GLM-4.6V-Flash

This model is part of the GLM-V family of models, introduced in the paper GLM-4.1V-Thinking and GLM-4.5V: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning.

• GLM-4.6V Blog: https://z.ai/blog/glm-4.6v
• Paper: https://huggingface.co/papers/2507.01006
• GitHub Repository: https://github.com/zai-org/GLM-V
• Online Demo: https://chat.z.ai/
• API Access: Z.ai Open Platform
• Desktop Assistant App: https://huggingface.co/spaces/zai-org/GLM-4.5V-Demo-App

Introduction

GLM-4.6V series model includes two versions: GLM-4.6V (106B), a foundation model designed for cloud and high-performance cluster scenarios, and GLM-4.6V-Flash (9B), a lightweight model optimized for local deployment and low-latency applications. GLM-4.6V scales its context window to 128k tokens in training, and achieves SoTA performance in visual understanding among models of similar parameter scales. Crucially, we integrate native Function Calling capabilities for the first time. This effectively bridges the gap between "visual perception" and "executable action" providing a unified technical foundation for multimodal agents in real-world business scenarios.

Beyond achieves SoTA performance across major multimodal benchmarks at comparable model scales. GLM-4.6V introduces several key features:

• Native Multimodal Function Calling Enables native vision-driven tool use. Images, screenshots, and document pages can be passed directly as tool inputs without text conversion, while visual outputs (charts, search images, rendered pages) are interpreted and integrated into the reasoning chain. This closes the loop from perception to understanding to execution.

• Interleaved Image-Text Content Generation Supports high-quality mixed media creation from complex multimodal inputs. GLM-4.6V takes a multimodal context—spanning documents, user inputs, and tool-retrieved images—and synthesizes coherent, interleaved image-text content tailored to the task. During generation it can actively call search and retrieval tools to gather and curate additional text and visuals, producing rich, visually grounded content.

• Multimodal Document Understanding GLM-4.6V can process up to 128K tokens of multi-document or long-document input, directly interpreting richly formatted pages as images. It understands text, layout, charts, tables, and figures jointly, enabling accurate comprehension of complex, image-heavy documents without requiring prior conversion to plain text.

• Frontend Replication & Visual Editing Reconstructs pixel-accurate HTML/CSS from UI screenshots and supports natural-language-driven edits. It detects layout, components, and styles visually, generates clean code, and applies iterative visual modifications through simple user instructions.

This Hugging Face repository hosts the GLM-4.6V-Flash model, part of the GLM-V series.

Usage

Environment Installation

For SGLang:

pip install sglang>=0.5.6.post1
pip install nvidia-cudnn-cu12==9.16.0.29
sudo apt update
sudo apt install ffmpeg

For vLLM:

pip install vllm>=0.12.0
pip install transformers>=5.0.0rc0

Quick Start with Transformers

from transformers import AutoProcessor, Glm4vForConditionalGeneration
import torch

MODEL_PATH = "zai-org/GLM-4.6V-Flash"
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": "https://upload.wikimedia.org/wikipedia/commons/f/fa/Grayscale_8bits_palette_sample_image.png"
            },
            {
                "type": "text",
                "text": "describe this image"
            }
        ],
    }
]
processor = AutoProcessor.from_pretrained(MODEL_PATH)
model = Glm4vForConditionalGeneration.from_pretrained(
    pretrained_model_name_or_path=MODEL_PATH,
    torch_dtype="auto",
    device_map="auto",
)
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)
inputs.pop("token_type_ids", None)
generated_ids = model.generate(**inputs, max_new_tokens=8192)
output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
print(output_text)

Evaluation Settings

We primarily use vLLM as the backend for model inference. For faster and more reliable performance on video tasks, we employ SGLang. To reproduce our leaderboard results, we recommend the following decoding parameters:

• top_p: 0.6
• top_k: 2
• temperature: 0.8
• repetition_penalty: 1.1
• max_generate_tokens: 16K

For more usage details, please refer to Our Github.

Fixed and Remaining Issues

Since the open-sourcing of GLM-4.1V, we have received extensive feedback from the community and are well aware that the model still has many shortcomings. In subsequent iterations, we attempted to address several common issues — such as repetitive thinking outputs and formatting errors — which have been mitigated to some extent in this new version.

However, the model still has several limitations and issues that we will fix as soon as possible:

1. Pure text QA capabilities still have significant room for improvement. In this development cycle, our primary focus was on visual multimodal scenarios, and we will enhance pure text abilities in upcoming updates.
2. The model may still overthink or even repeat itself in certain cases, especially when dealing with complex prompts.
3. In some situations, the model may restate the answer again at the end.
4. There remain certain perception limitations, such as counting accuracy an