RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/DeepSeek-V3.2-Exp

Default branch main · commit 87e509a2 · scanned 6/30/2026, 11:47:56 AM

GitHub: 1,608 stars · 180 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
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 deepseek-ai/DeepSeek-V3.2-Exp, 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 repository description

    Why:

    COPY-PASTE FIX
    DeepSeek-V3.2-Exp is an experimental large language model (LLM) featuring DeepSeek Sparse Attention for improved training and inference efficiency in long-context scenarios.
  • hightopics#2
    Add specific topics for LLMs, sparse attention, and efficiency

    Why:

    COPY-PASTE FIX
    large-language-model, llm, sparse-attention, deep-learning, artificial-intelligence, model-optimization, transformer, moes, mixture-of-experts
  • mediumhomepage#3
    Add the official DeepSeek AI homepage URL

    Why:

    COPY-PASTE FIX
    https://www.deepseek.com/

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 deepseek-ai/DeepSeek-V3.2-Exp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TimDettmers/bitsandbytes
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TimDettmers/bitsandbytes · recommended 1×
  2. microsoft/onnxruntime · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. NVIDIA/apex · recommended 1×
  • CATEGORY QUERY
    What are the benefits of using a sparse attention mechanism in large language models?
    you: not recommended
    Show full AI answer
  • CATEGORY QUERY
    How can I optimize the performance and efficiency of my large language models?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes (TimDettmers/bitsandbytes)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. Hugging Face Transformers (huggingface/transformers)
    4. PyTorch (pytorch/pytorch)
    5. NVIDIA Apex (NVIDIA/apex)
    6. Llama 2
    7. Mistral
    8. Falcon
    9. NVIDIA TensorRT
    10. OpenVINO (openvinotoolkit/openvino)
    11. DeepSpeed (microsoft/DeepSpeed)
    12. FlashAttention (Dao-AILab/flash-attention)
    13. xFormers (facebookresearch/xformers)

    AI recommended 13 alternatives but never named deepseek-ai/DeepSeek-V3.2-Exp. 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 deepseek-ai/DeepSeek-V3.2-Exp?
    pass
    AI named deepseek-ai/DeepSeek-V3.2-Exp explicitly

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

  • If a team adopts deepseek-ai/DeepSeek-V3.2-Exp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deepseek-ai/DeepSeek-V3.2-Exp 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 deepseek-ai/DeepSeek-V3.2-Exp solve, and who is the primary audience?
    pass
    AI named deepseek-ai/DeepSeek-V3.2-Exp explicitly

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

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deepseek-ai/DeepSeek-V3.2-Exp — 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