruct
    2. Mixtral 8x22B Instruct
    3. Llama 3 8B Instruct
    4. Mixtral 8x7B Instruct
    5. Gemma 7B Instruct
    6. Qwen 1.5 72B Chat

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

    Show full AI answer
  • CATEGORY QUERY
    Seeking advanced LLMs with hybrid attention for enhanced logical problem-solving.
    you: not recommended
    AI recommended (in order):
    1. Google Gemini
    2. GPT-4
    3. Claude 3
    4. Llama 3
    5. Mistral Large

    AI recommended 5 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 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?

  • 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?

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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