GLM 4.1V 9B Thinking vs Qwen2.5 VL 72B: Which Models Fits What Scenario？

Wondering whether GLM 4.1V 9B Thinking or Qwen2.5 VL 72B is right for you? We’ve got the quick answers! From smart document reading to interactive Q&A and AI image support, see which model shines. Want to know the logic behind our picks? Just slide down!

GLM 4.1V 9B Thinking vs Qwen2.5 VL 72B: Task

Input:

Output:

Evaluation of GLM 4.1V 9B Thinking and Qwen2.5 VL 72B:

GLM 4.1v 9B is better at answering the first two questions in a user-friendly way, and it frames the context as a tutorial where the user is learning or following along. However, neither answer directly provides actionable next steps.

Qwen 2.5 VL 72B

• What is this page?
  It explains the code and context, but it does not explicitly describe the user interface or what the user is seeing on the page (like a tutorial, code editor, or a web page screenshot).
• What is the code for?
  Provides a detailed technical explanation of the code’s purpose and what it achieves.

GLM 4.1v 9B

• What is this page?
  Directly explains that the page is a code example, likely part of a tutorial, and describes what is displayed (a code editor, files, etc.).
• What is the code for?
  Clearly summarizes the code’s purpose: to set up an Express route and render a dynamic page.

GLM 4.1V 9B Thinking vs Qwen2.5 VL 72B: Basic Introduction

Feature	GLM 4.1v 9B	Qwen 2.5 VL 72B
Model Size	9B	73.4B
Open Source	Yes	Yes
Training Method	Based on GLM 4 9B 0414	May Based on Qwen 2 VL
Context Window	64K and 4K image resolution	64K (videos of over 1 hour)
Multimodal Capability	Visual (images and videos) & textual inputs, but not simultaneous image & video	Visual (images and videos) & textual inputs
Language Support	Supports Chinese and English	In Multiple Languages
Chain-of-Thought reasoning	Provides “chain-of-thought” (CoT) reasoning	No
Document processing	Excel at STEM & long docs	Excellent OCR & document extraction

GLM 4.1V 9B Thinking is trained on GLM 4 9B 0414 and is designed to push the boundaries of reasoning in vision-language models. By introducing a “thinking paradigm” and leveraging reinforcement learning, the model significantly enhances its capabilities. As the first vision-language model to implement chain-of-thought (CoT) reasoning, GLM 4.1V 9B Thinking sets a new benchmark in multimodal reasoning.

GLM 4.1V 9B Thinking vs Qwen2.5 VL 72B: Benchmark

Benchmark	GLM 4.1V‑9B	Qwen 2.5 VL 72B	Winner
MMMU (image)	68.0	70.2	Qwen 2.5 VL
MMMU‑Pro	57.1	51.1	GLM
VideoMMMU	61.0	60.2	GLM
mvBench (video)	70.4	64.6	GLM
AITZ_EM (agent)	83.2	35.3*	GLM
Agent (OSWorld)	14.9	8.8	GLM
Agent (AndroidWorld)	41.7	35.0	GLM
Agent (WebVoyageSom)	69.0	40.4	GLM
Agent (Webquest‑SingleQA)	72.1	60.5	GLM
Agent (Webquest‑MultiQA)	54.7	52.1	GLM
Coding (Design2Code)	64.7	41.9	GLM
Coding (Flame‑VLM‑Code)	72.5	46.3	GLM
OCRBench	84.2	85.1	Qwen 2.5 VL
VideoMME (w/o text)	68.2	73.3	Qwen 2.5 VL
VideoMME (w/ text)	73.6	79.1	Qwen 2.5 VL
MMVU	59.4	62.9	Qwen 2.5 VL

Choose GLM 4.1V‑Thinking if your priority is multimodal reasoning, agent capabilities, STEM problem solving, or coding.

Choose Qwen 2.5 VL 72B if you’re focusing on document/image/video understanding—especially OCR, structured extraction, and visual perception.

GLM 4.1V 9B Thinking vs Qwen2.5 VL 72B: Using Cost

If you want to access locally:

Feature	GLM 4.1V 9B Thinking	Qwen 2.5 VL 72B
GPU Model	RTX 4090	H100
GPUs Used	1 GPU	8 GPUs
Total VRAM	22 GB	~640 GB
Total Price	~$2,935 from Amazon	~ $25,000 per GPU direct from NVIDIA
Cloud GPU Price (Novita AI)	$0.69/hr	$20.48/hr

If you want to use API like Novita AI:

Model	Context Window	Input Price (/1M tokens)	Output Price (/1M tokens)
GLM 4.1V 9B-Thinking	65,536	$0.035	$0.138
Qwen2.5 VL 72B Instruct	32,768	$0.80	$0.80

GLM 4.1V 9B-Thinking offers much better accessibility and cost-efficiency for both local and API use.

Qwen 2.5 VL 72B is for users with very high-end requirements and resources.

Which Visual Language Model to Use?

1. For Document Understanding

Qwen2.5 VL 72B is more suitable.
Reason: Qwen2.5 VL 72B excels at OCR, document extraction, and processing complex, structured documents (including natural scene text recognition). It is designed for high-accuracy document understanding tasks, especially in multilingual settings.

2. For Consumer-Facing (To-C) Multimodal Q&A

GLM 4.1V 9B Thinking is more suitable.
Reason: GLM 4.1V 9B Thinking provides user-friendly, tutorial-style responses, strong chain-of-thought reasoning, and is efficient for interactive, agent-style Q&A. This makes it a better fit for scalable, responsive consumer applications.

3. For AI-Generated Image Assistance (AI Drawing/Gen-Image Support)

Qwen2.5 VL 72B is more suitable.
Reason: Qwen2.5 VL 72B has advanced multimodal capabilities, particularly in visual perception, image understanding, and structured extraction, making it better for scenarios where AI assists users in generating or understanding images.

How to Access GLM 4.1V 9B Thinking 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="session_kgNdXtDPt2zYc95i-nDWPaW4Zl_e7nf4VDpukuIVBKpko1-LE8xCasG4YK7c-3c1xnPzGYRuocFk_DhkPUUQyQ==",
)

model = "thudm/glm-4.1v-9b-thinking"
stream = True # or False
max_tokens = 4000
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)
  
  

GLM 4.1V 9B Thinking is your best pick for friendly, interactive Q&A and consumer applications.
Qwen2.5 VL 72B stands out for deep document understanding and powerful AI image support.
Pick the model that matches your needs—and if you’re curious why, scroll down for the details!

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.

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