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Illustration: Vision models: understanding images instead of generating them

Vision models: understanding images instead of generating them

By Ivo Donker — compiled with AI assistance (Claude & Gemini) · Last updated: August 7, 2026

Within the landscape of artificial intelligence, the processing of visual information is traditionally divided into two fundamental branches: the synthesis of new visual artifacts and the interpretation of existing images. While the hub's catalog has so far focused primarily on the creative and synthetic side, as described in the broad overview of multimodal models, a systematic treatment of the receiving and analytical component is missing. This article fills that gap by focusing on vision-language models (VLMs). These systems form the missing link between the modal catalog and the later selection guides, with an emphasis on understanding, interpreting, and grasping visual data rather than generating it. This article does not cover image generation; see image generation models for the counterpart where pixels are built up from textual cues. The purpose of this contribution is to sharply delineate the technical foundations, operational mechanisms, inherent limitations, and concrete trade-offs of image understanding in practice for the Dutch-speaking builder.

The rise of VLMs marks a significant shift in how language models handle non-textual input. Where earlier generations of architecture specifications remained strictly separated between text processing and computer vision, modern architectures combine both modalities in an integrated latent space. This means that an input image is no longer locally encoded by an isolated neural network whose output serves purely as a numerical classification, but that the visual features are translated directly into structures the language model can interpret and grasp. To understand exactly how this translation takes place, it is necessary to look at the underlying technique of image tokens and visual tokenization, as outlined in the documentation on multimodal tokenization. This clarifies how continuous pixel grids are converted into discrete representations compatible with the self-attention mechanisms of large-scale transformer models.

What is a Vision-Language Model (VLM)?

A vision-language model is a neural network trained or fine-tuned to process and integrate both visual input (such as photos, diagrams, charts, illustrations, and scanned documents) and textual input. Unlike classic computer vision models limited to predefined tasks such as object detection with bounding boxes or binary classification, VLMs have the flexibility of a generative language model. This means they are able to produce free-form text in response to a visual question, draw complex connections between different elements within an image, and link visual evidence to textual reasoning.

The architecture of a standard VLM typically consists of three main components: a visual encoder, a projection layer, and an autoregressive language model. The visual encoder, often based on a Vision Transformer (ViT), receives the input image and divides it into smaller patches. These patches are linearly projected into vectors that capture the spatial and semantic features of the image. The projection layer then ensures these visual vectors are transformed into the same dimensional space as the language model's text tokens. Finally, the central language model processes both the textual instructions (the prompt) and the injected image tokens as if they were one continuous sequence, resulting in coherent textual output.

It is important to note that a VLM's understanding fundamentally differs from human perception. Where humans directly associate visual data with physical experiences and world knowledge, a VLM operates on the basis of statistical correlations between visual tokens and linguistic representations acquired during pre-training. Nevertheless, this leads in practice to an impressive level of semantic understanding, provided the input stays within the model's operational limits.

Tasks and functionalities within image understanding

Image understanding encompasses a broad spectrum of analytical tasks that go beyond simply naming objects. Within the context of modern LLM implementations, we can divide these tasks into five core categories:

This diversity of tasks makes VLMs extremely versatile, but it also places high demands on the underlying infrastructure. After all, processing visual data requires significantly more compute power and memory than pure text processing.

From pixels to context: how a VLM processes images

The transition from a physical image to a usable mental representation within the model follows a precisely structured process. When a user uploads an image, it is first scaled to a resolution that matches the capabilities of the visual encoder. Images that are too large are often cut into smaller sub-tiles to prevent loss of detail, while images that are too small are upscaled or padded.

Each tile or patch is then converted into a fixed number of image tokens. Because a single image can quickly generate hundreds to thousands of tokens, this has a direct impact on memory usage. This brings us to the technical implications for the context window, where visual tokens compete with the textual prompt and the generated output. As a result, a complex image with a lot of visual detail consumes a disproportionately large share of the available context space, which limits the maximum length of the conversation or document.

As the sequences grow, so do the computation times and the memory bandwidth needed for the self-attention calculations. This requires developers to carefully weigh which resolution and number of images per API call are desirable, especially in large-scale implementations.

Limits and limitations of vision models

Despite their advanced capabilities, VLMs are not flawless. It is essential to have a clear view of their limitations in order to avoid disappointments or faulty implementations in production environments. The main pain points are:

These technical limitations show that blind trust in VLMs carries risks. This article does not cover the comparison with classic OCR in detail; see vision models vs traditional OCR for an in-depth analysis of the differences in speed, accuracy, and resource usage between the two methods.

When should you not use a VLM?

A deliberate model selection requires not only knowledge of what a model can do, but above all insight into when a particular type of model is unsuitable for the task at hand. There are specific scenarios in which deploying a VLM is counterproductive:

  1. Classic, structured OCR tasks: When it is purely about quickly and flawlessly extracting typed text from thousands of uniform invoices or receipts, traditional OCR pipelines are often faster, more predictable, and many times cheaper than a generative VLM.
  2. Strict real-time requirements: In applications where response times within tens of milliseconds are required (such as live video streaming or robotics), the heavy token generation and context load of VLMs cause unacceptable delays.
  3. Critical compliance environments without human oversight: Because VLMs remain susceptible to hallucinations and interpretation errors, fully automated decision-making based on unsupervised visual analysis is risky in legal or medical contexts without human-in-the-loop validation.

For organizations that want to process large volumes of visual documents without immediately running into sky-high API costs, batch processing for LLM APIs offers a valuable alternative by bundling non-time-critical tasks and running them at a reduced rate. In addition, privacy-sensitive documents can be processed in local infrastructures following guidelines such as those found in the guide for locally searching documents.

Practical decision framework

The decision whether or not to integrate a visual model into an architecture requires a structured trade-off between accuracy, cost, and complexity. The overview below summarizes the key parameters for the Dutch-speaking builder who wants to make a well-considered decision within the modal catalog.

Dimension When to use a VLM When to choose an alternative
Task complexity Contextual understanding, reasoning about charts, visual Q&A. Simple text recognition, standard OCR, object counting.
Infrastructure & Budget Sufficient budget for higher token consumption and latency. Limited budget, strict cost limits per processed document.
Validation & Control Possibility of human inspection or robust post-processing. Fully autonomous processing in high-risk domains.

For an objective and measurable assessment of the performance of different visual models in various scenarios, it is advisable to consult the standardized guidelines and evaluation frameworks via multimodal evaluation. This ensures that the model choice is not based on assumptions, but on reproducible measurements.