RRepoGEO

REPOGEO REPORT · LITE

Open-Source-O1/Open-O1

Default branch main · commit c2e47884 · scanned 5/26/2026, 9:23:22 AM

GitHub: 1,342 stars · 54 forks

AI VISIBILITY SCORE
30 /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
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 Open-Source-O1/Open-O1, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    An open-source large language model (LLM) designed to match proprietary reasoning capabilities, focusing on enhanced long-reasoning and problem-solving through SFT data for CoT Activation.
  • mediumreadme#2
    Clarify the README's opening to emphasize LLM reasoning and counter multimodal misinterpretation

    Why:

    CURRENT
    # Open O1: A Model Matching Proprietary Power with Open-Source Innovation

Our **Open O1** aims to match the powerful capabilities of the proprietary OpenAI O1 model, empowering the community with advanced open-source alternatives. Our model has been developed by curating a set **SFT data for CoT Activation**, which was then used to train both LLaMA and Qwen models. This training approach has endowed the smaller models with enhanced long-reasoning and problem-solving capabilities.
    COPY-PASTE FIX
    # Open O1: An Open-Source Large Language Model for Advanced Reasoning

Open O1 is an open-source large language model (LLM) project dedicated to matching the advanced reasoning and problem-solving capabilities of proprietary models like OpenAI O1. We achieve this by curating SFT data for CoT Activation, which is then used to train models like LLaMA and Qwen, endowing them with enhanced long-reasoning abilities. Our mission is to empower the community with powerful, accessible AI for complex 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 Open-Source-O1/Open-O1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 3 · recommended 1×
  2. Mixtral 8x7B · recommended 1×
  3. Gemma · recommended 1×
  4. Qwen · recommended 1×
  5. Falcon · recommended 1×
  • CATEGORY QUERY
    What open-source large language models provide advanced reasoning capabilities comparable to proprietary solutions?
    you: not recommended
    AI recommended (in order):
    1. Llama 3
    2. Mixtral 8x7B
    3. Gemma
    4. Qwen
    5. Falcon
    6. Yi

    AI recommended 6 alternatives but never named Open-Source-O1/Open-O1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I enhance smaller open-source language models for complex long-reasoning and problem-solving?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Elasticsearch (elastic/elasticsearch)
    3. Pinecone
    4. Weaviate (weaviate/weaviate)
    5. LangChain (langchain-ai/langchain)
    6. LlamaIndex (run-llama/llama_index)
    7. YaRN (Yet another RoPE-scaling method)
    8. GPT-4
    9. Claude 3 Opus
    10. TRL (Transformer Reinforcement Learning) (huggingface/trl)

    AI recommended 10 alternatives but never named Open-Source-O1/Open-O1. 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 Open-Source-O1/Open-O1?
    pass
    AI named Open-Source-O1/Open-O1 explicitly

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

  • If a team adopts Open-Source-O1/Open-O1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Open-Source-O1/Open-O1 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 Open-Source-O1/Open-O1 solve, and who is the primary audience?
    pass
    AI named Open-Source-O1/Open-O1 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 Open-Source-O1/Open-O1. 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)
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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite