RRepoGEO

REPOGEO REPORT · LITE

MiniMax-AI/MiniMax-M2

Default branch main · commit 2e575efe · scanned 5/25/2026, 3:37:23 AM

GitHub: 2,593 stars · 214 forks

AI VISIBILITY SCORE
40 /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
3 / 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-M2, 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's opening statement to clarify the project's identity

    Why:

    COPY-PASTE FIX
    MiniMax-M2 is a powerful multimodal large language model (MLLM) designed for advanced coding and agentic workflows, supporting text, image, and audio understanding. It excels at generating and refactoring code efficiently, and is built for autonomous agent development and complex task execution.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    large-language-models, llm
    COPY-PASTE FIX
    large-language-models, llm, multimodal-llm, code-generation, ai-agents, agentic-workflows, text-to-image, text-to-audio
  • mediumreadme#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    This project is released under the terms specified in the `LICENSE` file. Please refer to the `LICENSE` file for full details on usage and distribution.

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-M2
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 2×
  2. Claude 3 Opus · recommended 2×
  3. Gemini 1.5 Pro · recommended 2×
  4. Code Llama · recommended 1×
  5. GPT-3.5 Turbo · recommended 1×
  • CATEGORY QUERY
    What are the best large language models for generating and refactoring code efficiently?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Gemini 1.5 Pro
    4. Code Llama
    5. GPT-3.5 Turbo
    6. DeepSeek Coder

    AI recommended 6 alternatives but never named MiniMax-AI/MiniMax-M2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an LLM that excels at autonomous agent development and complex task execution.
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Gemini 1.5 Pro
    4. Llama 3
    5. Mixtral 8x7B

    AI recommended 5 alternatives but never named MiniMax-AI/MiniMax-M2. 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-M2?
    pass
    AI named MiniMax-AI/MiniMax-M2 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-M2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MiniMax-AI/MiniMax-M2 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-M2 solve, and who is the primary audience?
    pass
    AI named MiniMax-AI/MiniMax-M2 explicitly

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

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MARKDOWN (README)
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MiniMax-AI/MiniMax-M2 — 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