RRepoGEO

REPOGEO REPORT · LITE

QwenLM/Qwen2.5-Omni

Default branch main · commit d8a31ca5 · scanned 6/28/2026, 6:18:06 AM

GitHub: 4,033 stars · 323 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
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 QwenLM/Qwen2.5-Omni, 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
    multimodal-llm, large-language-model, speech-synthesis, real-time-ai, text-to-speech, vision-language-model, audio-language-model, video-language-model, generative-ai, qwen
  • highreadme#2
    Refine the README's opening sentence to emphasize core capabilities and category

    Why:

    CURRENT
    We release **Qwen2.5-Omni**, the new flagship end-to-end multimodal model in the Qwen series. Designed for comprehensive multimodal perception, it seamlessly processes diverse inputs including text, images, audio, and video, while delivering real-time streaming responses through both text generation and natural speech synthesis.
    COPY-PASTE FIX
    Qwen2.5-Omni is the flagship end-to-end multimodal large language model (LLM) from Alibaba Cloud's Qwen team, designed for comprehensive perception across text, images, audio, and video, and capable of delivering real-time streaming responses with both text generation and natural speech synthesis.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://chat.qwenlm.ai/

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/Qwen2.5-Omni
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Azure AI Services
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Azure AI Services · recommended 2×
  2. Azure AI Speech · recommended 2×
  3. VALL-E X · recommended 2×
  4. OpenAI API · recommended 2×
  5. Google Cloud AI Platform / Vertex AI · recommended 1×
  • CATEGORY QUERY
    What AI model can process text, audio, video, and generate speech in real-time?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud AI Platform / Vertex AI
    2. Google Cloud Natural Language API
    3. BERT
    4. T5
    5. Google Cloud Speech-to-Text API
    6. Google Cloud Video AI API
    7. MediaPipe (google/mediapipe)
    8. YOLO
    9. Google Cloud Text-to-Speech API
    10. Azure AI Services
    11. Azure Cognitive Services for Language
    12. Azure Speech-to-Text
    13. Azure AI Speech
    14. Azure Video Analyzer
    15. Azure Computer Vision
    16. YOLOv8 (ultralytics/ultralytics)
    17. OpenPose (CMU-Perceptual-Computing-Lab/openpose)
    18. Azure Text-to-Speech
    19. Azure Machine Learning
    20. Azure Functions
    21. Hugging Face Transformers Ecosystem (huggingface/transformers)
    22. RoBERTa
    23. GPT-3.5
    24. LLaMA 2
    25. OpenAI Whisper (openai/whisper)
    26. AssemblyAI
    27. Deepgram
    28. ViT
    29. Bark (suno-ai/bark)
    30. VALL-E X
    31. Meta Voicebox
    32. ElevenLabs
    33. Resemble AI
    34. FastAPI (tiangolo/fastapi)
    35. NVIDIA Triton Inference Server (triton-inference-server/server)
    36. NVIDIA Riva
    37. DeepStream SDK
    38. ResNet
    39. OpenAI API
    40. GPT-4
    41. GPT-3.5 Turbo
    42. OpenAI Whisper API
    43. OpenAI Text-to-Speech API

    AI recommended 43 alternatives but never named QwenLM/Qwen2.5-Omni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a single AI solution to handle all types of media input and generate speech.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud AI Platform
    2. Google Cloud Vision AI
    3. Google Cloud Video AI
    4. Google Cloud Speech-to-Text
    5. Google Cloud Natural Language AI
    6. Google Cloud Text-to-Speech
    7. Azure AI Services
    8. Azure AI Vision
    9. Azure AI Speech
    10. Azure AI Language
    11. Azure AI Speech (Text-to-Speech)
    12. OpenAI API
    13. DALL-E 3
    14. GPT-4o
    15. OpenAI's Text-to-Speech (TTS) API
    16. AWS AI Services
    17. Amazon Rekognition
    18. Amazon Transcribe
    19. Amazon Comprehend
    20. Amazon Polly
    21. Hugging Face Transformers
    22. Whisper
    23. Llama 3
    24. Mistral
    25. Bark
    26. VALL-E X

    AI recommended 26 alternatives but never named QwenLM/Qwen2.5-Omni. 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 QwenLM/Qwen2.5-Omni?
    pass
    AI named QwenLM/Qwen2.5-Omni explicitly

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

  • If a team adopts QwenLM/Qwen2.5-Omni in production, what risks or prerequisites should they evaluate first?
    pass
    AI named QwenLM/Qwen2.5-Omni 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/Qwen2.5-Omni solve, and who is the primary audience?
    pass
    AI named QwenLM/Qwen2.5-Omni explicitly

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

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QwenLM/Qwen2.5-Omni — 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