GLM-4.5V

1. Model Introduction

GLM-4.5V is a state-of-the-art multimodal vision-language model from ZhipuAI, built on the next-generation flagship text foundation model GLM-4.5-Air (106B parameters, 12B active). It achieves SOTA performance among models of the same scale across 42 public vision-language benchmarks. Through efficient hybrid training, GLM-4.5V focuses on real-world usability and enables full-spectrum vision reasoning across diverse visual content types.

Hardware Support: NVIDIA B200/H100/H200, AMD MI300X/MI325X/MI355X

GLM-4.5V introduces several key features:

• Image Reasoning & Grounding Scene understanding, complex multi-image analysis, and spatial recognition with precise visual element localization. Supports bounding box predictions with normalized coordinates (0-1000) for accurate object detection.
• Video Understanding Long video segmentation and event recognition, supporting comprehensive temporal analysis across extended video sequences.
• GUI Agent Tasks Screen reading, icon recognition, and desktop operation assistance for agent-based applications. Enables natural interaction with graphical user interfaces.
• Complex Chart & Long Document Parsing Research report analysis and information extraction from documents with text, charts, tables, and figures. Processes up to 64K tokens of multimodal context.
• Thinking Mode Switch Allows users to balance between quick responses and deep reasoning. Users can enable/disable Chain-of-Thought reasoning based on task requirements for improved accuracy and interpretability.

2. SGLang Installation

SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.

Please refer to the official SGLang installation guide for installation instructions.

3. Model Deployment

This section provides deployment configurations optimized for different hardware platforms and use cases.

3.1 Basic Configuration

The GLM-4.5V offers models in various sizes and architectures, optimized for different hardware platforms. The recommended launch configurations vary by hardware and model size.

Interactive Command Generator: Use the interactive configuration generator below to customize your deployment settings. Select your hardware platform, model size, quantization method, and other options to generate the appropriate launch command.

3.2 Configuration Tips

• TTFT Optimization : Set SGLANG_USE_CUDA_IPC_TRANSPORT=1 to use CUDA IPC for transferring multimodal features, which significantly improves TTFT. This consumes additional memory and may require adjusting --mem-fraction-static and/or --max-running-requests. (additional memory is proportional to image size * number of images in current running requests.)
• TP=8 Configuration: When using Tensor Parallelism (TP) of 8, the vision attention’s 12 heads cannot be evenly divided. You can resolve this by adding --mm-enable-dp-encoder.
• Fast Model Loading: For large models (like the 106B version), you can speed up model loading by using --model-loader-extra-config='{"enable_multithread_load": "true","num_threads": 64}'.
• For more detailed configuration tips, please refer to GLM-4.5V/GLM-4.6V Usage.

4. Model Invocation

4.1 Basic Usage

For basic API usage and request examples, please refer to:

• SGLang Basic Usage Guide
• SGLang OpenAI Vision API Guide

4.2 Advanced Usage

4.2.1 Multi-Modal Inputs

GLM-4.5V supports both image and video inputs. Here’s a basic example with image input:

import time
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://ofasys-multimodal-wlcb-3-toshanghai.oss-accelerate.aliyuncs.com/wpf272043/keepme/image/receipt.png"
                }
            },
            {
                "type": "text",
                "text": "Describe this image in detail."
            }
        ]
    }
]

start = time.time()
response = client.chat.completions.create(
    model="zai-org/GLM-4.5V",
    messages=messages,
    max_tokens=2048
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")

Example Output:

Response costs: 3.37s
Generated text: Auntie Anne's

CINNAMON SUGAR
1 x 17,000                    17,000

SUB TOTAL                    17,000

GRAND TOTAL                  17,000

CASH IDR                     20,000

CHANGE DUE                  3,000

Multi-Image Input Example:

GLM-4.5V can process multiple images in a single request for comparison or analysis:

import time
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://www.civitatis.com/f/china/hong-kong/guia/taxi.jpg"
                }
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://cdn.cheapoguides.com/wp-content/uploads/sites/7/2025/05/GettyImages-509614603-1280x600.jpg"
                }
            },
            {
                "type": "text",
                "text": "Compare these two images and describe the differences in 100 words or less. Focus on the key visual elements, colors, textures, and any notable contrasts between the two scenes. Be specific about what you see in each image."
            }
        ]
    }
]

start = time.time()
response = client.chat.completions.create(
    model="zai-org/GLM-4.5V",
    messages=messages,
    max_tokens=2048
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")

Example Output:

Response costs: 3.86s
Generated text: The first image shows a close - up of a few red taxis on a street with storefronts in the background. The taxis are in a line, and the scene has an urban, busy feel with visible shop displays. The second image is an aerial view of a large taxi parking area with numerous red and green taxis, some with hoods open. The scene is more open, with a parking lot layout, and includes elements like a bridge and grassy areas. Key differences: number of taxis (few vs many), perspective (close - up vs aerial), color variety (mostly red vs red and green), and setting (street with shops vs parking lot).

Video Input Example:

GLM-4.5V supports video understanding by processing video URLs:

import time
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video_url",
                "video_url": {
                    "url": "https://videos.pexels.com/video-files/4114797/4114797-uhd_3840_2160_25fps.mp4"
                }
            },
            {
                "type": "text",
                "text": "Describe what happens in this video."
            }
        ]
    }
]

start = time.time()
response = client.chat.completions.create(
    model="zai-org/GLM-4.5V",
    messages=messages,
    max_tokens=2048
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")

Note:

• For video processing, ensure you have sufficient context length configured (up to 64K tokens)
• Video processing may require more memory; adjust --mem-fraction-static accordingly
• You can also provide local file paths using file:// protocol

Example Output:

Response costs: 3.89s
Generated text: A person wearing blue gloves is using a microscope. They are adjusting the focus knob with one hand while holding a pipette with the other, suggesting they are preparing or examining a sample on the slide beneath the objective lens. The microscope's 40x objective lens is positioned over the slide, indicating a high-magnification observation. The person carefully manipulates the slide and the microscope controls, likely to achieve a clear view of the specimen.

4.2.2 Thinking Mode

GLM-4.5V supports thinking mode for enhanced reasoning. Enable thinking mode during deployment:

python -m sglang.launch_server \
  --model-path zai-org/GLM-4.5V \
  --reasoning-parser glm45 \
  --tp 4 \
  --host 0.0.0.0 \
  --port 30000

Streaming with Thinking Process:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

# Enable streaming to see the thinking process in real-time
response = client.chat.completions.create(
    model="zai-org/GLM-4.5V",
    messages=[
        {"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
    ],
    temperature=0.7,
    max_tokens=2048,
    stream=True
)

# Process the stream
has_thinking = False
has_answer = False
thinking_started = False

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Print answer content
        if delta.content:
            # Close thinking section and add content header
            if has_thinking and not has_answer:
                print("\n=============== Content =================", flush=True)
                has_answer = True
            print(delta.content, end="", flush=True)

print()

Note: The reasoning parser captures the model’s step-by-step thinking process, allowing you to see how the model arrives at its conclusions.

Disable Thinking Mode:

To disable thinking mode for a specific request:

response = client.chat.completions.create(
    model="zai-org/GLM-4.5V",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}}
)

4.2.3 Tool Calling

GLM-4.5V supports tool calling capabilities. Enable the tool call parser:

python -m sglang.launch_server \
  --model-path zai-org/GLM-4.5V \
  --reasoning-parser glm45 \
  --tool-call-parser glm45 \
  --tp 4 \
  --host 0.0.0.0 \
  --port 30000

Python Example (with Thinking Process):

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

# Define available tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city name"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Make request with streaming to see thinking process
response = client.chat.completions.create(
    model="zai-org/GLM-4.5V",
    messages=[
        {"role": "user", "content": "What's the weather in Beijing?"}
    ],
    tools=tools,
    temperature=0.7,
    stream=True
)

# Process streaming response
thinking_started = False
has_thinking = False
tool_calls_accumulator = {}

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Accumulate tool calls
        if hasattr(delta, 'tool_calls') and delta.tool_calls:
            # Close thinking section if needed
            if has_thinking and thinking_started:
                print("\n=============== Content =================\n", flush=True)
                thinking_started = False

            for tool_call in delta.tool_calls:
                index = tool_call.index
                if index not in tool_calls_accumulator:
                    tool_calls_accumulator[index] = {
                        'name': None,
                        'arguments': ''
                    }

                if tool_call.function:
                    if tool_call.function.name:
                        tool_calls_accumulator[index]['name'] = tool_call.function.name
                    if tool_call.function.arguments:
                        tool_calls_accumulator[index]['arguments'] += tool_call.function.arguments

        # Print content
        if delta.content:
            print(delta.content, end="", flush=True)

# Print accumulated tool calls
for index, tool_call in sorted(tool_calls_accumulator.items()):
    print(f"🔧 Tool Call: {tool_call['name']}")
    print(f"   Arguments: {tool_call['arguments']}")

print()

Output Example:

=============== Thinking =================
The user is asking about the weather in Beijing. I need to use the get_weather function to retrieve this information.
I should call the function with location="Beijing".
=============== Content =================

🔧 Tool Call: get_weather
   Arguments: {"location": "Beijing", "unit": "celsius"}

Note:

• The reasoning parser shows how the model decides to use a tool
• Tool calls are clearly marked with the function name and arguments
• You can then execute the function and send the result back to continue the conversation

Handling Tool Call Results:

# After getting the tool call, execute the function
def get_weather(location, unit="celsius"):
    # Your actual weather API call here
    return f"The weather in {location} is 22°{unit[0].upper()} and sunny."

# Send tool result back to the model
messages = [
    {"role": "user", "content": "What's the weather in Beijing?"},
    {
        "role": "assistant",
        "content": None,
        "tool_calls": [{
            "id": "call_123",
            "type": "function",
            "function": {
                "name": "get_weather",
                "arguments": '{"location": "Beijing", "unit": "celsius"}'
            }
        }]
    },
    {
        "role": "tool",
        "tool_call_id": "call_123",
        "content": get_weather("Beijing", "celsius")
    }
]

final_response = client.chat.completions.create(
    model="zai-org/GLM-4.5V",
    messages=messages,
    temperature=0.7
)

print(final_response.choices[0].message.content)
# Output: "The weather in Beijing is currently 22°C and sunny."

5. Benchmark

5.1 Accuracy Benchmark

Document model accuracy on standard benchmarks:

5.1.1 MMMU Benchmark

• Benchmark Command

python3 benchmark/mmmu/bench_sglang.py --response-answer-regex "<\|begin_of_box\|>(.*)<\|end_of_box\|>" --port 30000 --concurrency 64

• Test Result

Benchmark time: 616.6163094160147
answers saved to: ./answer_sglang.json
Evaluating...
answers saved to: ./answer_sglang.json
{'Accounting': {'acc': 0.867, 'num': 30},
 'Agriculture': {'acc': 0.567, 'num': 30},
 'Architecture_and_Engineering': {'acc': 0.667, 'num': 30},
 'Art': {'acc': 0.667, 'num': 30},
 'Art_Theory': {'acc': 0.9, 'num': 30},
 'Basic_Medical_Science': {'acc': 0.8, 'num': 30},
 'Biology': {'acc': 0.6, 'num': 30},
 'Chemistry': {'acc': 0.533, 'num': 30},
 'Clinical_Medicine': {'acc': 0.667, 'num': 30},
 'Computer_Science': {'acc': 0.8, 'num': 30},
 'Design': {'acc': 0.867, 'num': 30},
 'Diagnostics_and_Laboratory_Medicine': {'acc': 0.667, 'num': 30},
 'Economics': {'acc': 0.833, 'num': 30},
 'Electronics': {'acc': 0.433, 'num': 30},
 'Energy_and_Power': {'acc': 0.733, 'num': 30},
 'Finance': {'acc': 0.767, 'num': 30},
 'Geography': {'acc': 0.667, 'num': 30},
 'History': {'acc': 0.8, 'num': 30},
 'Literature': {'acc': 0.9, 'num': 30},
 'Manage': {'acc': 0.733, 'num': 30},
 'Marketing': {'acc': 0.9, 'num': 30},
 'Materials': {'acc': 0.567, 'num': 30},
 'Math': {'acc': 0.8, 'num': 30},
 'Mechanical_Engineering': {'acc': 0.767, 'num': 30},
 'Music': {'acc': 0.3, 'num': 30},
 'Overall': {'acc': 0.732, 'num': 900},
 'Overall-Art and Design': {'acc': 0.683, 'num': 120},
 'Overall-Business': {'acc': 0.82, 'num': 150},
 'Overall-Health and Medicine': {'acc': 0.787, 'num': 150},
 'Overall-Humanities and Social Science': {'acc': 0.783, 'num': 120},
 'Overall-Science': {'acc': 0.707, 'num': 150},
 'Overall-Tech and Engineering': {'acc': 0.648, 'num': 210},
 'Pharmacy': {'acc': 0.9, 'num': 30},
 'Physics': {'acc': 0.933, 'num': 30},
 'Psychology': {'acc': 0.767, 'num': 30},
 'Public_Health': {'acc': 0.9, 'num': 30},
 'Sociology': {'acc': 0.667, 'num': 30}}
eval out saved to ./val_sglang.json
Overall accuracy: 0.732