RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/DeepSeek-MoE

Default branch main · commit 66edeee5 · scanned 6/30/2026, 7:28:20 AM

GitHub: 1,946 stars · 308 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
63 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
Rule findings
1 pass · 1 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 deepseek-ai/DeepSeek-MoE, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ["llm", "mixture-of-experts", "moe", "sparse-model", "deep-learning", "language-model", "ai", "efficient-inference"]
  • highreadme#2
    Explicitly mention sparsity and inference cost reduction in the README introduction

    Why:

    CURRENT
    DeepSeekMoE 16B is a Mixture-of-Experts (MoE) language model with 16.4B parameters. It employs an innovative MoE architecture, which involves two principal strategies: fine-grained expert segmentation and shared experts isolation. It is trained from scratch on 2T English and Chinese tokens, and exhibits comparable performance with DeekSeek 7B and LLaMA2 7B, with only about 40% of computations.
    COPY-PASTE FIX
    DeepSeekMoE 16B is a high-performance, sparse Mixture-of-Experts (MoE) language model with 16.4B parameters, designed to significantly reduce inference costs. It employs an innovative MoE architecture, which involves two principal strategies: fine-grained expert segmentation and shared experts isolation. Trained from scratch on 2T English and Chinese tokens, DeepSeekMoE exhibits comparable performance with DeekSeek 7B and LLaMA2 7B, achieving this with only about 40% of the computational resources.
  • mediumhomepage#3
    Add the official project homepage URL

    Why:

    CURRENT
    (none)
    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
1 / 2
50% of queries surface deepseek-ai/DeepSeek-MoE
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
OpenMoE
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenMoE · recommended 2×
  2. Mixtral 8x7B · recommended 1×
  3. Qwen1.5-MoE · recommended 1×
  4. Switch Transformers · recommended 1×
  5. DeepMind's GLaM · recommended 1×
  • CATEGORY QUERY
    Looking for an open-source Mixture-of-Experts large language model for research.
    you: #3
    AI recommended (in order):
    1. Mixtral 8x7B
    2. Qwen1.5-MoE
    3. DeepSeek-MoE ← you
    4. OpenMoE
    5. Switch Transformers
    Show full AI answer
  • CATEGORY QUERY
    Need a high-performance sparse expert model to reduce inference costs for LLMs.
    you: not recommended
    AI recommended (in order):
    1. DeepMind's GLaM
    2. Fairseq
    3. Megatron-LM
    4. Hugging Face Transformers
    5. OpenMoE
    6. TensorFlow
    7. PyTorch

    AI recommended 7 alternatives but never named deepseek-ai/DeepSeek-MoE. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    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-MoE?
    pass
    AI named deepseek-ai/DeepSeek-MoE 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-MoE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deepseek-ai/DeepSeek-MoE 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-MoE solve, and who is the primary audience?
    pass
    AI named deepseek-ai/DeepSeek-MoE 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-MoE — 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