GLM 4.5V vs Qwen 2.5-VL: Which Open VLM Should You Use for Your AI Application?

GLM 4.5V and Qwen 2.5-VL are two cutting-edge open-source vision-language models (VLMs) that have recently emerged from China’s AI community. Both models aim to push the state of the art in multimodal AI, combining natural language understanding with visual content analysis. In this blog post, we’ll compare GLM 4.5V and Qwen 2.5-VL across several dimensions important to developers

GLM 4.5V and Qwen 2.5-VL: Key Architectural Differences

Feature	GLM 4.5V	Qwen 2.5-VL
Architecture Type	Mixture-of-Experts (MoE), 355B total params, ~32B active per token (Air: 106B total / 12B active)	Dense Transformer, all 72B parameters active for each token
Efficiency vs Capacity	High capacity with lower inference cost due to partial expert activation	Stable but high computational cost, all parameters used per input
Vision Encoder	Vision Transformer (ViT)-based, standard implementation	ViT with Window Attention, RMSNorm, and SwiGLU for more efficient high-resolution processing
Context Length	Up to 128K tokens (131K in some configs)	Up to 32K tokens

GLM 4.5V and Qwen 2.5-VL: Training Data

1. Data Scale

Category	GLM 4.5V	Qwen 2.5-VL
Text Tokens	~23 trillion tokens total – 15T general – 8T reasoning/coding/agent tasks	Estimated ~18T+ tokens for 72B variant (based on scaling from earlier Qwen series)

2.Data Types

Category	GLM 4.5V	Qwen 2.5-VL
Text	Multilingual text, code, web text, reasoning prompts, agent-task data	General multilingual text, instructions, possibly preference-aligned prompts
Visual Data	Cleaned + re-captioned image-text pairs Academic diagrams, charts, math images GUI screenshots, PDFs, handwritten notes, multilingual OCR	Broad vision data Includes scanned forms, invoices, presentations, bounding box labels, OCR text
Video Data	Long-form videos with reasoning supervision	Videos with dynamic resolution and frame sampling

3.Extra Capabilities & Training Techniques

Category	GLM 4.5V	Qwen 2.5-VL
Reasoning Support	Trained with <think>...</think> chain-of-thought prompts interwoven with visual tasks	Reasoning is internal; no explicit chain-of-thought exposure
Fine-Tuning Approach	Reinforcement Learning with Curriculum Sampling (RLCS) across multiple domains: STEM, GUI, videos, documents	RLHF/RLAIF-like fine-tuning (not fully disclosed), applied to at least the 32B model, likely inherited in 72B
Multimodal Capabilities	Trained for agent tasks: reasoning over images, taking action (e.g., GUI interaction, tool usage)	Strong on structured outputs: OCR in JSON, layout parsing (QwenVL HTML), object detection with coordinates

In summary, GLM 4.5V’s training emphasized quality and reasoning (curated data + explicit reasoning + multi-domain RL), whereas Qwen 2.5-VL’s training emphasized breadth and vision (broad data coverage + dynamic visual training + some RL alignment).

GLM 4.5v and Qwen 2.5-VL: Inference Latency Comparison

GLM 4.5V uses a Mixture-of-Experts (MoE) architecture, meaning only a small portion (~12B parameters) is active per token during inference, despite the model’s total size being over 100B.

This design allows it to run more efficiently, delivering speeds similar to a 12B–20B dense model, rather than behaving like a 72B+ dense model in terms of latency and throughput.

GLM 4.5V handles long contexts (up to 128K tokens) with lower latency growth, making it particularly suitable for tasks involving lengthy documents or multi-turn conversations.

GLM supports a special /nothink mode, which disables step-by-step reasoning when it’s not needed, enabling faster and more concise outputs.

Overall, GLM 4.5V offers excellent long-context inference efficiency and scalability, but requires strong hardware and smart deployment to achieve its full potential.

GLM 4.5V and Qwen 2.5-VL: Benchmark Comparison

GLM-4.5V currently leads in overall benchmark performance, particularly in complex and long-context multimodal tasks,

but Qwen2.5-VL remains highly competitive and was previously the benchmark to beat.

Both models outperform most other open-source LLMs and are strong contenders even against closed-source giants in the vision-language space.

Strengths and Weaknesses of GLM 4.5V and Qwen 2.5-VL

GLM 4.5V vs Qwen 2.5-VL: Which is Better for Text Summarization, Chatbot,Image-Based NLP?

Text Summarization: GLM-4.5V wins

For summarizing long documents, reports, or multimodal content, GLM-4.5V has a clear edge. Its 128K context window allows it to handle entire books or large conversation logs without truncation. It can summarize while also analyzing or reasoning through the content, thanks to its built-in chain-of-thought mode.

Qwen 2.5-VL is also excellent at summarization, particularly for shorter articles or standard-length documents. It produces clean, concise, well-formatted summaries, and is faster for moderate-length tasks. However, for heavy-duty summarization, especially involving text + image, GLM is more capable.

Chatbots: Depends on needs

For chatbots requiring deep reasoning, long memory, and step-by-step task completion, GLM-4.5V is more powerful. It supports tool use and long conversations without forgetting context. Its structured reasoning (with <think> mode) enables better handling of complex queries.

For visual chatbots, especially those involving screenshots, images, or layout parsing, Qwen 2.5-VL excels. It understands images well, provides structured answers (e.g., in JSON), and supports multi-turn visual dialogue. It’s also slightly more aligned “out of the box” for smooth, polite interaction.

Image-Based NLP Tasks: Qwen2.5-VL leads

For tasks that involve extracting structured data from images, such as OCR, form understanding, or layout recognition, Qwen 2.5-VL is the stronger model.

• It supports bounding box detection, outputs structured layouts in HTML or JSON, and can parse complex visual documents.
• Its multi-language OCR and ability to reason over image content make it highly practical for business-facing visual NLP.

GLM-4.5V can also handle these tasks, but typically describes visual content in freeform text rather than structured formats, which may require more post-processing.

Novita AI: More Cost-Effectvely and Stable GLM 4.5V API Provider

Novita AI’s GLM-4.5V API offers 65.5K context, with input priced at $0.60/1K tokens, output at $1.80/1K tokens, and function calling and structured outputs supported.

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/openai",
    api_key="session_rDfpD7GWNXFvnoIbmYNFkVlStqevDItFJac__3tAuw3ZiENHe3wm498Kv9rZEc5JhZgEJ7c9To5Y3EmZZewMbw==",
)

model = "zai-org/glm-4.5v"
stream = True # or False
max_tokens = 32768
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)
  
  

Build a Simple Image Recognition Tool using MCP and GLM.

If you want to leverage the capabilities of GLM—such as building a simple image recognition tool to demonstrate its integration of visual recognition and reasoning—you can use the MCP functionality supported by Novita AI. Below is the sample code:

import os
import sys
from mcp.server.fastmcp import FastMCP
import requests
import uvicorn
from starlette.applications import Starlette
from starlette.routing import Mount

base_url = "https://api.novita.ai/v3"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.environ['NOVITA_API_KEY']}"
}

mcp = FastMCP("Novita_API")

@mcp.tool()
def list_models() -> str:
    """
    List all available models from the Novita API.
    """
    url = base_url + "/openai/models"
    response = requests.request("GET", url, headers=headers)
    data = response.json()["data"]

    text = ""
    for i, model in enumerate(data, start=1):
        text += f"Model id: {model['id']}\n"
        text += f"Model description: {model['description']}\n"
        text += f"Model type: {model['model_type']}\n\n"

    return text

@mcp.tool()
def get_model(model_id: str, message) -> str:
    """
    Provide a model ID and a message to get a response from the Novita API.
    """
    url = base_url + "/openai/chat/completions"
    payload = {
        "model": model_id,
        "messages": [
            {
                "content": message,
                "role": "user",
            }
        ],
        "max_tokens": 200,
        "response_format": {
            "type": "text",
        },
    }
    response = requests.request("POST", url, json=payload, headers=headers)
    content = response.json()["choices"][0]["message"]["content"]
    return content

@mcp.tool()
def vision_chat(model_id: str, image_url: str, question: str) -> str:
    """
    Use GLM-4.1V-9B-Thinking to answer a question about an image.
    """
    url = base_url + "/openai/chat/completions"
    payload = {
        "model": model_id,
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": image_url,
                        }
                    },
                    {
                        "type": "text",
                        "text": question,
                    }
                ]
            }
        ],
        "max_tokens": 500
    }
    response = requests.post(url, json=payload, headers=headers)
    return response.json()["choices"][0]["message"]["content"]

if __name__ == "__main__":
   # Run using stdio transport
   mcp.run(transport="stdio")

If you want to get the details, you can check out this article: How to Build Your First MCP Server with Novita AI!

Having compared these models, it’s clear both GLM 4.5V and Qwen 2.5-VL are extremely powerful. The “better” model truly depends on the specific use-case and constraints. We’ll conclude with a brief FAQ addressing some remaining practical questions:

Novita AI is the All-in-one cloud platform that empowers your AI ambitions. Integrated APIs, serverless, GPU Instance — the cost-effective tools you need. Eliminate infrastructure, start free, and make your AI vision a reality.

Recommend Reading

• Tutorial: How to Access Qwen2.5-VL-72B Locally, via API, on Cloud GPU
• GPT OSS 120B vs Qwen3 235B Thinking 2507: Chat or Code？
• Qwen3 Embedding 8B: Powerful Search, Flexible Customization, and Multilingual