Gemma 3 27B vs Qwen2.5-VL: Best for AI Photo Search!

Qwen 3 32B VS QWQ 32B is not just a comparison of size — it’s a comparison of flexibility, control, and deployment strategy. While both offer “thinking mode” for complex reasoning, Qwen 3 32B stands out with its customizable reasoning depth and broader application reach.

Gemma 3 27B vs Qwen2.5-VL-72B: Tasks

Prompt: what is sports in this photo?

Gemma 3 27B

The sport in the photo is table tennis, also known as ping pong.. You can tell by the player holding a paddle, the table tennis table, and the net. The background also indicates a professional table tennis competition (“World Table Tennis Championships”).

Strengths:

• Focuses on object-level recognition: paddle, table, net — clear signs of effective visual parsing.
• Identifies textual cues in the background (“World Table Tennis Championships”), indicating solid OCR and context anchoring.

Limitations:

• Description is somewhat static — focuses on objects but not the player’s dynamic action or scene interpretation.

Qwen2.5-VL-72B

The sport in the photo is table tennis, also known as ping pong. The player is actively engaged in a match, holding a paddle and preparing to hit the ball. The setting appears to be a professional tournament, as indicated by the signage and the formal attire of the player.

Strengths:

• Goes beyond object detection to describe player action (“actively engaged”, “preparing to hit”).
• Uses scene semantics and human reasoning — inferring professional context from signage and attire.
• Natural, human-like phrasing, good for conversational or assistant-style applications.

Limitations:

• More interpretive — suitable for natural Q&A, but may be less direct in object classification scenarios.

Comparison Table

Criterion	Gemma 3 27B	Qwen2.5-VL-72B
Object Recognition	✅ Accurate and clear	✅ Accurate
Action Interpretation	⚠️ Limited	✅ Strong (describes player movement)
Scene Reasoning	✅ Basic (based on visible text)	✅✅ Advanced (infers from context clues)
Language Naturalness	Neutral, factual	More natural, narrative-driven
Visual + Semantic Blend	Moderate	✅✅ Strong integration

Gemma 3 27B vs Qwen2.5-VL-72B: Basic Introduction

Feature	Qwen2.5-VL-72B	Gemma 3 27B
Model Size	73.4 billion parameters	27 billion parameters
Open Source	✅ Yes (by Qwen)	✅ Yes (by Google)
Architecture	Dynamic Resolution & Frame Rate Training	Interleaved Local-Global Attention
Training Data	18T tokens, excelling at document, video, and chart comprehension	14 trillion tokens
Multilingual Support	Strong in natural scenes and multilingual documents	Supports over 140 languages
Multimodal Capabilities	✅ Images + Videos + Text	✅ Images + Text (Outputs Text)
Context Window	Configurable (up to 64K for long videos)	Fixed 128K tokens

Gemma 3 27B vs Qwen2.5-VL-72B: Benchmark

Task	Gemma 3 27B	Qwen2.5-VL-72B	Key Insight
DocVQA (val)	85.6	96.4	Qwen excels in document visual Q&A
ChartQA (val)	76.3	89.5	Qwen delivers stronger factual extraction from charts

These results indicate that Qwen2.5-VL-72B is significantly more capable in tasks involving:

• Document layout understanding
• Visual OCR-based reasoning
• Chart and data interpretation

🔎 If your application involves invoices, academic papers, business charts, or PDF comprehension, Qwen2.5-VL-72B offers a far more reliable and advanced foundation.

Gemma 3 27B vs Qwen2.5-VL-72B: Hardware Requirements

Model	GPU Model	GPUs Required	Total VRAM Needed	Notes
Gemma 3 27B	RTX 4090	4 GPUs	63.5 GB	16GB per card; consumer-grade setup possible
Qwen2.5-VL-72B	NVIDIA H200	4 GPUs	564 GB	Enterprise-grade GPUs; extremely high memory demand

• Gemma 3 27B can run on high-end consumer hardware (e.g., RTX 4090), making it more accessible for research and small-scale deployment.
• Qwen2.5-VL-72B requires enterprise-level GPU infrastructure (e.g., H200 or A100 80GB x8), making it suitable for large-scale, multimodal production environments.

Gemma 3 27B vs Qwen2.5-VL-72B: Best Pick for Visual Q&A Tasks

Why Qwen2.5-VL-72B Wins

1. Richer Multimodal Input
   • Qwen natively supports images, videos, and text, enabling deeper visual understanding.
   • Gemma handles images and text only, with more limited multimodal scope.
2. Superior Visual Reasoning
   • Scene Reasoning: Qwen infers from context and visual cues , while Gemma relies mainly on visible text.
   • Action Interpretation: Qwen understands dynamic visual actions (e.g., player movements), which Gemma lacks.
3. Benchmark Performance
   • Qwen outperforms in both document and chart-based visual Q&A tasks

When to Consider Gemma 3 27B Instead

• If you’re working with limited hardware:
  Gemma runs on consumer-grade GPUs (e.g., 4× RTX 4090), while Qwen requires enterprise-level resources (e.g., 4× H200).
• If your tasks are text-heavy with minimal image complexity, and you need efficient deployment, Gemma may still be sufficient.

How to Access Gemma 3 27B and Qwen2.5-VL-72B via Novita API?

Step 1: Log In and Access the Model Library

Log in to your account and click on the Model Library button.

Step 2: Choose Your Model

Browse through the available options and select the model that suits your needs.

Step 3: Start Your Free Trial

Begin your free trial to explore the capabilities of the selected model.

Step 4: Get Your API Key

To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.

Step 5: Install the API

Install API using the package manager specific to your programming language.

After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for python users.

from openai import OpenAI
  
client = OpenAI(
    base_url="https://api.novita.ai/v3/openai",
    api_key="<YOUR Novita AI API Key>",
)

model = "qwen/qwen2.5-vl-72b-instruct"
stream = True # or False
max_tokens = 2048
system_content = """Be a helpful assistant"""
temperature = 1
top_p = 1
min_p = 0
top_k = 50
presence_penalty = 0
frequency_penalty = 0
repetition_penalty = 1
response_format = { "type": "text" }

chat_completion_res = client.chat.completions.create(
    model=model,
    messages=[
        {
            "role": "system",
            "content": system_content,
        },
        {
            "role": "user",
            "content": "Hi there!",
        }
    ],
    stream=stream,
    max_tokens=max_tokens,
    temperature=temperature,
    top_p=top_p,
    presence_penalty=presence_penalty,
    frequency_penalty=frequency_penalty,
    response_format=response_format,
    extra_body={
      "top_k": top_k,
      "repetition_penalty": repetition_penalty,
      "min_p": min_p
    }
  )

if stream:
    for chunk in chat_completion_res:
        print(chunk.choices[0].delta.content or "", end="")
else:
    print(chat_completion_res.choices[0].message.content)
  
 
  

For AI tasks involving photo understanding, document OCR, or chart comprehension, Qwen2.5-VL-72B is the superior choice. It delivers better performance in multimodal reasoning, scene interpretation, and factual extraction. However, if your deployment is limited by hardware or budget, Gemma 3 27B remains a solid alternative. Both models are available via Novita API, enabling flexible access without local deployment burdens.

Frequently Asked Questions

Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing the affordable and reliable GPU cloud for building and scaling.

Recommended Reading

• DeepSeek R1 vs QwQ-32B: RL-Powered Precision vs Efficiency
• QwQ 32B: A Compact AI Rival to DeepSeek R1
• Llama 3.2 3B vs DeepSeek V3: Comparing Efficiency and Performance.