Qwen-VL 2.5: The Future of OCR with Open-Source LLMs

8/10/2025

So, you’ve been hearing a lot about multimodal Large Language Models (LLMs), right? It feels like every week there’s a new model that’s bigger, better, & smarter than the last. It can be a lot to keep up with. But every now & then, a model comes along that makes you sit up & take notice, especially when it comes to a task that’s been a bit of a thorn in the side of automation for years: Optical Character Recognition, or OCR.

Honestly, OCR has been around for a while, but it's always been a bit… finicky. Traditional OCR tools are great at pulling text from a clean, simple document. But the moment you throw a messy, real-world document at them – think invoices with complex tables, handwritten notes, or forms with weird layouts – they can start to struggle.

This is where the new wave of multimodal LLMs comes in, & one model, in particular, has been making some serious waves: Qwen-VL 2.5. If you're in the business of document processing, automation, or just trying to make sense of the mountains of paperwork we all deal with, this is a name you're going to want to remember.

We’re going to take a deep dive into Qwen-VL 2.5 & its performance on OCR tasks. We'll look at the benchmarks, what makes it tick, & how it stacks up against the competition. We'll also get into the nitty-gritty of why using a multimodal LLM for OCR is a game-changer.

The New Kid on the Block: What is Qwen-VL 2.5?

First off, let's get acquainted. Qwen-VL 2.5 is a series of multimodal large language models developed by the Qwen team at Alibaba Cloud. The "VL" stands for "Vision-Language," which means these models are designed to understand both images & text. This is a BIG deal for OCR, because it means the model isn't just "seeing" the text on a page; it's understanding the context, the layout, & the structure of the document as a whole.

The Qwen-VL 2.5 family comes in a range of sizes, from a nimble 0.5B parameters all the way up to a heavyweight 72B model. This is pretty cool because it gives developers options. If you need a model that can run on a smaller device, you can go with one of the smaller versions. But if you need the absolute best performance for heavy-duty OCR tasks, the 72B model is where it's at.

Putting it to the Test: Qwen-VL 2.5's OCR Performance

So, how good is it really? Well, the numbers are pretty darn impressive.

According to some comprehensive benchmarks, the Qwen-VL 2.5 models, particularly the 72B & 32B variants, are showing some remarkable performance in OCR tasks. In tests involving JSON extraction from documents, both models achieved an accuracy of around 75%.

Now, 75% might not sound like a perfect score, but here's the thing: this is on par with GPT-4o, one of the most advanced closed-source models out there. What's even more impressive is that Qwen-VL 2.5 is an open-source model. This means that anyone can use it, build on it, & even fine-tune it for their specific needs. This is a HUGE win for the open-source community & for businesses that want to leverage cutting-edge AI without being locked into a single vendor's ecosystem.

But the good news doesn't stop there. Qwen-VL 2.5 also outperformed a model called mistral-ocr, which, as the name suggests, is specifically trained for OCR tasks. Mistral-ocr came in with an accuracy of 72.2%, so Qwen-VL 2.5 has a noticeable edge. It also blew other open-source models like Gemma-3 (27B) out of the water, with Gemma-3 only scoring 42.9% on the same benchmark.

So, to recap:

Qwen-VL 2.5 (72B & 32B): ~75% accuracy on JSON extraction
GPT-4o: ~75% accuracy
mistral-ocr: 72.2% accuracy
Gemma-3 (27B): 42.9% accuracy

It’s clear that Qwen-VL 2.5 isn't just keeping up; it's leading the pack in the open-source world when it comes to OCR.

What's Under the Hood? The Secret to Qwen-VL 2.5's Success

So, what makes Qwen-VL 2.5 so good at OCR? It's not just one thing, but a combination of factors that give it a serious advantage.

One of the key elements is its enhanced ability to process structured data. Think about all the documents that businesses rely on: invoices, purchase orders, financial reports, etc. These documents are full of tables, forms, & other structured layouts. Traditional OCR might be able to extract the text from these documents, but it often struggles to understand the relationships between the different pieces of information. For example, it might be able to read the words "Total Amount" & "$1,234.56," but it might not understand that the latter is the value associated with the former.

Qwen-VL 2.5, on the other hand, excels at understanding these structured formats. It can recognize a table, identify the rows & columns, & extract the data in a way that preserves the original structure. This is a massive leap forward for document automation, as it means you can go straight from a scanned document to structured data that you can use in your other systems, like your accounting software or your CRM.

Another big advantage is its improved JSON output generation. JSON (JavaScript Object Notation) is a lightweight data-interchange format that's easy for humans to read & write & easy for machines to parse & generate. When you're extracting data from a document, getting it in a structured format like JSON is the holy grail. Qwen-VL 2.5 has been specifically optimized to generate clean, accurate JSON output, which is a huge time-saver for developers.

And here's something you might not have thought about: the ability to provide instructions. With a traditional OCR engine, you feed it an image, & it gives you back the text. That's it. But with a multimodal LLM like Qwen-VL 2.5, you can actually give it instructions. For example, you could say, "Extract the invoice number, the total amount, & the due date from this document." Or, "Read the handwritten notes in the margins of this contract." This level of control & flexibility is simply not possible with older OCR technologies.

This ability to follow instructions is a game-changer for businesses. Imagine you have a customer support team that's constantly dealing with documents from customers. Instead of manually finding the information they need, they could use an AI-powered tool to extract the relevant data in seconds. This is where a platform like Arsturn comes in. You could build a custom AI chatbot, trained on your own data & integrated with a model like Qwen-VL 2.5, that can help your team process documents, answer questions, & resolve customer issues faster than ever before. Arsturn helps businesses create these custom AI chatbots that can provide instant customer support & engage with website visitors 24/7, & with the power of a model like Qwen-VL 2.5, you can take that to the next level by adding advanced document understanding to the mix.

The Power of Pre-training & Fine-Tuning

So, why is Qwen-VL 2.5 so good at OCR right out of the box? A big part of the answer lies in its pre-training data. The team behind Qwen-VL 2.5 was smart enough to include an OCR dataset in the model's pre-training. This means that the model has already seen a vast number of documents & has learned the ins & outs of text recognition before you even use it.

But what if you have a very specific type of document that you need to process? Maybe it's a unique form that your company uses, or a type of historical document with unusual fonts. This is where fine-tuning comes in.

Fine-tuning is the process of taking a pre-trained model like Qwen-VL 2.5 & training it further on your own data. This allows the model to learn the specific nuances of your documents & become even more accurate at extracting the information you need. One data scientist shared their experience of fine-tuning the Qwen-VL 2.5 7B model on a specific dataset. They found that the base model already performed quite well, with an accuracy of 93-99%. But after fine-tuning, they were able to push the performance even higher. This shows that even a model that's already great at OCR can be made even better with a little bit of extra training.

This is another area where the open-source nature of Qwen-VL 2.5 is a huge advantage. Because the model is open, you have the freedom to fine-tune it on your own data, without having to rely on a third-party vendor. This gives you more control over your AI solutions & allows you to build models that are perfectly tailored to your business needs.

The Bounding Box Debate: A Nod to Traditional OCR

One of the interesting discussions that has come up around Qwen-VL 2.5 is the topic of bounding boxes. In traditional OCR, a bounding box is a rectangle that's drawn around a piece of text to show its location on the page. This is incredibly useful for verifying the accuracy of the OCR output, as you can see exactly where the model "read" the text from.

Some people have wondered if multimodal LLMs like Qwen-VL 2.5 can provide this same kind of output. And the answer is yes! It turns out that Qwen-VL 2.5 is perfectly capable of outputting bounding boxes. In fact, the Qwen team has even included examples of how to do this in their "cookbooks" on GitHub. This is a great example of how these new models are not just replacing traditional OCR, but are actually incorporating the best features of the old technology while adding a whole new layer of intelligence & flexibility on top.

This is another feature that can be incredibly powerful when integrated into a business solution. Imagine you're using a tool to extract data from invoices. If the AI makes a mistake, you'll want to be able to quickly see where it went wrong. With bounding boxes, you can see exactly which part of the document the AI was looking at when it extracted the incorrect information. This makes it much easier to correct errors & improve the accuracy of your system over time.

This is another place where a platform like Arsturn could shine. You could build a custom AI chatbot that not only extracts data from documents, but also displays the bounding boxes for the extracted text. This would give your team a powerful tool for verifying the accuracy of the AI's output & making corrections when needed. By building a no-code AI chatbot trained on your own data with Arsturn, you can boost conversions & provide personalized customer experiences, & with features like bounding box visualization, you can do it with a level of accuracy & transparency that was previously impossible.

The Bigger Picture: Why Multimodal LLMs are the Future of OCR

At the end of the day, the rise of models like Qwen-VL 2.5 is about more than just better OCR. It's about a fundamental shift in how we think about document understanding.

For years, we've been trying to teach computers to read. But with multimodal LLMs, we're now teaching them to understand. They're not just seeing pixels on a page; they're understanding the layout, the context, & the meaning behind the words.

This opens up a whole new world of possibilities for businesses. Think about it:

Smarter Automation: You can automate a much wider range of document-based workflows, from invoice processing to contract analysis to customer onboarding.
Improved Customer Service: You can build AI-powered tools that can instantly understand & respond to customer inquiries that involve documents, like insurance claims or loan applications.
Better Data Analysis: You can extract structured data from a vast range of unstructured documents, giving you new insights into your business & your customers.

And this is just the beginning. As these models continue to get better, we're going to see even more amazing applications emerge.

So, What's the Catch?

Of course, no technology is perfect, & there are a few things to keep in mind with Qwen-VL 2.5. The biggest one is that the larger models, like the 72B variant, require a significant amount of computational resources to run. This means that if you want to use the most powerful version of the model, you'll need some pretty beefy hardware.

However, the fact that there are smaller, more efficient versions of the model available is a big plus. And with the rise of cloud computing & specialized AI hardware, the cost of running these models is likely to come down over time.

Another thing to consider is that while 75% accuracy is impressive, it's not perfect. For some applications, you might need a higher level of accuracy. This is where fine-tuning can be a big help, as it can help you get those last few percentage points of performance. And as we discussed, the ability to use bounding boxes can also help you build a human-in-the-loop system where people can quickly verify & correct the AI's output.

The Takeaway

So, there you have it. Qwen-VL 2.5 is a seriously impressive model, & it's pushing the boundaries of what's possible with OCR. Its ability to understand complex documents, generate structured output, & follow instructions makes it a powerful tool for any business that deals with a lot of paperwork.

The fact that it's open-source is a HUGE deal, as it gives businesses the freedom to build their own custom AI solutions without being locked into a single vendor. And with the ability to fine-tune the model & even get bounding box outputs, you have a level of control & flexibility that was simply not possible with older OCR technologies.

We're at a really exciting moment in the world of AI. For years, we've been talking about the promise of intelligent automation, & now, with models like Qwen-VL 2.5, we're finally starting to see that promise become a reality.

Hope this was helpful! Let me know what you think. It's a pretty exciting time to be working with this stuff, & I'm really looking forward to seeing what people build with these new tools.