GLM-4.5V : Best Open-Sourced Vision model
How to use GLM-4.5V for free?
So, here we are. Another week, another model drop. But this one’s different. No cutesy launch video. No poetic language about “unlocking multimodal creativity.” Just a beast of a model GLM-4.5V quietly pushed to GitHub by the folks at Zhipu AI and Tsinghua University. And it’s ridiculous how good this thing is.
https://medium.com/media/0f204ad555de0485463d155054a21020/href
GLM-4.5V is the most capable open-source vision-language model right now. Period.
Model Context Protocol: Advanced AI Agents for Beginners (Generative AI books)
If you’re still obsessing over LLaVA or Qwen-VL, you’re behind. GLM-4.5V smokes them. This isn’t me fanboying. I’m talking state-of-the-art numbers across 42 hardcore benchmarks, math, science, video, coding, charts, GUIs you name it. Not cherry-picked, not overfitted.
What Makes GLM-4.5V Stand Out?
Two things mainly:
1. It actually reasons over visual content.
2. It scales RL like no one else.
And that combo’s rare in the open. Most VLMs can caption a cat. GLM-4.5V can solve a physics diagram, generate React code from a screenshot, read a PDF table, and follow long chains of thought, in both images and video.
Under the Hood
It uses a ViT-based encoder, a clean MLP adapter, and then dumps all that into a massive language decoder. But that’s not the impressive part. The real sauce is how it’s trained.
They didn’t just use CLIP embeddings onto LLMs like most teams do and call it multimodal. They:
- Pretrained on 10B+ curated image-text pairs. Not scraped junk. They cleaned and re-captioned it.
- Added academic diagrams, scientific books, OCR data, GUI screens, and full PDFs.
- Fused it all with long-form chain-of-thought prompts using a tagging scheme like <think>…</think><answer>…</answer>.
That structure allows it to actually show its work, and not just spit out answers.
Then, they did something most groups avoid: full-scale reinforcement learning, not just on one domain, but across multiple at once. That includes STEM, video understanding, GUI interaction, chart reading, document parsing… all under one roof. They called it RLCS (Reinforcement Learning with Curriculum Sampling).
The Benchmarks
Let’s talk numbers. GLM-4.5V crushed or tied the best open-source models of any size in:
- General VQA: MMStar, GeoBench, HallusionBench
- Math & STEM: MathVista, AI2D, MMMU Pro
- OCR & Charts: OCRBench, ChartQAPro, ChartMuseum
- Video: VideoMMME, LVBench, MMVU
- GUI agents: WebVoyager, AndroidWorld
- Coding: Design2Code, Flame-React-Eval
Even the smaller variant, GLM-4.1V-9B-Thinking, outperformed Qwen2.5-VL-72B on 29 of 42 tasks. That’s a 9B model taking down a 72B. You don’t see that often.
Thinking vs Non-Thinking Mode
Another twist: GLM-4.5V runs in two modes.
- Thinking mode: does long CoT reasoning, tags outputs properly.
- Non-thinking mode: fast, short, efficient — basically just answers stuff like a typical VLM.
And yeah, you can flip between them on demand with a special /nothink tag.
Real Use Cases, Not Demos
Forget the benchmark buzz. Here’s what this model can actually do out of the box:
- Read an entire research paper as image pages and explain it.
- Watch a science experiment video and tell you what’s happening, with time-indexed reasoning.
- Understand GUI screenshots, click buttons, and even generate the HTML+JS to recreate it.
- Parse charts, decode OCR in messy scans, and extract table data from PDFs.
This thing is like a vision-native GPT-4, but open.
The model weights are available for free at HuggingFace
zai-org/GLM-4.5V · Hugging Face
The Bad News?
It’s massive. 106B params with a MoE architecture. Running it ain’t free. You’ll need serious hardware or inference tricks (think vLLM, tensor/model parallelism, long context optimization). But the team also open-sourced a smaller 9B version that’s surprisingly solid.
Why This Model Matters
We’ve been swimming in a sea of VLM mediocrity lately. Every other repo is just some mix-and-match remix of BLIP, LLaVA, or Flamingo. GLM-4.5V changes the game. Not with a flashy website, but with sheer engineering brutality and academic honesty.
They built the model they wanted. Then they gave it away.
Not because it’s finished. But because they know the future of vision-language models isn’t just better captions or prettier demos, it’s reasoning, grounding, understanding. And for the first time, an open-source model is actually doing it.
Final Thought
If you’re building tools that need a brain behind the eyes, not just vision but judgment stop patching old LLaVA forks and start playing with GLM-4.5V.
And please, someone wrap this thing in a GUI and make it easier to try. It deserves to be everywhere.
GLM-4.5V : Best Open-Sourced Vision model was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
