RRepoGEO

REPOGEO REPORT · LITE

QwenLM/Qwen3-Embedding

Default branch main · commit 44548aa5 · scanned 5/24/2026, 5:47:51 AM

GitHub: 1,931 stars · 123 forks

AI VISIBILITY SCORE
23 /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
2 / 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 QwenLM/Qwen3-Embedding, 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
    Qwen3 Embedding is a series of proprietary models for text embedding and ranking, offering state-of-the-art performance in multilingual semantic search, RAG, and classification tasks.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the chosen open-source license (e.g., Apache-2.0, MIT) that aligns with the project's intent for 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 QwenLM/Qwen3-Embedding
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Embeddings
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Embeddings · recommended 1×
  2. UKP-LABS/sentence-transformers · recommended 1×
  3. all-MiniLM-L6-v2 · recommended 1×
  4. Cohere Embed · recommended 1×
  5. E5 · recommended 1×
  • CATEGORY QUERY
    What are the best text embedding models for semantic search and information retrieval tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Embeddings
    2. Sentence-BERT (SBERT) (UKP-LABS/sentence-transformers)
    3. all-MiniLM-L6-v2
    4. Cohere Embed
    5. E5
    6. GTE
    7. Voyage AI
    8. Instructor-XL (HKUNLP/instructor-embedding)

    AI recommended 8 alternatives but never named QwenLM/Qwen3-Embedding. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a multilingual text embedding model with strong long-text understanding capabilities.
    you: not recommended
    AI recommended (in order):
    1. Cohere Embed v3 (multilingual)
    2. OpenAI `text-embedding-3-large`
    3. E5-Mistral-7B-instruct
    4. XLM-RoBERTa-large (XLM-R)
    5. LaBSE (Language-agnostic BERT Sentence Embedding)
    6. mBERT (Multilingual BERT)

    AI recommended 6 alternatives but never named QwenLM/Qwen3-Embedding. 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 QwenLM/Qwen3-Embedding?
    pass
    AI did not name QwenLM/Qwen3-Embedding — 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 QwenLM/Qwen3-Embedding in production, what risks or prerequisites should they evaluate first?
    pass
    AI named QwenLM/Qwen3-Embedding 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 QwenLM/Qwen3-Embedding solve, and who is the primary audience?
    pass
    AI named QwenLM/Qwen3-Embedding 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 QwenLM/Qwen3-Embedding. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/QwenLM/Qwen3-Embedding.svg)](https://repogeo.com/en/r/QwenLM/Qwen3-Embedding)
HTML
<a href="https://repogeo.com/en/r/QwenLM/Qwen3-Embedding"><img src="https://repogeo.com/badge/QwenLM/Qwen3-Embedding.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

QwenLM/Qwen3-Embedding — 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